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  • 1.
    Abedin, Md Reaz Ashraful
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Bensch, Suna
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Self-supervised language grounding by active sensing combined with Internet acquired images and text2017Ingår i: Proceedings of the Fourth International Workshop on Recognition and Action for Scene Understanding (REACTS2017) / [ed] Jorge Dias George Azzopardi, Rebeca Marf, Málaga: REACTS , 2017, s. 71-83Konferensbidrag (Refereegranskat)
    Abstract [en]

    For natural and efficient verbal communication between a robot and humans, the robot should be able to learn names and appearances of new objects it encounters. In this paper we present a solution combining active sensing of images with text based and image based search on the Internet. The approach allows the robot to learn both object name and how to recognise similar objects in the future, all self-supervised without human assistance. One part of the solution is a novel iterative method to determine the object name using image classi- fication, acquisition of images from additional viewpoints, and Internet search. In this paper, the algorithmic part of the proposed solution is presented together with evaluations using manually acquired camera images, while Internet data was acquired through direct and reverse image search with Google, Bing, and Yandex. Classification with multi-classSVM and with five different features settings were evaluated. With five object classes, the best performing classifier used a combination of Pyramid of Histogram of Visual Words (PHOW) and Pyramid of Histogram of Oriented Gradient (PHOG) features, and reached a precision of 80% and a recall of 78%.

  • 2.
    Ali, W
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Georgsson, Fredrik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Visual tree detection for autonomous navigation in forest environment2008Ingår i: IEEE Intelligent Vehicles SymposiumConference Location: Eindhoven, NETHERLANDS, 2008, , s. 1144-1149s. 1144-1149Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper describes a classification based tree detection method for autonomous navigation of forest vehicles in forest environment. Fusion of color, and texture cues has been used to segment the image into tree trunk and background objects. The segmentation of images into tree trunk and background objects is a challenging task due to high variations of illumination, effect of different color shades, non-homogeneous bark texture, shadows and foreshortening. To accomplish this, the approach has been to find the best combinations of color, and texture descriptors, and classification techniques. An additional task has been to estimate the distance between forest vehicle and the base of segmented trees using monocular vision. A simple heuristic distance measurement method is proposed that is based on pixel height and a reference width. The performance of various color and texture operators, and accuracy of classifiers has been evaluated using cross validation techniques.

  • 3. Arafat, Yeasin
    et al.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Rashid, Jayedur
    Parameterized sensor model and an approach for measuring goodness of robotic maps2010Ingår i: Proceedings of the 15th IASTED International Conference on Robotics and Applications (RA 2010), ACTA Press, 2010Konferensbidrag (Refereegranskat)
    Abstract [en]

    Map building is a classical problem in mobile and au tonomous robotics, and sensor models is a way to interpret raw sensory information, especially for building maps. In this paper we propose a parameterized sensor model, and optimize map goodness with respect to these parameters. A new approach, measuring the goodness of maps without a handcrafted map of the actual environment is introduced and evaluated. Three different techniques; statistical anal ysis, derivative of images, and comparison of binary maps have been used as estimates of map goodness. The results show that the proposed sensor model generates better maps than a standard sensor model. However, the proposed ap proach of measuring goodness of maps does not improve the results as much as expected.

  • 4.
    Athanassiadis, Dimitris
    et al.
    Dept. of Forest Resource Management, Swedish University of Agricultural Sciences.
    Bergström, Dan
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Lindroos, Ola
    Dept. of Forest Resource Management, Swedish University of Agricultural Sciences.
    Nordfjell, Tomas
    Dept. of Forest Resource Management, Swedish University of Agricultural Sciences.
    Ringdahl, Ola
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Path tracking for autonomous forwarders in forest terrain2010Ingår i: Precision Forestry Symposium: developments in Precision Forestry since 2006 / [ed] Ackerman P A, Ham H, & Lu C, 2010, s. 42-43Konferensbidrag (Refereegranskat)
  • 5.
    Barth, Ruud
    et al.
    Greenhouse Horticulture, Wageningen University & Research Center.
    Baur, Jörg
    Institute of Applied Mechanics, Technische Universität München.
    Buschmann, Thomas
    Institute of Applied Mechanics, Technische Universität München.
    Edan, Yael
    Department of Industrial Engineering and Management, Ben-Gurion University of the Negev.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Nguyen, Thanh
    KU Leuven, Department of Biosystems.
    Ringdahl, Ola
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Saeys, Wouter
    KU Leuven, Department of Biosystems.
    Salinas, Carlota
    Centre for Automation and Robotics UPM-CSIC.
    Vitzrabin, Efi
    Department of Industrial Engineering and Management, Ben-Gurion University of the Negev.
    Using ROS for agricultural robotics: design considerations and experiences2014Ingår i: RHEA-2014 / [ed] Pablo Gonzalez-de-Santos and Angela Ribeiro, 2014, s. 509-518Konferensbidrag (Refereegranskat)
    Abstract [en]

    We report on experiences of using the ROS middleware for developmentof agricultural robots. We describe software related design considerations for all maincomponents in developed subsystems as well as drawbacks and advantages with thechosen approaches. This work was partly funded by the European Commission(CROPS GA no 246252).

  • 6.
    Bensch, Suna
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Drewes, Frank
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Grammatical Inference of Graph Transformation Rules2015Ingår i: Proceedings of the 7th Workshop on Non-Classical Modelsof Automata and Applications (NCMA 2015), Austrian Computer Society , 2015, s. 73-90Konferensbidrag (Refereegranskat)
  • 7.
    Bensch, Suna
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    On ambiguity in learning from demonstration2010Ingår i: Intelligent Autonomous Systems 11 (IAS-11) / [ed] H. Christensen, F. Groen, and E. Petriu, Amsterdam: IOS Press , 2010, s. 47-56Konferensbidrag (Refereegranskat)
    Abstract [en]

    An overlooked problem in Learning From Demonstration is the ambiguity that arises, for instance, when the robot is equipped with more sensors than necessary for a certain task. Simply trying to repeat all aspects of a demonstration is seldom what the human teacher wants, and without additional information, it is hard for the robot to know which features are relevant and which should be ignored. This means that a single demonstration maps to several different behaviours the teacher might have intended. This one-to-many (or many-to-many) mapping from a demonstration (or several demonstrations) into possible intended behaviours is the ambiguity that is the topic of this paper. Ambiguity is defined as the size of the current hypothesis space. We investigate the nature of the ambiguity for different kinds of hypothesis spaces and how it is reduced by a new concept learning algorithm.

  • 8.
    Bensch, Suna
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hellström, ThomasUmeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Proceedings of Umeå's 18th student conference in computing science: USCCS 2014.12014Proceedings (redaktörskap) (Övrigt vetenskapligt)
  • 9.
    Bensch, Suna
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hellström, ThomasUmeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Proceedings of Umeå's 20th student conference in computing science: USCCS 20162016Proceedings (redaktörskap) (Övrigt vetenskapligt)
  • 10.
    Bensch, Suna
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hellström, ThomasUmeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Proceedings of Umeå's 21st student conference in computing science: USCCS 20172017Proceedings (redaktörskap) (Övrigt vetenskapligt)
  • 11.
    Bensch, Suna
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. Department of Computing Science.
    Hellström, ThomasUmeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. Department of Computing Science.
    Proceedings of Umeå's 22nd Student Conference in Computing Science (USCCS 2018)2018Proceedings (redaktörskap) (Övrigt vetenskapligt)
  • 12.
    Bensch, Suna
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hellström, ThomasUmeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Proceedings of Umeå's 23rd Student Conference in Computing Science: USCCS 20192019Proceedings (redaktörskap) (Övrigt vetenskapligt)
    Abstract [en]

    The Umeå Student Conference in Computing Science (USCCS) is organized annually as part of a course given by the Computing Science department at Umeå University. The objective of the course is to give the students a practical introduction to independent research, scientific writing, and oral presentation.

    A student who participates in the course first selects a topic and a research question that he or she is interested in. If the topic is accepted, the student outlines a paper and composes an annotated bibliography to give a survey of the research topic. The main work consists of conducting the actual research that answers the question asked, and convincingly and clearly reporting the results in a scientific paper. Another major part of the course is multiple internal peer review meetings in which groups of students read each others’ papers and give feedback to the author. This process gives valuable training in both giving and receiving criticism in a constructive manner. Altogether, the students learn to formulate and develop their own ideas in a scientific manner, in a process involving internal peer reviewing of each other’s work and under supervision of the teachers, and incremental development and refinement of a scientific paper.

    Each scientific paper is submitted to USCCS through an on-line submission system, and receives reviews written by members of the Computing Science department. Based on the review, the editors of the conference proceedings (the teachers of the course) issue a decision of preliminary acceptance of the paper to each author. If, after final revision, a paper is accepted, the student is given the opportunity to present the work at the conference. The review process and the conference format aims at mimicking realistic settings for publishing and participation at scientific conferences.

  • 13.
    Bensch, Suna
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Towards Proactive Robot Behavior Based on Incremental Language Analysis2014Ingår i: MMRWHRI '14 Proceedings of the 2014 Workshop on Multimodal, Multi-Party, Real-World Human-Robot Interaction / [ed] Mary Ellen Foster, Manuel Giuliani, Ronald P. A. Petrick, 2014, s. 21-22Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper describes ongoing and planned work on incremental language processing coupled to inference of expected robot actions. Utterances are processed word-by-word, simultaneously with inference of expected robot actions, thus enabling the robot to prepare and act proactively to human utterances. We believe that such a model results in more natural human-robot communication since proactive behavior is a feature of human-human communication.

  • 14.
    Bensch, Suna
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. Department of Computing Science.
    Jevtic, Aleksandar
    Institut de Robotica i Informatica Industrial, Technical University of Catalonia, Spain.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    On Interaction Quality in Human-Robot Interaction2017Ingår i: Proceedings of the 9th International Conference on Agents and Artificial Intelligence / [ed] H. Jaap van den Herik, Ana Paula Rocha, Joaquim Filipe, Setúbal: SciTePress, 2017, Vol. 1, s. 182-189Konferensbidrag (Refereegranskat)
    Abstract [en]

    In many complex robotics systems, interaction takes place in all directions between human, robot, and environment. Performance of such a system depends on this interaction, and a proper evaluation of a system must build on a proper modeling of interaction, a relevant set of performance metrics, and a methodology to combine metrics into a single performance value. In this paper, existing models of human-robot interaction are adapted to fit complex scenarios with one or several humans and robots. The interaction and the evaluation process is formalized, and a general method to fuse performance values over time and for several performance metrics is presented. The resulting value, denoted interaction quality, adds a dimension to ordinary performance metrics by being explicit about the interplay between performance metrics, and thereby provides a formal framework to understand, model, and address complex aspects of evaluation of human-robot interaction. 

  • 15.
    Billing, Erik
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    A formalism for learning from demonstration2010Ingår i: Paladyn Journal of Behavioral Robotics, ISSN 2080-9778, 2081-4836 (e-version), Vol. 1, nr 1, s. 1-13Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The paper describes and formalizes the concepts and assumptions involved in Learning from Demonstration (LFD), a common learning technique used in robotics. LFD-related concepts like goal, generalization, and repetition are here defined, analyzed, and put into context. Robot behaviors are described in terms of trajectories through information spaces and learning is formulated as mappings between some of these spaces. Finally, behavior primitives are introduced as one example of good bias in learning, dividing the learning process into the three stages of behavior segmentation, behavior recognition, and behavior coordination. The formalism is exemplified through a sequence learning task where a robot equipped with a gripper arm is to move objects to specific areas. The introduced concepts are illustrated with special focus on how bias of various kinds can be used to enable learning from a single demonstration, and how ambiguities in demonstrations can be identified and handled.

  • 16.
    Billing, Erik
    et al.
    Umeå universitet, Teknisk-naturvetenskaplig fakultet, Institutionen för datavetenskap.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskaplig fakultet, Institutionen för datavetenskap.
    Behavior recognition for segmentation of demonstrated tasks2008Ingår i: IEEE SMC International Conference on Distributed Human-Machine Systems (DHMS), 2008Konferensbidrag (Refereegranskat)
    Abstract [en]

    One common approach to the robot learning technique Learning From Demonstration, is to use a set of pre-programmed skills as building blocks for more complex tasks. One important part of this approach is recognition of these skills in a demonstration comprising a stream of sensor and actuator data. In this paper, three novel techniques for behavior recognition are presented and compared. The first technique is function-oriented and compares actions for similar inputs. The second technique is based on auto-associative neural networks and compares reconstruction errors in sensory-motor space. The third technique is based on S-Learning and compares sequences of patterns in sensory-motor space. All three techniques compute an activity level which can be seen as an alternative to a pure classification approach. Performed tests show how the former approach allows a more informative interpretation of a demonstration, by not determining "correct" behaviors but rather a number of alternative interpretations.

  • 17.
    Billing, Erik
    et al.
    Umeå universitet, Teknisk-naturvetenskaplig fakultet, Institutionen för datavetenskap.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskaplig fakultet, Institutionen för datavetenskap.
    Formalising learning from demonstration2008Rapport (Övrigt vetenskapligt)
    Abstract [en]

    The paper describes and formalizes the concepts and assumptions involved in Learning from Demonstration (LFD), a common learning technique used in robotics. Inspired by the work on planning and actuation by LaValle, common LFD-related concepts like goal, generalization, and repetition are here defined, analyzed, and put into context. Robot behaviors are described in terms of trajectories through information spaces and learning is formulated as the mappings between some of these spaces. Finally, behavior primitives are introduced as one example of useful bias in the learning process, dividing the learning process into the three stages of behavior segmentation, behavior recognition, and behavior coordination.

  • 18.
    Billing, Erik
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Predictive Learning in Context2010Ingår i: Proceedings of the tenth international conference on epigenetic robotics: modeling cognitive development in robotic systems / [ed] Birger Johansson, Erol Sahin & Christian Balkenius, Lund, Sweden, 2010, s. 157-158Konferensbidrag (Refereegranskat)
  • 19.
    Billing, Erik
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Janlert, Lars Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Behavior recognition for learning from demonstration2010Ingår i: Proceedings of IEEE International Conference on Robotics and Automation / [ed] Nancy M. Amato et. al, 2010, s. 866-872Konferensbidrag (Refereegranskat)
    Abstract [en]

    Two methods for behavior recognition are presented and evaluated. Both methods are based on the dynamic temporal difference algorithm Predictive Sequence Learning (PSL) which has previously been proposed as a learning algorithm for robot control. One strength of the proposed recognition methods is that the model PSL builds to recognize behaviors is identical to that used for control, implying that the controller (inverse model) and the recognition algorithm (forward model) can be implemented as two aspects of the same model. The two proposed methods, PSLE-Comparison and PSLH-Comparison, are evaluated in a Learning from Demonstration setting, where each algorithm should recognize a known skill in a demonstration performed via teleoperation. PSLH-Comparison produced the smallest recognition error. The results indicate that PSLH-Comparison could be a suitable algorithm for integration in a hierarchical control system consistent with recent models of human perception and motor control.

  • 20.
    Billing, Erik
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Janlert, Lars Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Model-free learning from demonstration2010Ingår i: ICAART 2010 - Proceedings of the international conference on agents and artificial intelligence:  volume 2 / [ed] Joaquim Filipe, Ana LN Fred, Bernadette Sharp, Portugal: INSTICC , 2010, s. 62-71Konferensbidrag (Refereegranskat)
    Abstract [en]

    A novel robot learning algorithm called Predictive Sequence Learning (PSL) is presented and evaluated. PSL is a model-free prediction algorithm inspired by the dynamic temporal difference algorithm S-Learning. While S-Learning has previously been applied as a reinforcement learning algorithm for robots, PSL is here applied to a Learning from Demonstration problem. The proposed algorithm is evaluated on four tasks using a Khepera II robot. PSL builds a model from demonstrated data which is used to repeat the demonstrated behavior. After training, PSL can control the robot by continually predicting the next action, based on the sequence of passed sensor and motor events. PSL was able to successfully learn and repeat the first three (elementary) tasks, but it was unable to successfully repeat the fourth (composed) behavior. The results indicate that PSL is suitable for learning problems up to a certain complexity, while higher level coordination is required for learning more complex behaviors.

  • 21.
    Billing, Erik
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Janlert, Lars Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Predictive learning from demonstration2011Ingår i: Agents and artificial Intelligence: Second International Conference, ICAART 2010, Valencia, Spain, January 22-24, 2010. Revised Selected Papers / [ed] Filipe, Joaquim, Fred, Ana, Sharp, Bernadette, Berlin: Springer Verlag , 2011, 1, s. 186-200Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    A model-free learning algorithm called Predictive Sequence Learning (PSL) is presented and evaluated in a robot Learning from Demonstration (LFD) setting. PSL is inspired by several functional models of the brain. It constructs sequences of predictable sensory-motor patterns, without relying on predefined higher-level concepts. The algorithm is demonstrated on a Khepera II robot in four different tasks. During training, PSL generates a hypothesis library from demonstrated data. The library is then used to control the robot by continually predicting the next action, based on the sequence of passed sensor and motor events. In this way, the robot reproduces the demonstrated behavior. PSL is able to successfully learn and repeat three elementary tasks, but is unable to repeat a fourth, composed behavior. The results indicate that PSL is suitable for learning problems up to a certain complexity, while higher level coordination is required for learning more complex behaviors.

  • 22.
    Billing, Erik
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Janlert, Lars Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Robot learning from demonstration using predictive sequence learning2011Ingår i: Robotic systems: applications, control and programming / [ed] Ashish Dutta, Kanpur, India: IN-TECH, 2011, s. 235-250Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    In this chapter, the prediction algorithm Predictive Sequence Learning (PSL) is presented and evaluated in a robot Learning from Demonstration (LFD) setting. PSL generates hypotheses from a sequence of sensory-motor events. Generated hypotheses can be used as a semi-reactive controller for robots. PSL has previously been used as a method for LFD, but suffered from combinatorial explosion when applied to data with many dimensions, such as high dimensional sensor and motor data. A new version of PSL, referred to as Fuzzy Predictive Sequence Learning (FPSL), is presented and evaluated in this chapter. FPSL is implemented as a Fuzzy Logic rule base and works on a continuous state space, in contrast to the discrete state space used in the original design of PSL. The evaluation of FPSL shows a significant performance improvement in comparison to the discrete version of the algorithm. Applied to an LFD task in a simulated apartment environment, the robot is able to learn to navigate to a specific location, starting from an unknown position in the apartment.

  • 23.
    Billing, Erik
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Janlert, Lars Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Simultaneous control and recognition of demonstrated behavior2011Rapport (Övrigt vetenskapligt)
    Abstract [en]

    A method for Learning from Demonstration (LFD) is presented and evaluated on a simulated Robosoft Kompai robot. The presented algorithm, called Predictive Sequence Learning (PSL), builds fuzzy rules describing temporal relations between sensory-motor events recorded while a human operator is tele-operating the robot. The generated rule base can be used to control the robot and to predict expected sensor events in response to executed actions. The rule base can be trained under different contexts, represented as fuzzy sets. In the present work, contexts are used to represent different behaviors. Several behaviors can in this way be stored in the same rule base and partly share information. The context that best matches present circumstances can be identified using the predictive model and the robot can in this way automatically identify the most suitable behavior for precent circumstances. The performance of PSL as a method for LFD is evaluated with, and without, contextual information. The results indicate that PSL without contexts can learn and reproduce simple behaviors. The system also successfully identifies the most suitable context in almost all test cases. The robot's ability to reproduce more complex behaviors, with partly overlapping and conflicting information, significantly increases with the use of contexts. The results support a further development of PSL as a component of a dynamic hierarchical system performing control and predictions on several levels of abstraction. 

  • 24. Billing, Erik
    et al.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Janlert, Lars-Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Simultaneous recognition and reproduction of demonstrated behavior2015Ingår i: Biologically Inspired Cognitive Architectures, ISSN 2212-683X, Vol. 12, s. 43-53Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Predictions of sensory-motor interactions with the world is often referred to as a key component in cognition. We here demonstrate that prediction of sensory-motor events, i.e., relationships between percepts and actions, is sufficient to learn navigation skills for a robot navigating in an apartment environment. In the evaluated application, the simulated Robosoft Kompai robot learns from human demonstrations. The system builds fuzzy rules describing temporal relations between sensory-motor events recorded while a human operator is tele-operating the robot. With this architecture, referred to as Predictive Sequence Learning (PSL), learned associations can be used to control the robot and to predict expected sensor events in response to executed actions. The predictive component of PSL is used in two ways: (1) to identify which behavior that best matches current context and (2) to decide when to learn, i.e., update the confidence of different sensory-motor associations. Using this approach, knowledge interference due to over-fitting of an increasingly complex world model can be avoided. The system can also automatically estimate the confidence in the currently executed behavior and decide when to switch to an alternate behavior. The performance of PSL as a method for learning from demonstration is evaluated with, and without, contextual information. The results indicate that PSL without contextual information can learn and reproduce simple behaviors, but fails when the behavioral repertoire becomes more diverse. When a contextual layer is added, PSL successfully identifies the most suitable behavior in almost all test cases. The robot's ability to reproduce more complex behaviors, with partly overlapping and conflicting information, significantly increases with the use of contextual information. The results support a further development of PSL as a component of a dynamic hierarchical system performing control and predictions on several levels of abstraction.

  • 25. Bontsema, J.
    et al.
    Hemming, J.
    Pekkeriet, E.
    Saeys, W.
    Edan, Y.
    Shapiro, A.
    Hočevar, M.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Oberti, R.
    Armada, M.
    Ulbrich, H.
    Baur, J.
    Debilde, B.
    Best, S.
    Evain, S.
    Gauchel, W.
    Ringdahl, Ola
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    CROPS: high tech agricultural robots2014Konferensbidrag (Övrigt vetenskapligt)
  • 26. Bontsema, Jan
    et al.
    Hemming, Jochen
    Pekkeriet, Erik
    Saeys, Wouter
    Edan, Yael
    Shapiro, Amir
    Hočevar, Marko
    Oberti, Roberto
    Armada, Manuel
    Ulbrich, Heinz
    Baur, Jörg
    Debilde, Benoit
    Best, Stanley
    Evain, Sébastien
    Gauchel, Wolfgang
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Ringdahl, Ola
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    CROPS: Clever Robots for Crops2015Ingår i: Engineering & Technology Reference, ISSN 2056-4007, Vol. 1, nr 1Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In the EU-funded CROPS project robots are developed for site-specific spraying and selective harvesting of fruit and fruit vegetables. The robots are being designed to harvest crops, such as greenhouse vegetables, apples, grapes and for canopy spraying in orchards and for precision target spraying in grape vines. Attention is paid to the detection of obstacles for autonomous navigation in a safe way in plantations and forests. For the different applications, platforms were built. Sensing systems and vision algorithms have been developed. For software the Robot Operating System is used. A 9 degrees of freedom manipulator was designed and tested for sweet-pepper harvesting, apple harvesting and in close range spraying. For the applications different end-effectors were designed and tested. For sweet pepper a platform that can move in between the crop rows on the common greenhouse rail system which also serves as heating pipes was built. The apple harvesting platform is based on a current mechanical grape harvester. In discussion with growers so-called ‘walls of fruit trees’ have been designed which bring robots closer to the practice. A canopy-optimised sprayer has been designed as a trailed sprayer with a centrifugal blower. All the applications have been tested under practical conditions.

  • 27.
    Fonooni, Benjamin
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Janlert, Lars-Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Learning High-Level Behaviors From Demonstration Through Semantic Networks2012Ingår i: Proceedings of 4th International Conference on Agents and Artificial Intelligence, 2012, s. 419-426Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper we present an approach for high-level behavior recognition and selection integrated with alow-level controller to help the robot to learn new skills from demonstrations. By means of SemanticNetwork as the core of the method, the robot gains the ability to model the world with concepts and relatethem to low-level sensory-motor states. We also show how the generalization ability of Semantic Networkscan be used to extend learned skills to new situations.

  • 28.
    Fonooni, Benjamin
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Janlert, Lars-Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Towards Goal Based Architecture Design for Learning High-Level Representation of Behaviors from Demonstration2013Ingår i: 2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), 2013, s. 67-74Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper gives a brief overview of challenges indesigning cognitive architectures for Learning fromDemonstration. By investigating features and functionality ofsome related architectures, we propose a modular architectureparticularly suited for sequential learning high-levelrepresentations of behaviors. We head towards designing andimplementing goal based imitation learning that not only allowsthe robot to learn necessary conditions for executing particularbehaviors, but also to understand the intents of the tutor andreproduce the same behaviors accordingly.

  • 29.
    Fonooni, Benjamin
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Jevtić, Aleksandar
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Janlert, Lars-Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Applying Ant Colony Optimization Algorithms for High-Level Behavior Learning and Reproduction from Demonstrations2015Ingår i: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 65, s. 24-39Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In domains where robots carry out human’s tasks, the ability to learn new behaviors easily and quickly plays an important role. Two major challenges with Learning from Demonstration (LfD) are to identify what information in a demonstrated behavior requires attention by the robot, and to generalize the learned behavior such that the robot is able to perform the same behavior in novel situations. The main goal of this paper is to incorporate Ant Colony Optimization (ACO) algorithms into LfD in an approach that focuses on understanding tutor's intentions and learning conditions to exhibit a behavior. The proposed method combines ACO algorithms with semantic networks and spreading activation mechanism to reason and generalize the knowledge obtained through demonstrations. The approach also provides structures for behavior reproduction under new circumstances. Finally, applicability of the system in an object shape classification scenario is evaluated.

  • 30.
    Fonooni, Benjamin
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Thomas, Hellström
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Applying a Priming Mechanism for Intention Recognition in Shared Control2015Ingår i: 2015 IEEE INTERNATIONAL MULTI-DISCIPLINARY CONFERENCE ON COGNITIVE METHODS IN SITUATION AWARENESS AND DECISION SUPPORT (COGSIMA), 2015, s. 35-41Konferensbidrag (Refereegranskat)
    Abstract [en]

    In many robotics shared control applications, users are forced to focus hard on the robot due to the task’s high sensitivity or the robot’s misunderstanding of the user’s intention. This brings frustration and dissatisfaction to the user and reduces overall efficiency. The user’s intention is sometimes unclear and hard to identify without some kind of bias in the identification process. In this paper, we present a solution in which an attentional mechanism helps the robot to recognize the user’s intention. The solution uses a priming mechanism and parameterized behavior primitives to support intention recognition and improve shared control for teleoperation tasks.

  • 31.
    Fonooni, Benjamin
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Thomas, Hellström
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    On the Similarities Between Control Based and Behavior Based Visual Servoing2015Ingår i: Proceedings of the 30th Annual ACM Symposium on Applied Computing, New York: Association for Computing Machinery (ACM), 2015, s. 320-326Konferensbidrag (Refereegranskat)
    Abstract [en]

    Abstract Robotics is tightly connected to both artificial intelligence (AI) and control theory. Both AI and control based robotics are active and successful research areas, but research is often conducted by well separated communities. In this paper, we compare the two approaches in a case study for the design of a robot that should move its arm towards an object with the help of camera data. The control based approach is a model-free version of Image Based Visual Servoing (IBVS), which is based on mathematical modeling of the sensing and motion task. The AI approach, here denoted Behavior-Based Visual Servoing (BBVS), contains elements that are biologically plausible and inspired by schema-theory. We show how the two approaches lead to very similar solutions, even identical given a few simplifying assumptions. This similarity is shown both analytically and numerically. However, in a simple picking task with a 3 DoF robot arm, BBVS shows significantly higher performance than the IBVS approach, partly because it contains more manually tuned parameters. While the results obviously do not apply to all tasks and solutions, it illustrates both strengths and weaknesses with both approaches, and how they are tightly connected and share many similarities despite very different starting points and methodologies.

  • 32.
    Fonooni, Benjamin
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Thomas, Hellström
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Janlert, Lars-Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Priming as a means to reduce ambiguity in learning from demonstration2016Ingår i: International Journal of Social Robotics, ISSN 1875-4791, E-ISSN 1875-4805, Vol. 8, nr 1, s. 5-19Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Learning from Demonstration (LfD) is an established robot learning technique by which a robot acquires a skill by observing a human or robot teacher demonstrating the skill. In this paper we address the ambiguity involved in inferring the intention with one or several demonstrations. We suggest a method based on priming, and a memory model with similarities to human learning. Conducted experiments show that the developed method leads to faster and improved understanding of the intention with a demonstration by reducing ambiguity.

  • 33.
    Hamrin, Maria
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Norqvist, Patrik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Andre, Mats
    Eriksson, AI
    A statistical study of ion energization at 1700 km in the auroral region2002Ingår i: Annales Geophysicae, ISSN 0992-7689, E-ISSN 1432-0576, Vol. 20, nr 12, s. 1943-1958Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We present a comprehensive overview of several potentially relevant causes for the oxygen energization in the auroral region. Data from the Freja satellite near 1700 km altitude are used for an unconditional statistical investigation. The data are obtained in the Northern Hemisphere during 21 months in the declining phase of the solar cycle. The importance of various wave types for the ion energization is statistically studied. We also investigate the correlation of ion heating with precipitating protons, accelerated auroral electrons, suprathermal electron bursts, the electron density variations, K-P index and solar illumination of the nearest conjugate ionosphere. We find that sufficiently strong broadband ELF waves, electromagnetic ion cyclotron waves, and waves around the lower hybrid frequency are foremost associated with the ion heating. However, magnetosonic waves, with a sharp, lower frequency cutoff just below the proton gyrofrequency, are not found to contribute to the ion heating. In the absence of the first three wave emissions, transversely energized ions are rare. These wave types are approximately equally efficient in heating the ions, but we find that the main source for the heating is broadband ELF waves, since they are most common in the auroral region. We have also observed that the conditions for ion heating are more favourable for smaller ratios of the spectral densities S-E/S-B of the broadband ELF waves at the oxygen gyrofrequency.

  • 34.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    A random walk through the stock market1998Övrigt (Övrigt vetenskapligt)
  • 35.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    An intelligent rollator with steering by braking2012Rapport (Övrigt vetenskapligt)
    Abstract [en]

    Walking aids such as rollators help a lot of individuals to maintain mobility and independence. While these devices clearly improve balance and mobility they also lead to increased risk of falling accidents. With an increasing proportion of elderly in the population, there is a clear need for improving these devices. This paper describes ongoing work on the development of ROAR - an intelligent rollator that can help users with limited vision, cognition or motoric abilities. Automatic detection and avoidance of obstacles such as furniture and doorposts simplify usage in cluttered indoor environments. For outdoors usage, the design includes a function to avoid curbs and other holes that may otherwise cause serious accidents. Ongoing work includes a novel approach to compensate for sideway drift that occur both indoors and outdoors for users with certain types of cognitive or motoric disabilities. Also the control mechanism differs from other similar designs. Steering is achieved by activating electrical brakes instead of turning the front wheels. Furthermore, cheap infrared sensors are used instead of a laser scanner for detection of objects.  Altogether, the design is believed to lead to increased acceptability, lower price and safer operation.

  • 36.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Kinematics Equations for Differential Drive and Articulated Steering2011Rapport (Övrigt vetenskapligt)
  • 37.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Licens att döda2012Ingår i: Forskning och framsteg, ISSN 0015-7937, nr 5/6, s. 26-29Artikel i tidskrift (Övrig (populärvetenskap, debatt, mm))
  • 38.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    On the moral responsibility of military robots2013Ingår i: Ethics and Information Technology, ISSN 1388-1957, E-ISSN 1572-8439, Vol. 15, nr 2, s. 99-107Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This article discusses mechanisms and principles for assignment of moral responsibility to intelligent robots, with special focus on military robots. We introduce the concept autonomous power as a new concept, and use it to identify the type of robots that call for moral considerations. It is furthermore argued that autonomous power, and in particular the ability to learn, is decisive for assignment of moral responsibility to robots. As technological development will lead to robots with increasing autonomous power, we should be prepared for a future when people blame robots for their actions. It is important to, already today, investigate the mechanisms that control human behavior in this respect. The results may be used when designing future military robots, to control unwanted tendencies to assign responsibility to the robots. Independent of the responsibility issue, the moral quality of robots’ behavior should be seen as one of many performance measures by which we evaluate robots. How to design ethics based control systems should be carefully investigated already now. From a consequentialist view, it would indeed be highly immoral to develop robots capable of performing acts involving life and death, without including some kind of moral framework.

  • 39.
    Hellström, Thomas
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Bensch, Suna
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Modeling Interaction for Understanding in HRI2018Ingår i: Proceedings of Explainable Robotic Systems Workshop at HRI 2018, Chicago, USA, March 2018, 2018Konferensbidrag (Refereegranskat)
    Abstract [en]

    As robots become more and more capable and autonomous, there is an increased need for humans to understand what the robots do and think. In this paper we investigate what such understanding means and includes, and how robots are and can be designed to support understanding. We present a model of interaction for understanding. The aim is to provide a uniform formal understanding of the large body of existing work, and also to support continued work in the area.

  • 40.
    Hellström, Thomas
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Bensch, Suna
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Understandable Robots: What, Why, and How2018Ingår i: Paladyn - Journal of Behavioral Robotics, ISSN 2080-9778, E-ISSN 2081-4836, Vol. 9, nr 1, s. 110-123Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    As robots become more and more capable and autonomous, there is an increasing need for humans to understand what the robots do and think. In this paper, we investigate what such understanding means and in- cludes, and how robots can be designed to support un- derstanding. After an in-depth survey of related earlier work, we discuss examples showing that understanding includes not only the intentions of the robot, but also de- sires, knowledge, beliefs, emotions, perceptions, capabil- ities, and limitations of the robot. The term understandingis formally defined, and the term communicative actions is defined to denote the various ways in which a robot may support a human’s understanding of the robot. A novel model of interaction for understanding is presented. The model describes how both human and robot may utilize a first or higher-order theory of mind to understand each other and perform communicative actions in order to sup- port the other’s understanding. It also describes simpler cases in which the robot performs static communicative actions in order to support the human’s understanding of the robot. In general, communicative actions performed by the robot aim at reducing the mismatch between the mind of the robot, and the robot’s inferred model of the human’s model of the mind of the robot. Based on the pro- posed model, a set of questions are formulated, to serve as support when developing and implementing the model in real interacting robots.

  • 41.
    Hellström, Thomas
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hohnloser, Peter
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Ringdahl, Ola
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Tree diameter estimation using laser scanner2012Rapport (Övrigt vetenskapligt)
    Abstract [en]

    Accurate vehicle localization in forest environments is still an unresolved problem. GPS has obvious limitations in dense forest, and has to be mixed with other techniques to provide satisfying solutions. One possible way is to localize the vehicle relative to trees detected around the vehicle. The first step to implement this method is is to find reliable methods to detect trees, and also to match them to maps. The reliability of this matching operation is improved by accurate estimations of tree diameter. In this paper we evaluate a number of existing algorithms for detection of trees and estimation of tree diameter. Three new algorithms are also suggested. All algorithms were evaluated in field experiments at three different locations with varying tree trunk visibility. The results show that one of the existing algorithms is clearly less reliable than the other two. Noticeable is that the existing algorithms often overestimate tree trunk diameter. The new algorithms mostly underestimate, but are most accurate in some situations. 

  • 42.
    Hellström, Thomas
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Johansson, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Ringdahl, Ola
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    A Java-based middleware for control and sensing in mobile robotics2008Ingår i: International Conference on Intelligent Automation and Robotics 2008, 2008, s. 649-654Konferensbidrag (Refereegranskat)
    Abstract [en]

    Many of the existing mobile-robot software packages do not include handling of sensors and actuators in a sufficiently systematic and uniform way, as described later in this section. The software framework proposed in this paper, denoted NAV2000, addresses the specific need for interchangeability of components in robotics. At the lowest level, sensors, and sometimes also actuators, often have to be replaced by similar, yet not identical, components. At a higher level, the target vehicle often changes during the work process. The presented software provides a framework that supports these replacements and allows configurations of sensors, actuators, and target machines to be specified and manipulated in an efficient manner. The system can be distributed over a network of computers if some software modules require more computing power, i.e. more hardware can be added to the system without any software changes. To accomplish sufficient monitoring of the system's health, a dedicated system keeps track of all software modules. The system uses logfiles to enable convenient debugging and performance analysis of hardware and software modules. The software has been developed as part of, and is currently in use in, a R&D-project for an autonomous path-tracking forest machine.

  • 43.
    Hellström, Thomas
    et al.
    Umeå universitet, Teknisk-naturvetenskaplig fakultet, Institutionen för datavetenskap.
    Johansson, Thomas
    Umeå universitet, Teknisk-naturvetenskaplig fakultet, Institutionen för datavetenskap.
    Ringdahl, Ola
    Umeå universitet, Teknisk-naturvetenskaplig fakultet, Institutionen för datavetenskap.
    Development of an Autonomous Forest Machine for Path Tracking2006Ingår i: Field and Service Robotics: Results of the 5th International Conference, New York: Springer , 2006, s. 603-614Konferensbidrag (Refereegranskat)
    Abstract [en]

    In many respects traditional automation in the forest-machine industry hasreached an upper limit, since the driver already has to deal with an excess ofinformation and take too many decisions at a very high pace. To furtherautomation still, introduction of semi-autonomous and autonomous functions areexpected and considered necessary. This paper describes an ongoing projectalong these ideas. We describe the development of the hardware and software ofan unmanned shuttle that shifts timber from the area of felling to the mainroads for further transportation. A new path-tracking algorithm is introduced,and demonstrated as being superior to standard techniques, such as Follow theCarrot and Pure Pursuit. To facilitate the research and development, acomprehensive software architecture for sensor and actuator interfacing isdeveloped. Obstacle avoidance is accomplished by a new kind of radar,developed for and by the automotive industry. Localization is accomplished by combining data from a Real-Time Kinematic DifferentialGPS/GLONASS and odometry. Tests conducted on a simulator and asmall-scale robot show promising results. Tests on the real forest machine areongoing.

  • 44.
    Hellström, Thomas
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. Umeå universitet, Medicinska fakulteten, Institutionen för samhällsmedicin och rehabilitering. University Hospital of Northern Sweden, Umeå, Sweden.
    Lindahl, Olof
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper. Umeå universitet, Medicinska fakulteten, Institutionen för samhällsmedicin och rehabilitering. University Hospital of Northern Sweden, Umeå, Sweden.
    Bäcklund, Tomas
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper. Umeå universitet, Medicinska fakulteten, Institutionen för samhällsmedicin och rehabilitering. University Hospital of Northern Sweden, Umeå, Sweden.
    Karlsson, Marcus
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper. Umeå universitet, Medicinska fakulteten, Institutionen för samhällsmedicin och rehabilitering. University Hospital of Northern Sweden, Umeå, Sweden.
    Hohnloser, Peter
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. Umeå universitet, Medicinska fakulteten, Institutionen för samhällsmedicin och rehabilitering. University Hospital of Northern Sweden, Umeå, Sweden.
    Bråndal, Anna
    Umeå universitet, Medicinska fakulteten, Institutionen för samhällsmedicin och rehabilitering. Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. University Hospital of Northern Sweden, Umeå, Sweden.
    Hu, Xiaolei
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. Umeå universitet, Medicinska fakulteten, Institutionen för samhällsmedicin och rehabilitering. University Hospital of Northern Sweden, Umeå, Sweden; Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Sweden.
    Wester, Per
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. University Hospital of Northern Sweden, Umeå, Sweden.
    An intelligent rollator for mobility impaired persons, especially stroke patients2016Ingår i: Journal of Medical Engineering & Technology, ISSN 0309-1902, E-ISSN 1464-522X, Vol. 40, nr 5, s. 270-279Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    An intelligent rollator (IRO) was developed that aims at obstacle detection and guidance to avoid collisions and accidental falls. The IRO is a retrofit four-wheeled rollator with an embedded computer, two solenoid brakes, rotation sensors on the wheels and IR-distance sensors. The value reported by each distance sensor was compared in the computer to a nominal distance. Deviations indicated a present obstacle and caused activation of one of the brakes in order to influence the direction of motion to avoid the obstacle. The IRO was tested by seven healthy subjects with simulated restricted and blurred sight and five stroke subjects on a standardised indoor track with obstacles. All tested subjects walked faster with intelligence deactivated. Three out of five stroke patients experienced more detected obstacles with intelligence activated. This suggests enhanced safety during walking with IRO. Further studies are required to explore the full value of the IRO.

  • 45.
    Hellström, Thomas
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Lärkeryd, Per
    Indexator .
    Nordfjell, Tomas
    Department of Forest Resource Management, Swedish University of Agricultural Sciences.
    Ringdahl, Ola
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Autonomous forest vehicles: historic, envisioned, and state-of-the-art2009Ingår i: International Journal of Forest Engineering, ISSN 1494-2119, E-ISSN 1913-2220, Vol. 20, nr 1, s. 33-38Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The feasibility of using autonomous forest vehicles (which can be regarded as logical developments in the ongoing automation of forest machines), the systems that could be applied in them, their potential advantages and their limitations (in the foreseeable future) are considered here. The aims were to analyse: (1) the factors influencing the degree of automation in logging; (2) the technical principles that can be applied to autonomous forest machines, and (3) the feasibility of developing an autonomous path-tracking forest vehicle. A type of vehicle that is believed to have considerable commercial potential is an autonomous forwarder. The degree of automation is influenced by increased productivity, the machine operator as a bottle-neck, cost reduction, and environmental aspects. Technical principles that can be applied to autonomous forest vehicles are satellite navigation, wheel odometry, laser scanner and radar. A new path-tracking algorithm has been developed to reduce deviations from the desired path by utilizing the driver’s steering commands. The presented system has demonstrated both possibilities and difficulties associated with autonomous forest machines. It is in a field study shown that it is quite possible for them to learn and track a path previously demonstrated by an operator with an accuracy of 0.1m on flat ground and also to detect and avoid unexpected obstacles. Although the forest machine safely avoids obstacles, the study shows that further research in the field of obstacle avoidance is needed to optimize performance and ensure safe operation in a real forest environment.

  • 46.
    Hellström, Thomas
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Lärkeryd, Pär
    Nordfjell, Thomas
    Ringdahl, Ola
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Autonomous forest machines: Past present and future2008Rapport (Övrigt vetenskapligt)
    Abstract [en]

    The feasibility of using autonomous forest vehicles (which can be regarded as logical developments in the ongoing automation of forest machines), the systems that could be applied in them, their potential advantages and their limitations (in the foreseeable future) are considered here. The aims were to analyse: (1) the factors influencing the degree of automation in logging; (2) the technical principles that can be applied to autonomous forest machines, and (3) the feasibility of developing an autonomous path-tracking forest vehicle. A class of such vehicles that are believed to have considerable commercial potential is autonomous wood shuttles (forwarders). The degree of automation is influenced by increased productivity, the machine operator as a bottle-neck, cost reduction, and environmental aspects. Technical principles that can be applied to autonomous forest vehicles are satellite navigation, laser odometry, wheel odometry, laser scanner and radar. The presented system has demonstrated both possibilities and difficulties associated with autonomous forest machines. It is in a field study shown that it is quite possible for them to learn and track a path previously demonstrated by an operator with an accuracy of 0.1m on flat ground. A new pathtracking algorithm has been developed to reduce deviations by utilizing the driver’s steering commands.

  • 47.
    Hellström, Thomas
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Ostovar, Ahmad
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Detection of Trees Based on Quality Guided Image Segmentation2014Ingår i: Second International Conference on Robotics and associated High-technologies and Equipment for Agriculture and forestry (RHEA-2014): New trends in mobile robotics, perception and actuation for agriculture and forestry / [ed] Pablo Gonzalez-de-Santos and Angela Ribeiro, RHEA Consortium , 2014, s. 531-540Konferensbidrag (Refereegranskat)
    Abstract [en]

    Detection of objects is crucial for any autonomous field robot orvehicle. Typically, object detection is used to avoid collisions whennavigating, but detection capability is essential also for autonomous or semiautonomousobject manipulation such as automatic gripping of logs withharvester cranes used in forestry. In the EU financed project CROPS,special focus is given to detection of trees, bushes, humans, and rocks inforest environments. In this paper we address the specific problem ofidentifying trees using color images. A presented method combinesalgorithms for seed point generation and segmentation similar to regiongrowing. Both algorithms are tailored by heuristics for the specific task oftree detection. Seed points are generated by scanning a verticallycompressed hue matrix for outliers. Each one of these seed points is thenused to segment the entire image into segments with pixels similar to asmall surrounding around the seed point. All generated segments are refinedby a series of morphological operations, taking into account thepredominantly vertical nature of trees. The refined segments are evaluatedby a heuristically designed quality function. For each seed point, thesegment with the highest quality is selected among all segments that coverthe seed point. The set of all selected segments constitute the identified treeobjects in the image. The method was evaluated with images containing intotal 197 trees, collected in forest environments in northern Sweden. In thispreliminary evaluation, precision in detection was 81% and recall rate 87%.

  • 48.
    Hellström, Thomas
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Ringdahl, Ola
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    A software framework for agricultural and forestry robotics2012Ingår i: Proceedings of the first International Conference on Robotics and associated High-technologies and Equipment for agriculture: Applications of automated systems and robotics for crop protection in sustainable precision agriculture / [ed] Andrea Peruzzi, Pisa: Pisa University Press , 2012, s. 171-176Konferensbidrag (Refereegranskat)
    Abstract [en]

    In  this  paper  we  describe  on-going  development  of  a  generic software framework for development of agricultural and forestry robots.  The  goal  is  to  provide  generic  high-level  functionality and to encourage distributed and structured programming, thus leading to faster and simplified development of robots. Different aspects  of  the  framework  are  described  using  different architecture views.  We show how these views complement each other  in  a  way  that  supports  development  and  description  of robot software. 

  • 49.
    Hellström, Thomas
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Ringdahl, Ola
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    A software framework for agricultural and forestry robots2013Ingår i: Industrial robot, ISSN 0143-991X, E-ISSN 1758-5791, Vol. 40, nr 1, s. 20-26Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Purpose: The purpose of this paper is to describe a generic software framework for development of agricultural and forestry robots. The primary goal is to provide generic high-level functionality and to encourage distributed and structured programming, thus leading to faster and simplified development of robots. A secondary goal is to investigate the value of several architecture views when describing different software aspects of a robotics system.

    Design/methodology/approach: The framework is constructed with a hybrid robot architecture, with a static state machine that implements a flow diagram describing each specific robot. Furthermore, generic modules for GUI, resource management, performance monitoring, and error handling are included. The framework is described with logical, development, process, and physical architecture views.

    Findings: The multiple architecture views provide complementary information that is valuable both during and after the design phase. The framework has been shown to be efficient and time saving when integrating work by several partners in several robotics projects. Although the framework is guided by the specific needs of harvesting agricultural robots, the result is believed to be of general value for development also of other types of robots.

    Originality/value: In this paper, the authors present a novel generic framework for development of agricultural and forestry robots. The robot architecture uses a state machine as replacement for the planner commonly found in other hybrid architectures. The framework is described with multiple architecture views.

  • 50.
    Hellström, Thomas
    et al.
    Umeå universitet.
    Ringdahl, Ola
    Umeå universitet.
    Autonomous Path Tracking Using Recorded Orientation and Steering Commands2005Ingår i: Proceedings of Towards Autonomous Robotic Systems 2005 (TAROS05), 2005Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper describes a novel algorithm, Follow-the-Past, for autonomous path-tracking vehicles. Common algorithms, like Pure Pursuit and Follow the Carrot, compute steering commands that make a vehicle follow approximately a predefined path. One problem with these algorithms is that they tend to cut corners, since they do not explicitly take into account the actual curvature of the path. The method presented in this paper uses recorded orientation and steering commands to overcome this problem. The algorithm is constructed within the reactive paradigm, common in modern robotics, and is divided into three separate behaviors, each responsible for one aspect of the path-tracking task. We present results from both a simulator for autonomous forest machines and experiments with a physical robot. The results are compared with the Pure-Pursuit and the Follow-the-Carrot algorithms, and show a significant improvement in performance.

12 1 - 50 av 74
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