| 31. |
- Lundh, Robert, et al.
(författare)
-
Can Emil help Pippi?
- 2005
-
Konferensbidrag (refereegranskat)abstract
- This work is about the use of artificial intelligence (AI) planning techniques to automatically configure a group of cooperating robots. In particular, we study societies of autonomous robotic systems in which robots can help each other by offering information-producing resources and functionalities. A configuration in our societies is a way to allocate and connect functionalities among robots. In general, different configurations can be used to solve the same task, depending on the current situation. In this paper, we show a general approach to define, generate, and execute configurations. We use knowledge-based planning to automatically generate a configuration for a given task, environment, and set of resources. We describe an experimental system where these ideas are implemented, and show an example of it in which two robots mutually help each other to cross a door.
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| 32. |
- Lundh, Robert, et al.
(författare)
-
Dynamic configuration of a team of robots
- 2004
-
Ingår i: Proceedings of the ECAI-04 workshop on agents in dynamic and real-time environments. ; , s. 57-62
-
Konferensbidrag (refereegranskat)abstract
- We study teams of autonomous robotic agents in which agents can help each other by offering information-producing resources and functionalities. Depending on the current situation and tasks, the team may need to change its functional configuration, that is, which agents provide which functionalities to whom. We propose to use knowledge-based techniques to automatically synthesize new team configurations in response to changes in the situation or tasks. This note summarizes our approach, and reports our preliminary steps in this direction.
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| 33. |
- Lundh, Robert, 1978-
(författare)
-
Plan-Based Configuration of a Group of Robots
- 2006
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Imagine the following situation. You give your favorite robot, named Pippi, the task to fetch a parcel that just arrived at your front door. While pushing the parcel back to you, she must travel through a door opening. Unfortunately, the parcel she is pushing is blocking her camera, giving her a hard time to see the door to cross. If she cannot see the door, she cannot safely push the parcel through the door opening. What would you as a human do in a similar situation? Most probably you would ask someone for help, someone to guide you through the door, as we ask for help then we need to park our car in a tight parking spot. Why not let the robots do the same? Why not let robots help each other. Luckily for Pippi, there is another robot, named Emil, vacuum cleaning the floor in the same room. Since Emil can view both Pippi and the door at the same time, he can guide pippi through the door, enabling her to deliver the parcel to you. This work is about societies of autonomous robots in which robots can help each other by offering information-producing functionalities. A functional configuration is a way to allocate and connect functionalities among robots. In general, different configurations can be used to solve the same task, depending on the current situation. For the work on configurations, we have three steps. The first step is to formally define the idea of functional configuration. Second, to show how configurations can be automatically generated and executed. The third step is to address the problem of when and how to change a configuration in response to changing conditions. In this licenciate thesis we report initial work that focus on the two first steps: the third step is subject of future work. We propose a formal definition of functional configurations, and we propose an approach based on artificial intelligence (AI) planning techniques to automatically generate a preferred configuration for a given task, environment, and set of resources. To illustrate these ideas, we describe an experimental system where these are implemented, and show two example of it in which two robots mutually help each other to address tasks. In the first example they help each other to cross a door, and in the second example they carry a bar together.
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| 34. |
- Lundh, Robert, et al.
(författare)
-
Plan-based configuration of a group of robots
- 2006
-
Ingår i: 17th European Conference on Artificial Intelligence (ECAI). - : IOS Press. - 1586036424 ; , s. 683-692
-
Konferensbidrag (refereegranskat)abstract
- We consider groups of autonomous robots in which robots can help each other by offering information-producing functionalities. A functional configuration} is a way to allocate and connect functionalities among robots. In general, different configurations can be used to solve the same task, depending on the current situation. In this paper, we define the idea of functional configuration, and we propose a plan-based approach to automatically generate a preferred configuration for a given task, environment, and set of resources. To illustrate these ideas, we show a simple experiment in which two robots mutually help each-other to cross a door.
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| 35. |
- Lundh, Robert, et al.
(författare)
-
Plan-based configuration of an ecology of robots
- 2007
-
Ingår i: 2007 IEEE international conference on robotics and automation. - 1424406013 ; , s. 64-70
-
Konferensbidrag (refereegranskat)abstract
- We consider an ecology of robots in which robots can help each other by offering information-producing functionalities. A functional configuration of this ecology is a way to allocate and connect functionalities among the participating robots. In general, different configurations can be used to solve the same task, depending on the current situation, and some tasks require sequences of different configurations to be solved. In this paper, we propose a plan-based approach to automatically generate a preferred configuration for a given task, environment, and set of resources. We also describe how our configuration planner can be integrated with an action planner to deal with tasks that require sequences of configurations. We illustrate these ideas on a specific instance of an ecology of robots, called a PEIS-Ecology. We also show an experiment run on our PEIS Ecology testbed, in which a sequence of configurations for an olfactory task is automatically generated and executed
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| 36. |
- Lundh, Robert, 1978-
(författare)
-
Robots that help each other : self-configuration of eistributed robot systems
- 2009
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Imagine the following situation. You give your favorite robot, named Pippi, the task to fetch a heavy parcel that just arrived at your front door. While pushing the parcel back to you, she must travel through a door. Unfortunately, the parcel she is pushing is blocking her camera, giving her a hard time to see the door. If she cannot see the door, she cannot safely push the parcel through it. What would you as a human do in a similar situation? Most probably you would ask someone for help, someone to guide you through the door, as we ask for help when we need to park our car in a tight parking spot. Why not let the robots do the same? Why not let robots help each other? Luckily for Pippi, there is another robot, named Emil, vacuum cleaning the floor in the same room. Since Emil has a video camera and can view both Pippi and the door at the same time, he can estimate Pippi's position relative to the door and use this information to guide Pippi through the door by wireless communication. In that way he can enable Pippi to deliver the parcel to you. The goal of this thesis is to endow robots with the ability to help each other in a similar way. More specifically, we consider distributed robot systems in which: (1) each robot includes modular functionalities for sensing, acting and/or processing; and (2) robots can help each other by offering those functionalities. A functional configuration of such a system is any way to allocate and connect functionalities configuration among the robots. An interesting feature of a system of this type is the possibility to use different functional configurations to make the same set of robots perform different tasks, or to perform the same task under different conditions. In the above example, Emil is offering a perceptual functionality to Pippi. In a different situation, Emil could offer his motion functionality to help Pippi push a heavier parcel. In this thesis, we propose an approach to automatically generate, at run time, a functional configuration of a distributed robot system to perform a given task in a given environment, and to dynamically change this configuration in response to failures. Our approach is based on artificial intelligence planning techniques, and it is provably sound, complete and optimal. In order to handle tasks that require more than one step (i.e., one configuration) to be accomplished, we also show how methods for automatic configuration can be integrated with methods for task planning to produce a complete plan were each step is a configuration. For the scenario above, generating a complete plan before the execution starts enables Pippi to know before hand if she will be able to get the parcel or not. We also propose an approach to merge configurations, which enables concurrent execution of configurations, thus reducing execution time. We demonstrate the applicability of our approach on a specific type of distributed robot system, called Peis-Ecology, and show experiments in which configurations and sequences of configurations are automatically generated and executed on real robots. Further, we give an experiment where merged configurations are created and executed on simulated robots.
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| 37. |
- Pettersson, Ola, et al.
(författare)
-
Model-free execution monitoring by learning from simulation
- 2005
-
Ingår i: 2005 IEEE International Symposium on Computational Intelligence in Robotics and Automation. - 0780393554 ; , s. 505-511
-
Konferensbidrag (refereegranskat)abstract
- Autonomous robots need the ability to plan their actions and to execute them robustly and in a safe way in face of a changing and partially unpredictable environment. This is especially important if we want to design autonomous robots that can safely co-habitate with humans. In order to manage this, these robots need the ability to detect when the execution does not proceed as planned, and to correctly identify the causes of the failure. An execution monitoring system is a system that allows the robot to detect and classify these failures. In this work we show that pattern recognition techniques can be applied to realize execution monitoring by classifying observed behavioral patterns into normal or faulty behaviors. The approach has been successfully tested on a real robot navigating in an office environment. Interesting, these tests show that we can train an execution monitor in simulation, and then use it in a real robot.
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| 38. |
- Pettersson, Ola, et al.
(författare)
-
Model-free execution monitoring in behavior-based mobile robotics
- 2003
-
Konferensbidrag (refereegranskat)abstract
- In this paper we present a model-free execution monitor for behavior-based mobile robots. By model-free we mean that the monitoring is based only on the actual execution, without involving any predictive models of the controlled system. Model-free monitors are especially suitable for systems where it is hard to obtain adequate models. In our approach we analyze the activation levels of the different behaviors using a pattern recognition technique. Our model-free execution monitor, which is realized by radial basis function networks, is shown to give a high performance in several realistic simulations.
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| 39. |
- Pettersson, Ola, 1972-
(författare)
-
Model-free execution monitoring in behavior-based mobile robotics
- 2004
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In the near future, autonomous mobile robots are expected to assist us by performing service tasks in many different areas, including transportation, cleaning, mining, or agriculture. In order to manage these tasks in a changing and partially unpredictable environment, without the help of humans, the robot must have the ability to plan its actions and to execute them robustly and in a safe way. Since the real world is dynamic and not fully predictable, the robot must also have the ability to detect when the execution does not proceed as planned, and to correctly identify the causes of the failure. An execution monitoring system is a system that allows the robot to detect and classify these failures. Most current approaches to execution monitoring in robotics are based on the idea of predicting the outcomes of the robot's actions by using some sort of model, and comparing the predicted outcomes with the observed ones. In contrary, this thesis explores the use of model-free approaches to execution monitoring, that is, approaches that do not use predictive models. These approaches solely observe the actual execution of the robot, and detect certain patterns that indicate a problem. In this thesis, we show that pattern recognition techniques can be applied to realize model-free execution monitoring by classifying observed behavioral patterns into normal or faulty behaviors. We investigate the use of several such techniques, and verify their utility in a number of experiments involving the navigation of a mobile robot in indoor environments. Statistical measures are used to compare the results given from several realistic simulations. Our approach has also been successfully tested on a real robot navigating in an office environment. Interesting, this test has shown that we can train a model-free execution monitor in simulation, and then use it in a real robot.
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| 40. |
- Pettersson, Ola, et al.
(författare)
-
Model-Free Execution Monitoring in Behavior-Based Robotics
- 2007
-
Ingår i: IEEE transactions on systems, man and cybernetics. Part B. Cybernetics. - New York, NY : Institute of Electrical and Electronics Engineers. - 1083-4419 .- 1941-0492. ; 37:4, s. 890-901
-
Tidskriftsartikel (refereegranskat)abstract
- In the near future, autonomous mobile robots are expected to help humans by performing service tasks in many different areas, including personal assistance, transportation, cleaning, mining, or agriculture. In order to manage these tasks in a changing and partially unpredictable environment without the aid of humans, the robot must have the ability to plan its actions and to execute them robustly and safely. The robot must also have the ability to detect when the execution does not proceed as planned and to correctly identify the causes of the failure. An execution monitoring system allows the robot to detect and classify these failures. Most current approaches to execution monitoring in robotics are based on the idea of predicting the outcomes of the robot’s actions by using some sort of predictive model and comparing the predicted outcomes with the observed ones. In contrary, this paper explores the use of model-free approaches to execution monitoring, that is, approaches that do not use predictive models. In this paper, we show that pattern recognition techniques can be applied to realize model-free execution monitoring by classifying observed behavioral patterns into normal or faulty execution. We investigate the use of several such techniques and verify their utility in a number of experiments involving the navigation of a mobile robot in indoor environments.
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