A methodology for integrating multiple perceptual algorithms within a reactive robotic control system is presented. A model using finite state acceptors is developed as a means for expressing perceptual processing ove...
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A methodology for integrating multiple perceptual algorithms within a reactive robotic control system is presented. A model using finite state acceptors is developed as a means for expressing perceptual processing over space and time in the context of a particular motor behavior. This model can be utilized for a wide range of perceptual sequencing problems. The feasibility of this method is demonstrated in two separate implementations. The first is in the context of mobilerobot docking where our mobilerobot uses four different vision and ultrasonic algorithms to position itself relative to a docking workstation over a long-range course. The second uses vision, IR beacon, and ultrasonic algorithms to park the robot next to a desired plastic pole randomly placed within an arena.
Multiagent robotics affords the opportunity to solve problems more efficiently and effectively than any single agent could achieve. Nonetheless, communication bottle-necks between agents pose potentially serious drawb...
Multiagent robotics affords the opportunity to solve problems more efficiently and effectively than any single agent could achieve. Nonetheless, communication bottle-necks between agents pose potentially serious drawbacks in coordinated behavior. In this research, we demonstrate the efficiency of multiagent schema-based navigation for object retrieval. Primitive motor behaviors are specified for each of the individual robotic agents, which produce safe task-achieving action in an unstructured environment. When implemented over several identical units, retrieval is facilitated in the absence of interagent communication as evidenced by spontaneous recruitment of several agents to accomplish a task. Simulation results are provided to demonstrate these effects. Extensions to other task-achieving behaviors are also feasible.
Autonomous teams of robots operating in a dynamic, adversarial environment stand to benefit from using all available resources. But how can knowledge be used to construct a plan that does not interfere with the robots...
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Autonomous teams of robots operating in a dynamic, adversarial environment stand to benefit from using all available resources. But how can knowledge be used to construct a plan that does not interfere with the robots ability to react to its environment? In this research we distill abstract representation into a plan usable by a reactive behavior-based architecture. This plan is then exploited to enhance the performance of a team of robots tasked with maintaining communications while performing reconnaissance. Utilizing multiple plans in serial and in parallel is shown via simulation to be a promising method for increasing mission performance. We conclude that the utility of these internalized plans warrants further investigation as a method for imbuing reactive agents with a priori knowledge.
Behavior-based robotic systems have been successfully deployed for a wide range of planar ground-based tasks. This article describes how reactive (behavior-based) systems can be applied to new and more difficult targe...
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In this paper, the effects of adaptive robotic behavior via Learning Momentum in the context of a robotic team are studied. Learning momentum is a variation on parametric adjustment methods that has previously been su...
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In this paper, the effects of adaptive robotic behavior via Learning Momentum in the context of a robotic team are studied. Learning momentum is a variation on parametric adjustment methods that has previously been successfully applied to enhance individual robot performance. In particular, we now assess, via simulation, the potential advantages of a team of robots using this capability to alter behavioral parameters when compared to a similar team of robots with static parameters.
This paper presents an approach to learning an optimal behavioral parameterization in the framework of a Case-Based Reasoning methodology for autonomous navigation tasks. It is based on our previous work on a behavior...
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This paper presents an approach to learning an optimal behavioral parameterization in the framework of a Case-Based Reasoning methodology for autonomous navigation tasks. It is based on our previous work on a behavior-based robotic system that also employed spatio-temporal case-based reasoning [3] in the selection of behavioral parameters but was not capable of learning new parameterizations. The present method extends the case-based reasoning module by making it capable of learning new and optimizing the existing cases where each case is a set of behavioral parameters. The learning process can either be a separate training process or be part of the mission execution. In either case, the robot learns an optimal parameterization of its behavior for different environments it encounters. The goal of this research is not only to automatically optimize the performance of the robot but also to avoid the manual configuration of behavioral parameters and the initial configuration of a case library, both of which require the user to possess good knowledge of robot behavior and the performance of numerous experiments. The presented method was integrated within a hybrid robot architecture and evaluated in extensive computer simulations, showing a significant increase in the performance over a non-adaptive system and a performance comparable to a non-learning CBR system that uses a hand-coded case library.
This paper presents a method for a mobilerobot to construct and localize relative to a "cognitive map", where the cognitive map is assumed to be a representational structure that encodes both spatial and be...
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This paper presents a method for a mobilerobot to construct and localize relative to a "cognitive map", where the cognitive map is assumed to be a representational structure that encodes both spatial and behavioral information. The localization is performed by applying a generic Bayes filter. The cognitive map was implemented within a behavior-based robotic system, providing a new behavior that allows the robot to anticipate future events using the cognitive map. One of the prominent advantages of this approach is elimination of the pose sensor usage (e.g., shaft encoder, compass, GPS, etc.), which is known for its limitations and proneness to various errors. A preliminary experiment was conducted in simulation and its promising results are discussed.
In this paper, we present successful strategies for forgetting cases in a Case-Based Reasoning (CBR) system applied to autonomous robot navigation. This extends previous work that involved a CBR architecture which ind...
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ISBN:
(纸本)0780384636
In this paper, we present successful strategies for forgetting cases in a Case-Based Reasoning (CBR) system applied to autonomous robot navigation. This extends previous work that involved a CBR architecture which indexes cases by the spatio-temporal characteristics of the sensor data, and outputs or selects parameters of behaviors in a behavior-based robot architecture. In such a system, the removal of cases can be applied when a new situation unlike any current case in the library is encountered, but the library is full. Various strategies of determining which cases to remove are proposed, including metrics such as how frequently a case is used and a novel spreading activation mechanism. Experimental results show that such mechanisms can increase the performance of the system significantly and allow it to essentially forget old environments in which it was trained in favor of new environments it is currently encountering. The performance of this new system is better than both a purely reactive behavior-based system as well as the CBR module that did not forget cases. Furthermore, such forgetting mechanisms can be useful even when there is no major environmental shift during training, since some cases can potentially be harmful or rarely used. The relationship between the forgetting mechanism and the case library size is also discussed.
The design and implementation of mood as an affective component for robotic behavior is described in the context of the TAME framework - a comprehensive, time-varying affective model for robotic behavior that encompas...
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A range of issues is addressed regarding human interaction with autonomous robots, noting the lack of a coherent model to incorporate time varying and local/global effects on behavior. In particular, an experimental s...
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A range of issues is addressed regarding human interaction with autonomous robots, noting the lack of a coherent model to incorporate time varying and local/global effects on behavior. In particular, an experimental study to identify relevant affective phenomena that may increase ease and pleasantness of such interaction is discussed, and a novel framework, TAME, for integrating a variety of these phenomena into behavior-based robotic systems is described.
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