Behavioral dimension of multiobjective behavior coordination for a mobile robot

Kubota, Naoyuki; Masuta, Hiroyuki; Taniguchi, Koichi

doi:10.1109/cira.2001.1013228

Cited by 13 publications

(7 citation statements)

References 5 publications

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“…It favors those behaviors, which contribute to the ongoing goal. Hence, a behavior coordinator must consider past and present states of the system so that the behavioral activities are controlled in a consistent manner while taking into account the present context of the world [13,16,22,24,34,35,36,39,42,60,61,62,63].…”

Section: Problem Iii: Decision Making and Analysismentioning

confidence: 99%

“…• With/without memory: When a coordination mechanism employs previously calculated activation levels of the behaviors (in addition to the currently calculated activation levels) [34,35,42,44,60,62,63], the method is characterized as with memory. If the system does not preserve previous activation states [11,14,18,23,33,37,38,40,52,55,66,68,77], it is characterized as without memory.…”

Section: The Proposed Classificationmentioning

confidence: 99%

“…Finally, the top-most behavior generates the motion commands. Single level approaches [11,13,14,34,37,38,40,42,55,61,62,63,66,77] do not utilize a layered behavioral decomposition; instead prior knowledge and current sensory feedback are directly used to select a subset of primitive behaviors.…”

Section: The Proposed Classificationmentioning

confidence: 99%

“…• Context-rule and crisp /fuzzy logic: The context-rule based approaches [13,14,18,22,62,65,68] employ a predefined rule-base that processes the sensory feedback and prior knowledge of the system to select a relevant group of behaviors with respect to the current world state. The context rules are constructed using either crisp logic or fuzzy logic.…”

Section: The Proposed Classificationmentioning

confidence: 99%

“…Finally, a defuzzification technique is employed to determine the actuator signal. In the superposition-based techniques [42,40,62,78], actions generated by different behaviors are linearly combined to produce the motor command of the robot.…”

Section: Introductionmentioning

confidence: 99%

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Behavior-based Robot Control Using Fuzzy Discrete Event System

Huq

Mann

Gosine

2006

2006 IEEE International Conference on Fuzzy Systems

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The key issue associated with behavior-based architecture is the development of a behavior coordinator. A behavior coordinator should possess a number of important properties. The literature suggests that (a) both behavior arbitration and command fusion techniques should be combined in order to coordinate competitive and cooperative behaviors, (b) the coordinator should possess adequate means of modeling the current state of the world, (c) sensory uncertainties should be modeled using multivalued logic, (d) the coordinator should be capable of making persistent behavior selection, (e) it should provide the opportunity to accommodate hierarchical decisionmaking for reactive action generation, (f) it should possess predictive decision-making capability for handling future environmental uncertainties, (g) it should provide satisfactory means of decision analysis, and finally (h) it should be modular to achieve robustness with a larger number of behaviors. This thesis attempts to address some of the issues mentioned above while exploiting fuzzy logic (FL) based methodologies for behavior coordination.The initial investigation includes experimentation of different methodologies in the literature. A FL-based controller is designed for motor schema based mobile robot navigation. Fuzzy meta rules have been used to adaptively generate weights for each motor schema. It uses human reasoning to model the deterministic uncertainty of sensory data, which in turn helps to reduce the possibility of generating incorrect weights for motor schemas. Experimental results demonstrate that the fuzzy logic based approach overcomes some of the common problems of schema-based navigation.It has been observed that a pure FL-based method has several disadvantages, such as it imposes a scalability problem when the system consists of both competitive and cooperative behaviors and it does not provide any feedback measures for decision analysis. Moreover, the rule-based knowledge representation becomes complex in cases where previous state information is incorporated for persistent decision-making.While understanding the shortcomings of each technique, this thesis present a novel behavior coordination architecture using Fuzzy Discrete Event System (FDES).This architecture addresses the shortcomings of the FL-based technique using complimentary properties of Discrete Event System (DES). The DES-based method provides supervisory control techniques for behavior arbitration and has a suitable framework for decision analysis in terms of observability and controllability. Furthermore, it supports multi-level behavioral decomposition and uses previous state information of the system for persistent decision-making. As a result, the combination of FL and DES provides the opportunity to integrate several key features to achieve a high performance behavior coordinator.Finally the proposed architecture is experimentally tested using three different robotic applications, namely robotic navigation, robotic box pulling and robotic visual attention. Th...

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Section: Problem Iii: Decision Making and Analysismentioning

confidence: 99%

Section: The Proposed Classificationmentioning

confidence: 99%

Section: The Proposed Classificationmentioning

confidence: 99%

Section: The Proposed Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Behavior-based Robot Control Using Fuzzy Discrete Event System

Huq

Mann

Gosine

2006

2006 IEEE International Conference on Fuzzy Systems

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A Spatial Attention-Based Sensory Network for Fuzzy Controller of Mobile Robot in Dynamic Environments

Shoji

Oshio

Hong

et al. 2022

2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Perceptual system and action system of a mobile robot with structured intelligence

Kubota¹,

Masuta²,

Kojima

et al.

2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proc

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This paper proposes a controlling method of mobile robot with structured intelligence. Modular neural networks are applied for action control based on perceiving-acting cycle of ecological psychology. In the proposed method, the perceptual system and action system restrict each other. Next, we conduct several experiments using a mobile robot we developed. The experimental results show the robot can learn actions based on the perceiving-acting cycle. Finally, we discuss the unit of the action using modular neural networks for robotic systems.

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Behavioral dimension of multiobjective behavior coordination for a mobile robot

Cited by 13 publications

References 5 publications

Behavior-based Robot Control Using Fuzzy Discrete Event System

Behavior-based Robot Control Using Fuzzy Discrete Event System

A Spatial Attention-Based Sensory Network for Fuzzy Controller of Mobile Robot in Dynamic Environments

Perceptual system and action system of a mobile robot with structured intelligence

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