CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
A multivalued logic approach to integrating planning and control
Artificial Intelligence - Special volume on planning and scheduling
RoboCop: today and tomorrow-what we have learned
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An Behavior-based Robotics
An architecture to implement agents co-operating in dynamic environments
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Anchoring Symbols to Sensor Data: Preliminary Report
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
An architecture to coordinate fuzzy behaviors to control an autonomous robot
Fuzzy Sets and Systems - Special issue: Fuzzy set techniques for intelligent robotic systems
Qualitative kinematics of planar robots: Intelligent connection
International Journal of Approximate Reasoning
Fuzzy uncertainty modeling for grid based localization of mobile robots
International Journal of Approximate Reasoning
Intelligent decision-making system for autonomous robots
International Journal of Applied Mathematics and Computer Science
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In this paper, we propose a modeling paradigm that uses fuzzy sets to represent concepts on which control modules of a behavior-based autonomous robot operate. The primitives defined in the modeling paradigm are expressive enough to represent the knowledge needed by planning, coordination, and reactive control of a multi-robot control system. At the same time, it provides a well-founded tool to represent in a compact way the data interpretations needed to reason effectively about what is happening in the world and what is desired to happen. This modeling paradigm makes the design of behavior, planning, and coordination modules easy, since its primitives are simple and expressive. Moreover, it provides a sound framework to deal with uncertainty in sensing and world modeling.