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Evolutionary Fuzzy Control and Navigation for Two Wheeled Robots Cooperatively Carrying an Object in Unknown
IEEE Transactions on Cybernetics
|November 15, 2014
Summary
This study introduces a novel method for two-wheeled robots to navigate unknown areas cooperatively. An adaptive optimization algorithm automatically designs fuzzy controllers for synchronized obstacle avoidance and target seeking, enhancing robotic navigation efficiency.
Area of Science:
- Robotics
- Artificial Intelligence
- Control Systems
Background:
- Cooperative navigation in unknown environments presents challenges for multi-robot systems.
- Manual design of controllers for complex robotic behaviors is time-consuming and suboptimal.
Purpose of the Study:
- To develop an automated method for cooperative navigation of two-wheeled mobile robots carrying an object.
- To enable robots to perform obstacle boundary following and target seeking autonomously.
Main Methods:
- Utilized an adaptive fusion of continuous ant colony optimization and particle swarm optimization (AF-CACPSO) to learn fuzzy controller (FC) rules.
- Implemented cooperative obstacle boundary following (OBF) and target seeking (TS) strategies for leader-follower robot pairs.
- Introduced a cooperative behavior supervisor to dynamically select between OBF and TS modes.
Main Results:
- AF-CACPSO effectively learned fuzzy control rules for single-robot OBF and cooperative OBF.
- A coordination rule was developed for cooperative target seeking, enhancing multi-robot coordination.
- Simulations and experiments demonstrated the approach's effectiveness in cooperative navigation tasks.
Conclusions:
- The proposed AF-CACPSO-based evolutionary fuzzy control approach automates controller design for cooperative robot navigation.
- The method enables robust cooperative navigation, handling both obstacle avoidance and goal-directed movement in unknown environments.
- This research contributes to advancing autonomous multi-robot systems capable of complex collaborative tasks.
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