Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning to Avoid Moving Obstacles Optimally for Mobile Robots Using a Genetic-Fuzzy Approach
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Effects of turning gait parameters on energy consumption and stability of a six-legged walking robot
Robotics and Autonomous Systems
Expert Systems with Applications: An International Journal
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
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This paper describes a new method for generating the turning-gait of a six-legged robot using a combined genetic algorithm (GA)-Fuzzy approach. The main drawback of the traditional methods of gait generation is their high computational load. Thus, there is still a need for the development of a computationally tractable algorithm that can be implemented online to generate stable gait of a multilegged robot. In the proposed genetic-fuzzy system, the fuzzy logic controllers (FLCs) are used to generate the stable gait of a hexapod and a GA is used to improve the performance of the FLCs. The effectiveness of the proposed algorithm is tested on a number of turning-gait generation problems of a hexapod that involve translation as well as rotation of the vehicle. The hexapod will have to take a sharp circular turn (either clockwise or counter-clockwise) with minimum number of ground legs having the maximum average kinematic margin. Moreover, the stability margin should lie within a certain range to ensure static stability of the vehicle. Each leg of a six-legged robot is controlled by a separate FLC and the performance of the controllers is improved by using a GA. It is to be noted that the actual optimization is done off-line and the hexapod can use these optimized FLCs to navigate in real-world scenarios. As an FLC is computationally less expensive, the proposed algorithm will be faster compared with the traditional methods of gait-generation, which include both graphical as well as analytical methods. The GA-tuned FLCs are found to perform better than the author-defined FLCs.