Swarm-based optimizations in hexapod robot walking

Abstract

During previous research [1-7] and development several hexapod walking robots and its simulation model were built by the authors. The latest model called Szabad(ka)-II is a complex, servo motor driven, multiprocessor device. In parallel with the building of this hexapod robot, a simulation model was also built in order to help optimize the robot's structure, walking and driving [5]. The results of modeling and parameter optimizations can be used as a guideline during the design of a new and improved robot. The Particle Swarm Optimization (PSO) method was chosen because its simplicity and effectiveness [1, 2]. It has produced better and faster results compared to previously used Genetic Algorithm (GA) [3]. However, neither selected method is able to provide the global optimum in the case of one-time run. Using an optimization benchmark disclose the differences and help to get the best parameterized optimization method for a given problem.