Abstract
The purpose of this study is the development of obstacle avoidance algorithm for quadrupedal walking robot in response to environment. Stepping points are generated by using neural network. Training data for neural network are optimum stepping points generated by using the genetic algorithm based on the mechanism of robot model and the obstacle. A large number of training data are required because obstacles have various sizes and shapes. We compare optimum stepping points generated by using the genetic algorithm to make a search for starting position of stride adjustment. Some numerical results of the stride adjustment are shown for several obstacle environments.
