Posture Optimization Strategy for a Statically Stable Robot Traversing Rough Terrain

This paper presents a posture optimization algorithm for a six-legged walking robot. During walking on rough terrain and planning its motion the robot has to determine the horizontal position, distance to the ground, and inclination of the platform. The proposed posture optimization algorithm is based on the Particle Swarm Optimization method. The algorithm increases the stability margin and maximizes the possible motion range of the robot (by maximizing the kinematic margin of each leg). The computation of the kinematic margin is performed by using an analytical function obtained with the Gaussian approximation. The Gaussian-based approximation significantly decreases the time consumed by the algorithm and allows to implement the posture optimization procedure on the real robot. The posture optimization is used as a part of the RRT-based motion planer to find a full-body path while climbing the obstacles.