Improved Dynamic Window Approach for Dynamic Obstacle Avoidance of Quadruped Robots

Abstract

This paper studies dynamic obstacle avoidance strategies of quadruped robots in an unknown and dynamically changing environment. Different from classical navigation problems with static environment, an unknown number of moving obstacles with unknown dynamics are present in the scenario considered in this paper. This unknown and rapidly changing environment prevents us from directly applying existing navigation approaches for quadruped robots. To address the additional challenges introduced by the dynamic obstacles, an improved dynamic window approach (DWA) is proposed in which both evaluation function and constraints are modified. To enable real-time implementation of the proposed algorithm, multi-layer techniques for processing camera point cloud data are applied for online extraction of environmental information. The proposed algorithm is tested both in simulation and on hardware with a quadruped robot equipped with a low-cost RGB-D camera. The results show that the proposed algorithm significantly increases the rate of success for avoiding dynamical obstacles and reduces the time spent for reaching the target.