Autonomous Obstacle Avoidance by Learning Policies for Reference Modification

The key problem for autonomous robots is how to navigate through complex, obstacle-filled environments, without colliding into obstacles. The navigation problem is split into generating a global reference path to the goal and then using a local planner to track the reference path and avoid obstacles by generating velocity and steering references for the robot control system to execute. This paper presents a hybrid local planning architecture that combines a classic path following algorithm with a deep reinforcement learning agent. The novel "modification planner" uses the path follower to track the global plan and the deep reinforcement learning agent to modify the references generated by the path follower to avoid obstacles. Importantly, our architecture does not require an updated obstacle map and only 10 laser range finders to avoid obstacles. The modification planner is evaluated in the context of F1/10th autonomous racing and compared to a mapless navigation baseline, the Follow the Gap Method and an optimisation based planner. The results show that the modification planner can achieve faster average times compared to the baseline mapless navigation planner and a 94% success rate which is similar to the baseline.