The Multi-Dimensional Actions Control Approach for Obstacle Avoidance Based on Reinforcement Learning

Abstract

In robotics, obstacle avoidance is an essential ability for distance sensor-based robots. This type of robot has axisymmetrically distributed distance sensors to acquire obstacle distance, so the state is symmetrical. Training the control policy with a reinforcement learning method is a trend. Considering the complexity of environments, such as narrow paths and right-angle turns, robots will have a better ability if the control policy can control the steering direction and speed simultaneously. This paper proposes the multi-dimensional action control (MDAC) approach based on a reinforcement learning technique, which can be used in multiple continuous action space tasks. It adopts a hierarchical structure, which has high and low-level modules. Low-level policies output concrete actions and the high-level policy determines when to invoke low-level modules according to the environment’s features. We design robot navigation experiments with continuous action spaces to test the method’s performance. It is an end-to-end approach and can solve complex obstacle avoidance tasks in navigation.

Building a Connected Communication Network for UAV Clusters Using DE-MADDPG

“…Recently, deep reinforcement learning (DRL) methods have been proposed to solve extremely high complexity problems which are not often solvable using traditional control methods [8]. The DRL provides new ideas for solving the problems of large-scale timevarying systems [9].…”