Collision avoidance planning method for mobile robot based on deep reinforcement learning in dynamic environment

Abstract

The invention discloses a mobile robot collision avoidance planning method based on deep reinforcement learning in a dynamic environment, and belongs to the technical field of mobile robot navigation. The invention collects original data through a laser rangefinder, processes the original data as the input of a neural network, and establishes an LSTM neural network. Through the A3C algorithm, the neural network outputs corresponding parameters, and obtains the action of each step of the robot after processing. The present invention does not need to model the environment, and is more suitable for environments with unknown obstacles. It adopts the actor-critic framework and time difference algorithm, realizes low variance and is more suitable for continuous action spaces, and realizes the effect of learning while training. Design a continuous action space with a heading angle limit, and use 4 threads for parallel learning and training. Compared with the general deep reinforcement learning method, it greatly improves the learning and training time, reduces sample correlation, and ensures the high utilization of the exploration space and exploration strategies. Diversity, thereby improving algorithm convergence, stability and success rate of obstacle avoidance.

Description

technical field [0001] The invention belongs to the technical field of mobile robot navigation, in particular to a collision avoidance planning method for mobile robots based on deep reinforcement learning in a dynamic environment. Background technique [0002] The research of mobile robots began in the 1960s, and so far this field has become one of the important research directions of robotics. With the continuous advancement and development of science and technology, autonomous mobile robots have a high level of intelligence and can complete tasks autonomously without supervision. Therefore, mobile robots have good development space and prospects in the fields of search, detection, fire protection, and reconnaissance. In the above-mentioned special fields, the environment is usually complex and changeable, and there are not only dynamic obstacles but also static obstacles. In order to successfully realize the function of a mobile robot, an appropriate obstacle avoidance a...

AI Technical Summary

Problems solved by technology

Generally speaking, traditional obstacle avoidance methods have relatively large limitations, especially for complex and dynamic unknown environments; while intelligent obstacle avoidance algorithms, especially the popular obstacle avoidance algorithms combined with deep learning and reinforcement learning in recent years, are suitable for continuous High-dimensional complex dynamic unknown environment has great advantages

Images