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

Abstract

The invention discloses a collision avoidance planning method for mobile robots based on deep reinforcement learning in a dynamic environment, and belongs to the technical field of mobile robot navigation. The method of the invention includes the following steps of: collecting raw data through a laser rangefinder, processing the raw data as input of a neural network, and building an LSTM neural network; through an A3C algorithm, outputting corresponding parameters by the neural network, and processing the corresponding parameters to obtain the action of each step of the robot. The scheme of the invention does not need to model the environment, is more suitable for an unknown obstacle environment, adopts an actor-critic framework and a temporal difference algorithm, is more suitable for a continuous motion space while realizing low variance, and realizes the effect of learning while training. The scheme of the invention designs the continuous motion space with a heading angle limitationand uses 4 threads for parallel learning and training, so that compared with general deep reinforcement learning methods, the learning and training time is greatly improved, the sample correlation isreduced, the high utilization of exploration spaces and the diversity of exploration strategies are guaranteed, and thus the algorithm convergence, stability and the success rate of obstacle avoidance can be improved.

Description

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

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

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