Application of an Improved Double Q-Learning Algorithm in Ground Mobile Robots

Since efficient path planning technology is the key to the safe and autonomous navigation of autonomous ground robots, and in the complex and asymmetrically distributed land environment, the existing path planning and obstacle avoidance technologies seem somewhat inadequate. Since efficient path planning technology is key to the safe and autonomous navigation of autonomous ground robots, an advanced double Q-learning algorithm based on self-supervised prediction and curiosity-driven exploration is proposed. The algorithm reduces the risk of overestimation and bootstrapping by adjusting the calculation method of the target Q value and optimizing the network structure. In addition, a priority experience replay is introduced to set the priority for the data in the experience pool, thereby increasing the probability that better data is extracted. Experience pool data with fewer training times can be used more effectively. Adding the curiosity network to the original neural network, each state is given an overall reward when performing diverse actions. This method enhances the exploration of unmanned ground mobile robots and can independently select the shortest path to the endpoint. In complex environments, compared with the Sparrow Search Algorithm, Dung Beetle Optimization Algorithm, and Particle Swarm Optimization Algorithm, the results of the proposed algorithm are reduced by 18.07%, 7.91%, and 5.56%, respectively. Therefore, it could better cope with the challenges brought by complex environments and solve the problem that the algorithm cannot converge in complex environments.