Intelligent Obstacle Avoidance Algorithm for Mobile Robots in Uncertain Environment. (30th March 2022)

• Record Type: Journal Article
• Authors: Guan, Liwei; Lu, Yu; He, Zhijie; Chen, Xi
• Other Names: Zhong Shan Academic Editor.
• Is Part Of: Journal of robotics. Volume 2022(2022)
• Publication Date: 2022-03-30
• Subjects: Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1155/2022/8954060
• Languages: English
• ISSNs: 1687-9600

Abstract

Abstract : The application of mobile robots and artificial intelligence technology has shown great application prospects in many fields. The ability of intelligent obstacle avoidance is the basis for the deep application of mobile robots. However, there are often more or less uncertain factors in the actual operating environment of the robot, such as people or objects that are not updated in time or temporarily appear. Therefore, it is an important step to complete the automatic learning of obstacle avoidance for mobile robots. In a nondeterministic environment, a mobile robot intelligent obstacle avoidance algorithm based on an improved fuzzy neural network with self-learning is firstly proposed. The mobile robot intelligent obstacle avoidance system is constructed through the reaction layer, the deliberation layer, and the supervision layer. Through the analysis of sensor performance, model accuracy, path obstacle avoidance optimization, and obstacle avoidance simulation, the following conclusions are drawn. First, through network training, the accuracy rate of the test set is stable at 98%, and the loss of the function value has also been reduced from the original 0.79 to 0.08, which is 10 times smaller. Second, the traditional single sensor cannot meet the obstacle avoidance requirements of robots, and mobile robots must combine multipurpose technology. Third, the algorithm in this paper encounters the following. When there are obstacles, the path is dominated by straight lines, obstacle avoidance planning is optimal, and the distance is shorter. Fourth, the larger N : M, the larger the solution space, indicating that this algorithm gradually improves the search efficiency to the greatest extent and can handle any form of medium and large scale task allocation problem.