A Path-Planning Method for Wall Surface Inspection Robot Based on Improved Genetic Algorithm

• Published in: Electronics (Basel) Vol. 11; no. 8; p. 1192
• Main Authors: Tao, Yong, Wen, Yufang, Gao, He, Wang, Tianmiao, Wan, Jiahao, Lan, Jiangbo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2022
• Subjects: Convergence; Genetic algorithms; Global positioning systems; GPS; Inertial platforms; Inspection; Mutation; Path planning; Planning; Robots; Signal processing; Signal strength
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics11081192

A wall surface inspection robot mainly relies on the inertial measurement unit and global positioning system (GPS) signal during intelligent wall surface inspection. The robot may encounter incorrect positioning under a GPS-denied environment, easily triggering safety accidents. In order to obtain a path suitable for the safe work of the robot wall surface inspection robot in a GPS-denied environment, a global path-planning method for wall surface inspection robots was proposed based on the improved generic algorithm (GA). The influencing factor for GPS signal strength was introduced into the heuristic function in path planning for GA to address the aforementioned problem. Using the PSO algorithm, GA was initialized and the influencing term of GPS signal was introduced into the fitness degree function so as to achieve point-to-point path planning of vertical wall surface inspection robot. Path angle and probability of intersection and variation was taken into account for better path planning capability. Finally, the simulation experiments were performed. The generated path using the improved GA was found to avoid the blind area of the GPS signal. The algorithm proposed has a good performance with average convergence times of 35.9 times and an angle of 55.88° in simple environment. Contrary to the traditional GA and PSO algorithm, the method showed advantages in terms of the convergence rate, path quality, path angle change, and algorithm stability. The research presented in this article is meaningful and relatively sufficient. The simulation test is also quite convincing. The proposed method was proved to be effective in global path planning for a wall surface inspection robot.