Improved Genetic Algorithm-Based Obstacle Avoidance Path Planning Method for Inspection Robots

The current conventional genetic algorithm is prone to the problem of local optimum due to the lack of adjustment of parameters when planning the path of inspection robots. In this regard, an improved genetic algorithm-based inspection robot obstacle avoidance path planning method is proposed. The working space type of the inspection robot is analyzed, and the scenario is modeled by combining the spatial interference weights. The traditional genetic algorithm is optimized by introducing variational operators, and finally the robot obstacle avoidance path planning algorithm is solved to obtain the optimal route. In the experiments, the planning performance of the proposed method is verified. The analysis of the experimental results shows that the path distance is small and has a more desirable path planning performance when the proposed method is used to plan the path of the inspection robot.