Path planning of inspection robot in complex industrial scene based on improved genetic algorithm and improved TEB algorithm

Aiming at the high risk and low efficiency of traditional artificial inspection methods in the complex industrial scene, this study focuses on the inspection robot and conducts research on path planning through simulation and experiment. Firstly, according to the unstructured characteristics of complex industrial scenes, in order to reduce the loop error, the Lazy Decision algorithm is introduced to improve loop detection of Cartographer algorithm. Secondly, at the level of global path planning, a feasible path objective model under multiple constraints is constructed, and the genetic algorithm is reasonably improved through adaptive adjustment of crossover and mutation probabilities, design of composite selection strategy and node deletion operator, to enhance the efficiency and smoothness of global path planning. Then, at the level of local path planning, in order to improve the safety of robot driving, path point and trajectory pose point constraints are introduced into the TEB algorithm, and an obstacle evaluation sub-function is constructed to optimize its obstacle avoidance performance, achieving the collaborative fusion of global and local path planning. Finally, the simulation and experiment of path planning are carried out. The results show that the optimized Cartographer algorithm has higher mapping accuracy, and the relative errors in the simulation and experimental scenes are reduced by 34.25% and 45.09% respectively, which effectively alleviates the problem of map ghosting. The path length, number of inflection points, and planning time of the improved genetic algorithm are reduced by up to 16.32%, 59.10%, and 42.10%, respectively. The local path planned by the improved TEB algorithm can also follow the global path well and the safety distance from the turning point is increased by 41.37%. The fusion algorithm reduces path length and driving time by 31.91% and 23.76% respectively, balancing the safety and efficiency of path planning for the inspection robot.