The Optimal Global Path Planning of Mobile Robot Based on Improved Hybrid Adaptive Genetic Algorithm in Different Tasks and Complex Road Environments

In complex environments, mobile robots performing tasks with different hazard levels need to consider different road factors, this paper proposes a functional model correlating task hazard levels with road factors, proposing an innovative Hybrid Adaptive Genetic Algorithm (HAGA). The HAGA integrates an optimized two-optimization (2-opt) operator* with an enhanced Adaptive Genetic Algorithm (AGA) for efficient path planning in diverse tasks and complex road conditions. Firstly, pre-optimize the initial paths is performed by introducing a new domain knowledge-based operator that duplicates paths in the path are deleted to avoid the redundant paths, and then they are divided into the TOP layer and the ordinary layer, the TOP layer is optimized by using the adaptive 2-opt* operator that satisfies the hyperbolic tangent function (TANH), and the crossover and variability of the ordinary layer are optimized by using the S-type function (Sigmoid function, Sigmoid) and TANH AGA for the optimization treatment for the crossover and variance of the ordinary layer, respectively, to establish a robot path planning algorithm suitable for multitasking in complex environments. The experiment proves that the improved HAGA has strong global search ability and also improves the local search ability, has good generality and robustness, and reduces the optimal path distance by a minimum of 2.74% and a maximum of 10.86% compared with the comparison algorithm in the experiment. The experimental results showed that the method enables mobile robots to perform tasks with different hazard levels in complex environments with good generalization and robustness, and the coefficient of variation (CV) of repeated experiments at five safety levels was kept within 2 %. which had good universality and versatility. This research has the potential to enhance the safety and efficiency of mobile robot operations in challenging environments.