Improved Crayfish Optimization Algorithm for Mobile Robot Path Planning Optimization

Regarding the issues of non-smooth paths, unstable obstacle avoidance, and getting stuck in local optima in mobile robot path planning, a new Adaptive Crayfish Optimization Algorithm (ACOA) is proposed. The algorithm improves robot path planning. First, a Piecewise Chaotic Map is used to set up the population. It adds diversity and randomness to the population and helps the algorithm search better globally. Finally, a special fitness function is designed. It allows robots to avoid obstacles better and creates smoother paths. Tests show that the improved algorithm works well in different complex environments. It gives shorter paths, faster convergence, and fewer turns. Compared with the COA algorithm, the ACOA algorithm minimizes the path length by 3.9% and 5.3% in the two maps, and the inflection points are reduced by 40% and 33%, respectively. It also smooths It also smooths paths and better avoids obstacles.