Research on the Application of the Bionic Ant Path Planning Algorithm in Robot Navigation

Path planning has emerged as a key problem in the area of robot navigation due to the rapid growth of robotics. In order to address the shortcomings of conventional route planning algorithms in dynamic, unstructured settings, this research investigates a path planning technique based on the Ant Colony Optimization (ACO) algorithm. The ACO algorithm simulates the cooperative behaviour and pheromone mechanism of an ant colony foraging, achieving a balance between global optimisation and local search. This paper elaborates on the theoretical basis, path selection rules, pheromone update mechanism and heuristic information design of the bionic ant algorithm, and verifies its effectiveness and superiority in robot navigation through experimental simulation. According to the experimental findings, the bionic ant algorithm has high resilience and practical utility, outperforming conventional algorithms in terms of route length, search efficiency, and adaptability to dynamic situations. The research in this paper provides theoretical support and application reference for solving the problem of robot path planning in complex environments.