Path Planning Optimization of Logistics Mobile Robot Based on Improved Adaptive Ant Colony Algorithm

In order to address the problems of slow convergence speed, easy local optima trapping, and many turning points in traditional ant colony algorithms for logistics robot path planning in two-dimensional grid environments, an improved adaptive ant colony algorithm was designed. To accelerate the convergence speed of the algorithm during logistics robot path planning, an artificial potential field attraction was introduced into the state transition probability function. An adaptive pheromone increment was designed to adjust the pheromone concentration update function to enhance the algorithm's optimization capability. The triangle pruning method was used to perform secondary planning on the path, reducing redundant paths and turning points and improving path quality. To verify the effectiveness of the algorithm, global simulation experiments were carried out in grid environments of different complexities, and the performance of the algorithm was analyzed. The simulation results show that the improved adaptive ant colony algorithm has a good global search capability, validating the effectiveness of the algorithm for logistics robot path planning.