A Multi-objective Path Planning Algorithm Based on a A* and Ant Colony

To solve the problems of target conflicts and difficulty in dynamic obstacle avoidance in multi-objective path planning, this paper proposes a hybrid optimization scheme combining the A* algorithm and ant colony optimization (ACO). First, the A* algorithm quickly generates an initial feasible path from the start to the end point, and this path enhances the pheromone distribution of ACO to guide optimization. If the optimized path length is reduced by more than 5%, the optimized result is adopted; otherwise, the original A* path is retained. When the A* algorithm has no solution, ACO performs global path planning to ensure effectiveness and optimality. Experimental results show that in complex scenarios with static, dynamic obstacles and multiple target points, the hybrid algorithm achieves efficient, safe, and dynamically responsive multi-objective dynamic path planning, with an obstacle avoidance success rate of 94%. It significantly improves comprehensive performance and robustness, balancing computational efficiency and path quality, and provides reliable theoretical support and practical solutions for robot multi-objective path planning algorithms.