Mobile Robot Path Planning Based on Multi-parameters Optimization Ant Colony Algorithm

The basic ant colony algorithm for mobile robot path planning has many problems, such as lack of stability,algorithm premature convergence, more difficult to find optimal solution for complex problems and so on. This paper proposes four improvement measures. 1. Apply genetic algorithm to optimization and configuration parameters of the basic ant colony algorithm; 2. Apply max min ant method to change pheromone update strategy; 3. For the ants fall into the trap causing stagnation problem, this paper proposes ant fallback strategy; 4. Add orientation function to enhance ant efficiency, reduce the time complexity of the algorithm. Simulation results show that the improved optimal path length significantly less than the basic ant colony algorithm and volatility is smaller, stability significantly improves. The stability of improved ant colony algorithm is superior to the basic ant colony algorithm, which verifies the effectiveness of the improvement measures.