An effective hybrid algorithm for mobile robot global path planning

Ant Colony Optimization (ACO) exhibits parallelism, contains certain redundancy and historical information of the past solutions with pheromone trail, and is suitable for implementation on massively parallel architecture. But it is not easy to avoid local optima, especially for large-scale schedule problems. Simulated annealing (SA) is a naturally serial algorithm, but its behavior can be controlled by the cooling schedule. By reasonably combining these two global probabilistic search algorithms, we develop a general, parallel and easily implemented hybrid optimization framework, and apply it to mobile robot global path planning problems. This strategy combines the strongpoint of two algorithms. Local search algorithm of SA can further improve the solutions constructed by individuals of ant systems. Moreover, the ant system can provide effective initial solutions for neighborhood search algorithm. Simulate results shows that the hybrid strategy computation is simple, the convergence speed is fast, which significantly improve the computational efficiency of solving mobile robot’s global path planning problems. Key-Words: Mobile robot path planning, Ant colony optimization, Simulated annealing, Hybrid optimization strategy