Lei Wang, Chaomin Luo, Ming Li, and Jingcao Cai
Genetic algorithm, ant colony optimization, global trajectoryplanning, elitist selection, dynamic fusion
An evolving ant colony system called genetic algorithm and ant colony optimization (GA-ACO) is proposed to solve an autonomous mobile robot trajectory planning problem. The proposed evolving ant colony system ensures a feasible, safe and shortest moving trajectory by making full use of their advantages and strongly makes up for their deficiencies. The global optimal trajectory for an autonomous mobile robot can be achieved through two steps. In the first step, several initial solutions obtained by using GA were transformed into the initial pheromone values. In the second step, the best solution was obtained by fusing dynamically genetic operators into ACO to improve the performance. Compared with the basic ACO and other improved algorithm, our GA-ACO is better at avoiding falling into the local optimum and enhancing the convergence rate. The experimental results indicate that the proposed GA-ACO is efficient and feasible for solving autonomous mobile robot trajectory planning problem.
Important Links:
Go Back
