A new approach to smooth path planning of Ackerman mobile robot based on improved ACO algorithm and B-spline curve

In this paper, a new approach is proposed for the smooth path planning of Ackermann mobile robots based on an improved ant colony algorithm and B-spline curves. Firstly, by incorporating path length constraints and path smoothing constraints into the objective function, the smooth path planning problem for Ackermann mobile robots is transformed into a multi-objective optimization problem. Secondly, to address the limitations of the traditional ant colony algorithm, an improved ant colony algorithm based on the turning angle constraint (IACO-TAC) is proposed. IACO-TAC incorporates the distance factor and steering angle penalty factor in the heuristic function to reduce the path search's blindness. Moreover, the pheromone update method is improved, consisting of local pheromone update and global pheromone update, which uses a reward penalty mechanism to improve the convergence speed of the algorithm and increases the pheromone concentration of the global optimal path, respectively. Thirdly, an improved B-spline curve smoothing algorithm that considers the minimum turning radius constraint is proposed to generate a path that satisfies the kinematic constraints of the Ackermann mobile robot. Finally, the proposed method is evaluated by conducting gradient comparison experiments and ant colony algorithm comparison experiments on maps of different sizes. The experimental results demonstrate that our method exhibits a fast convergence rate and plans a path that balances path length and turn frequency while satisfying the kinematic constraints of the mobile robot. Thus, the proposed method offers an efficient and smooth path planning solution for Ackermann mobile robots in complex environments.