Path planning for multi-factor improved potential field ant colony algorithm

Aiming at the problem that the ant colony algorithm in the path planning algorithm is blind to the target point and cannot cope with multi-path conditions, a multi-factor improved potential field ant colony algorithm is proposed. Firstly, the algorithm introduces the artificial potential field method to reconstruct the path length heuristic function and adds the potential field force decreasing coefficient, so as to solve the problem that the ant colony algorithm has a long iteration time and is easy to fall into the local optimal solution. Then, a new multi-factor heuristic function is constructed by comprehensively considering the path length factor, path flatness factor and smoothness factor of the potential field to adapt to the complex and changeable road environment. Finally, the dynamic tangent point method is used to smooth the path to improve the overall quality of the path. Simulation experiments show that the algorithm has good adaptability in complex bumpy road conditions, and can effectively solve the problem of robot path planning.