Ant Colony Path Planning Based on Non-uniform Modeling of Complex Environment

For a large auxiliary robot working in complex environment, an improved ant colony algorithm based on non-uniform environment modeling is proposed to search feasible paths. In the process of environment modeling, convex projection is firstly conducted to calculate the obstacle area of each environment obstacle on the projection plane. Then, the obstacles are extended by using the concept of the Minkowski sum, and a grid model of the environment is non-uniformly constructed based on the linear quadtree method. During path planning, an improved ant colony algorithm is adopted to search feasible paths of the vehicle. In order to improve performance, the pheromone update mechanisms of the max-min ant system (MMAS) and ant colony system (ACS) algorithms are fused, some target distance based and obstacle distance based heuristic search methods are used in grid selection, and a fitness function suitable for the constructed environment model is designed. Finally, the method is experimentally verified in the target area of a large laser facility, and the result shows that it can efficiently plan the vehicle path in complex environment in the presence of obstacles. Further, comparison experiments are also well conducted to demonstrate the superior performance of the proposed method in convergence and adaptability comparing with the basic ant colony algorithm.