Research Of Mobile Robot Path Planning Based On Ant Colony Optimization With Potential Field

With the continuous development of computers and automation technology, the mobile robot technology has been more widely used in the field of medical equipment, aerospace, and many other applications. Path planning problems is one of the important link of the mobile robot navigation technology research which involves interdisciplinary technology such as environmental perception, data processing and action coordination. At present, many optimizations have been proposed to solve the problem of path planning. But there isn’t the most efficient path planning optimization. Therefore, we need to make further research of path planning optimization.In view of the complementary advantages of the strategy among the different path planning optimizations, after explaining the basic principles and the advantages and disadvantages of the artificial potential field optimization and ant colony optimization, this article proposed the ant colony optimization with potential field, which combines potential field optimization and ant colony optimization. The optimization innovation and the work mainly contain the following contents:First, the effect of potential field resultant is introduced on the basis of ant colony distance heuristic information, which is a part of integrated heuristic information of ant colony optimization with potential field. Using the guide of the potential field resultant in artificial potential field optimization can speed up the convergence rate of ant colony optimization and improve the efficiency of optimization.Secondly, in order to balance the artificial potential field optimization and ant colony optimization in the path planning process, we introduce a potential field resultant heuristic information influence coefficient which can reduce the randomness of the ants search and enhance the guiding role of potential field resultant in the early stage of path planning. In the late stage of path planning, we should weaken the guild of potential field resultant to avoid premature search convergence and precocious puberty, finally the ants find the optimal solution to reach the target position quickly. Finally, the path planning of ant colony optimization with potential field and conventional optimization are simulated respectively by using the MATLAB simulation software under the grid environment. Simulation results show that ant colony optimization with potential field path planning is better than single artificial potential field optimization and ant colony optimization, and has better convergence speed and optimal accuracy.