An Improved Fruit Fly Optimization Algorithm for Robot Path Planning with Dynamic Obstacles

In order to better solve the robot path planning problems with dynamic obstacles, an improved fruit fly optimization algorithm (IFOA) is proposed. Based on the original algorithm, the following improvement measures are proposed. Firstly, chaos initialization is applied to improve the quality of the initial population. Secondly, the idea of adaptive step lengths is introduced to improve the convergence speed and solving precision of the algorithm. Then the proposed concept of density factor can effectively prevent fruit fly population from falling into the local optima. Finally, a parallel strategy is brought in to make the algorithm more effective for the balance of global search and local search. Based on two typical maps, considering the obstacles with uniform linear motion and circular motion, the mathematical optimization model for the path planning problems is established and the following problem-solving strategy is presented. First, Dijkstra algorithm is employed to obtain the coarse-grained paths, then optimized algorithms are used to obtain final planned paths. By simulation data analysis and result comparisons, the feasibility and effectiveness of the proposed IFOA algorithm for solving this kind of dynamic obstacle path planning problems are verified.