Robot Path Planning Based On Immune Ant Colony Algorithm

Path planning is referring to the robot in the obstacle environment, to find a pathfrom the beginning to the end, the path not only makes robot avoid obstacle, but alsooptimize distance, time, and energy in certain indicators as much as possible. Antcolony algorithm is an algorithm that widely studied and applied, it has strong localsearch capability, fast convergence, but it is easy to fall into local optimum. Artificialimmune algorithm has powerful search capability and global convergence in acondition, but random search compromised local optimization performance andaffected the convergence speed.The robot working space and space’s obstacle converted into a two-dimensionalplane by using genotype, the two-dimensional plane do grid division, and use this asrobot path planning environment model. It has the same affect that to get pathplanning in the two-dimensional plane, and the robot path planning in the workingspace. Exploiting respective performance, the advantages and disadvantages those arecharacteristics of artificial immune algorithm and ant colony algorithm, it combinesartificial immune algorithm with ant colony algorithm form immune ant colonyalgorithm. The immune ant colony algorithm, firstly it uses artificial immunealgorithm quick search global feasible path a in the workspace, secondly generatespheromone distribution, lastly uses ant colony algorithm to search for the optimalsolution. Immune ant colony algorithm is a optimization method that has relativelygood convergence and optimization capabilities, it can get the optimal path of therobot path planning.In LABVIEW simulation environment for robot path planning simulation, usingthe same environment model of different algorithms, the same size different obstacleposition and the same problems of different sizes three way comparison. In the sameenvironment model and the same iteration times, immune ant colony algorithm’sshortest path is shortest, convergence speed is fastest. Compared with artificialimmune algorithm and ant colony algorithm, immune ant colony algorithm has anobvious advantage in the global search and rapid convergence characteristics.Through the comparison of simulation results, immune ant colony algorithm has better feasibility, effectiveness, adaptability and real-time effect.