Research On New Algorithm For Path Planning Of Mobile Robot Based On Automatic Diffluent Ant

Path planning of mobile robots is one of key issues in robotics research. It is always a focus topic for the researchers all over the world. However, traditional methods of path planning all have their own drawbacks respectively. To find a superior algorithm for the problem becomes a study hotspot in the field. Firstly, a difficult issue of robot path planning in a cluttered environment is that planed path is global optimal. Based on automatic diffluent ant new ant algorithm is proposed. First the grid method is built to describe the working space of the mobile robot,then the foraging behavior of ant is simulated and optimal path search is finished by many ant cooperatively. Furthmore, the strategies of probabilistic search,nearest neighbor search and a goal guiding function are applied to enable the searching to be rapid and efficient. Secondly, a novel algorithm for robot rolling path planning is presented. In the algorithm, the target is mapped into the closest node inside the robot visual domain and a static, locally optimal path which robot makes a step following by is accomplished. The procedure of the algorithm is repeated on every locomotion step of robot and robot can find and follow a globally optimal path to approach the target without the risk of collisions. Thirdly, a based on Ant Algorithm (AA) and Particle Swarm Optimizer (PSO) Algorithm for path planning of the robot is proposed. First the MAKLINK graph is built to describe the working space of the mobile robot ,then the Ant Algorithm is used to obtain the global navigation path, and the Particle Swarm Optimization Algorithm is adopted to get the better path. Finally,a based on Ant Algorithm (AA) and Genetic Algorithm(GA) for path planning of the robot is proposed. First the grid method is built to describe the working space of the mobile robot ,then the Ant Algorithm is used to obtain the global navigation path, and the Genetic Algorithm is adopted to get the better path .Our computer experiments demonstrate that the algorithms are robust and stable.