Path Planning Of Mobile Robot Dynamic Environment

With the rapid development of science and technology, especially the technical request for exploring outside world, mobile robot has been received more and more application. Path planning is one of the most fundamental and most important tasks in mobile robots. And it shows the robot's exchanging ability with around environment. So the mobile robot's path planning has important and practical meaning.In this thesis, path planning problem for mobile robots under dynamic environments is comprehensively studied and two methods are presented to deal with the problem. The first method is based on the artificial potential field. And the second one is suggested by Rapidly-exploring Random Tree (RRT) and rolling windows planning.The mobile robot is treated as simple geometrical disk in artificial potential field, in which it is forced to avoid obstacles. A new potential function is used to planning robot path. In order to eliminating chattering phenomenon at target point, the new potential functions is added an exponential factor to the conic curve attractive function. It can eliminate singularity chattering in planning. Simulation results are given in the paper.The second method researches dynamic path planning under dynamic environment of mobile robot subjecting to rolling constraint that is involved in the robotic kinematics model. At first, considering only complex static working environment, an algorithm combining optimization and Rapidly-exploring Random Tree is presented. After extended and improved, RRT generates local targets and new states with special iterative way to get close to whole goal state. During producing local target, bias parameter is introduced to speed the search. Then the variation environment with dynamic obstacles is considered, the rolling planning strategy is used to plan overall path, as the BAIS Rapidly-exploring Random Tree algorithm to find out local path. The rolling planning strategy can applied to dynamic condition by gathering dynamic obstacles' information after one local planning. It is fit for the path planning of higher dimension robotic system.