Quadtree based path planning for Unmanned Ground Vehicle in unknown environments

In this research is began with an attempt to operate the Unmanned Ground Vehicle(UGV) in unknown environments. We present a heuristic-based path planning algorithm for generating smooth and optimal paths. UGV operating in outdoor environments must deal with new objects during traversal. Path planning in occupied area must be incremental to accommodate new information and must use efficient representations. But geneal grid-based representation is dealing with a lagre amount of information about obstacles and free space. And the system performance is slow as a result. Hence we propose the use of quadtree to improve computation speed and memory requirements. Also the path planner needs to generate paths intended to be phsically realizable by vehicles with constrained dynamics. So we use a simple model of vehicle dynamics to search on a multi resolution.