Human-machine Cooperation Path Planning Based On Three Dimensional Terrain

For the navigation and controlling of Lunar Rover Vehicle (LRV), path planning is one of the key technologies. The result of path planning relies on the environment modeling of lunar surface directly. At the present time, automatically planning the whole path still could not be implemented well because of the complexity of lunar surface. Hence, human participation is needed in path planning while trying to improve its independence. For this reason, in this thesis, the path planning with human-machine cooperation which based on three-dimensional terrain is deeply studied. The main content of this thesis concludes the following:1) A traversability analysis method based on environment's Digital Elevation Model is presented. With the extracted basic terrain information, testing and evaluation of different types of obstacles are analyzed, comprehensively considering the factors such as the geometry restrictions, mobile capability of the robot etc. Traversability analysis gets reasonable result of modeling by using methods of patch analysis and subsection management.2) A hierarchical path planning method based on D* algorithm is given. By dynamically updating environment model, this method completes path planning in two steps, which are off-line planning and on-line planning respectively. With the idea of mult-path-planning, a mechanism that combines off-line planning information with on-line planning and integrate pre-action planning result with current planning, is studied and implemented in this thesis. The simulation result shows that this method has good dynamic performance and can fit different environment well.3) With the work above, a path planning system based on human-machine cooperation is designed and constructed in this thesis. This system introduces human planning and decision-making into path planning through a friendly human-machine interaction interface , it exerts the advantages of automatically management of machine and human obstacle recognition and overcomes the shortcomings both. Therefore, it guarantees the safety and reasonability of path planning of LRV.