Robot path planning

This thesis consists of three papers concerned with the basic path planning problem for robots moving in a known static environment. Our main interest has been industrial robots, but the methods are general and apply to a wide range of robots. The path planning problem is to find a sequence of configurations that moves a robot from an initial configuration to a goal configuration without colliding with obstacles in the environment. The first paper presents a variation of the Probabilistic Roadmap Method (PRM). The new planner is called Lazy PRM and is tailored for single query path planning. By introducing a scheme for lazy evaluation, the pronounced multiple query planner is converted into an efficient single query planner. The second paper presents a resolution complete, de-randomized version of Lazy PRM. The planner uses an implicit, non-uniform grid that allows local refinement to represent the configuration space. The third paper presents a novel potential field method for free-flying rigid bodies. The planning is performed directly in the group SE(3) and is reinforced by a potential function in the workspace. Thus, the planner benefits from the explicit representation of the workspace obstacles at the same time as the planning takes place in the 6-dimensional configuration space. The potential function is harmonic and is composed of translates of the Green kernel in SE(3). Experimental results provided show that the planners are capable of solving relevant problems in various environments.