Motion planning is one of the most important areas of robotics research. The complexity of the motion-planning problem has hindered the development of practical algorithms. This paper surveys the work on gross-motion planning, including motion planners for point robots, rigid robots, and manipulators in stationary, time-varying, constrained, and movable-object environments. The general issues in motion planning are explained. Recent approaches and their performances are briefly described, and possible future research directions are discussed.

Abstract-We present a path-planning algorithm for the classical mover's problem in three dimensions using a potential field representation of obstacles. A potential function similar to the electrostatic potential is assigned to each obstacle, and the topological structure of the free space is derived in the form of minimum potential valleys. Path planing is done at two levels. First, a global planner selects a robot's path from the minimum potential valleys and its orientations along the path that minimize a heuristic estimate of the path length and the chance of collision. Then a local planner modifies the path and orientations to derive the final collision-free path and orientations. If the local planner fails, a new path and orientations are selected by the global planner and subsequently examined by the local planner. This process is continued until a solution is found or there are no paths left to be examined. Our algorithm solves a much wider class of problems than other heuristic algorithms and at the same time runs much faster than exact algorithms (typically 5 to 30 min on a Sun 3/260). The algorithm fails on a small set of very hard problems involving tight free spaces. The performance of our algorithm is demonstrated on a variety of examples.

We present a general search strategy called SAN-DROS for motion planning, and its applications to motion planning for three types of robots: 1) manipulator; 2) rigid object; 3) multiple rigid objects. SANDROS is a dynamic-graph search algorithm, and can be described as a hierarchical, nonuniform-multiresolution, and bestfirst search to find a heuristically short motion in the configuration space. The SANDROS planner is resolution complete, and its computation time is commensurate with the problem difficulty measured empirically by the solution-path complexity. For many realistic problems involving a manipulator or a rigid object with six degrees of freedom, its computation times are under 1 min for easy problems involving wide free space, and several minutes for relatively hard problems.

Path planning among movable obstacles is a practical problem that is in need of a solution. In this paper, we present an efficient heuristic algorithm that uses a generate-and-test paradigm: a "good" candidate path is hypothesized by a global planner and subsequently verified by a local planner. In the process of formalizing the problem, we also present a technique for modeling object interactions through contact. Our algorithm has been tested on a variety of examples, and was able to generate solutions within 10 seconds.