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.

To address the need of a practical motion planner for manipulators, we present an efficient and resolution-complete algorithm that has performance commensurate with task difficu._ty. The algorithm uses SANDROS, a new search strategy that combines hierarchical, nonuniform-multi-resolution, and be_t-first search to find a near-optimal solution in the configuration space. This algorithm can be applied to any manipulator, and has been tested with 5 and 6-degree-of-freedom robots, with execution time ranging from 20 seconds to 10 minutes on a 16 MIPS workstation.

We present a motion planner for theclassical mover's problem in three dimensions that is both resolution-complete and efficient in that it has performance commensurate with task difficulty. It is based on the SANDROS search strategy, which uses a hierarchical, multi-resolution representation of the configuration space along with a generate-and-test paradigm for solution paths. This planner can control the trade-offs between the computation resource and algorithmic completeness/solution path quality, and thus can fully utilize the available computing power. It is useful for navigation of mobile robots, submarines and spacecraft, or part motion feasibility in assembly planning.

We present a global motion planner for tracing curves in three dimensions with robot manipulator tool frames. This planner generates an efficient motion satisfying three types of constraints: constraints on the tool tip for curve tracing, robot kinematic constraints and robotlink collision constraints. Motions are planned using a global search algorithm and a local planner based on a potential-field approach. This planner can be used with any robots including redundant manipulators, and can control the trade-offs between its algorithmic completeness and computation time. It can be applied in many robotic tasks such as seam welding, caulking, edge deburring and chamfering, and is expected to reduce motion programming times from days to minutes.

We describe a learning user interface (LUI) for robot task planning and programming. A robot operator interacts with LUI with commands in a natural-language-like form, and teaches LUI new commands necessary to perform assigned tasks using the programming by demonstration paradigm. LUI has a basic set of commands for moving a robot. When a new form of a command is given along with a demonstration of how to execute the command in terms of a sequence of known commands, LUI abstracts and stores the demonstration sequence. The abstraction involves qualitative reasoning about spatial relationships of the robot and objects involved during the demonstration. LUI allows robot operators to teach and subsequently issue high-level, natural-language-like motion commands rather than precise numeric commands involving coordinate specifications. This capability can speed up robotic operations in many applications in which similar tasks must be carried out repetitively.