Adaptive path planning for flexible manufacturing

Conference · Mon Aug 01 04:00:00 EDT 1994

OSTI ID:10172152

Chen, Pang C

Path planning needs to be fast to facilitate real-time robot programming. Unfortunately, current planning techniques are still too slow to be effective, as they often require several minutes, if not hours of computation. To overcome this difficulty, we present an adaptive algorithm that uses past experience to speed up future performance. It is a learning algorithm suitable for automating flexible manufacturing in incrementally-changing environments. The algorithm allows the robot to adapt to its environment by having two experience manipulation schemes: For minor environmental change, we use an object-attached experience abstraction scheme to increase the flexibility of the learned experience; for major environmental change, we use an on-demand experience repair scheme to retain those experiences that remain valid and useful. Using this algorithm, we can effectively reduce the overall robot planning time by re-using the computation result for one task to plan a path for another.

• Research Organization: Sandia National Labs., Albuquerque, NM (United States)
• Sponsoring Organization: USDOE, Washington, DC (United States)
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 10172152
• Report Number(s): SAND--94-2005C; CONF-9410167--1; ON: DE94016561; BR: GB0103012
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
420200
99 GENERAL AND MISCELLANEOUS
990200
ALGORITHMS
COMPUTER-AIDED MANUFACTURING
FACILITIES, EQUIPMENT, AND TECHNIQUES
LEARNING
MANIPULATORS
MATHEMATICS AND COMPUTERS
ROBOTS