Adaptive path planning in changing environments

Technical Report · Fri Oct 01 04:00:00 EDT 1993

DOI:https://doi.org/10.2172/10104955· OSTI ID:10104955

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 previous experience to speed up future performance. It is a learning algorithm suitable for incrementally-changing environments such as those encountered in manufacturing of evolving products and waste-site remediation. The algorithm extends our previous work for stationary environments in two directions: For minor environmental change, an object-attached experience abstraction scheme is introduced to increase the flexibility of the learned experience; for major environmental change, an on-demand experience repair scheme is also introduced to retain those experiences that remain valid and useful. In addition to presenting this algorithm, we identify three other variants with different repair strategies. To compare these algorithms, we develop an analytic model to compare the costs and benefits of the corresponding repair processes. Using this model, we formalize the concept of incremental change, and prove the optimality of our proposed algorithm under such change. Empirically, we also characterize the performance curve of each variant, confirm our theoretical optimality results, and demonstrate the practicality of our algorithm.

• Research Organization: Sandia National Labs., Albuquerque, NM (United States)
• Sponsoring Organization: USDOE, Washington, DC (United States)
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 10104955
• Report Number(s): SAND--92-2744; ON: DE94003269; BR: GB0103012
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
420200
99 GENERAL AND MISCELLANEOUS
990200
ALGORITHMS
FACILITIES, EQUIPMENT, AND TECHNIQUES
LEARNING
MATHEMATICS AND COMPUTERS
NAVIGATION
PROGRAMMING
ROBOTS