An efficient motion planning algorithm for stochastic dynamic systems with constraints on probability of failure

Proceedings of the National Conference on Artificial Intelligence

Volumn 3, Issue , 2008, Pages 1376-1382

  Ono, Masahiro ^a   Williams, Brian C ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHANCE CONSTRAINTS; COMPUTATION COSTS; CONTROL SEQUENCES; CONVEX OPTIMIZATION PROBLEMS; DYNAMIC SYSTEMS; ITERATIVE ALGORITHMS; MOTION PLANNING ALGORITHMS; NEW APPROACHES; OPTIMAL CONTROLS; OPTIMALITY; OPTIMIZATION APPROACHES; PERFORMANCE METRIC; PLANNING PROBLEMS; PRIOR ARTS; PROBABILITY OF FAILURES; RISK ALLOCATIONS; RISK BOUNDS; STOCHASTIC DYNAMIC SYSTEMS; STOCHASTIC DYNAMICS; SUBOPTIMALITY; SUBSTANTIAL IMPROVEMENTS; TRADE OFFS; UNCERTAIN ENVIRONMENTS; UPPER BOUNDS; UPPER STAGES;

AUTONOMOUS UNDERWATER VEHICLES; BIONICS; DYNAMIC PROGRAMMING; MOTION PLANNING; OPTIMIZATION; PROBABILITY; STOCHASTIC MODELS; SUBMERSIBLES; UNDERWATER EQUIPMENT; WATER CRAFT;

ARTIFICIAL INTELLIGENCE;

EID: 57749097475     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

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4. Léauté, T., and Williams, B. C. 2005. Coordinating agile systems through the model-based execution of temporal plans. In Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI).
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6. van Hessem, D. H. 2004. Stochastic inequality constrained closed-loop model predictive control with application to chemical process operation. Ph.D. Dissertation, Delft University of Technology.
7. Yan, J., and Bitmead, R. R. 2005. Incorporating state estimation into model predictive control and its application to network traffic control. Automatica 41:595-604.