Planning in Stochastic Environments with Goal Uncertainty
| Autor: | Sandhya Saisubramanian, Luis Pineda, Shlomo Zilberstein, Kyle Hollins Wray |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Flexibility (engineering) 0209 industrial biotechnology Admissible heuristic Mathematical optimization Computer Science - Artificial Intelligence Computer science Probabilistic logic Mobile robot 02 engineering and technology Artificial Intelligence (cs.AI) 020901 industrial engineering & automation Shortest path problem 0202 electrical engineering electronic engineering information engineering State space 020201 artificial intelligence & image processing Observability Motion planning |
| Zdroj: | IROS |
| Popis: | We present the Goal Uncertain Stochastic Shortest Path (GUSSP) problem -- a general framework to model path planning and decision making in stochastic environments with goal uncertainty. The framework extends the stochastic shortest path (SSP) model to dynamic environments in which it is impossible to determine the exact goal states ahead of plan execution. GUSSPs introduce flexibility in goal specification by allowing a belief over possible goal configurations. The unique observations at potential goals helps the agent identify the true goal during plan execution. The partial observability is restricted to goals, facilitating the reduction to an SSP with a modified state space. We formally define a GUSSP and discuss its theoretical properties. We then propose an admissible heuristic that reduces the planning time using FLARES -- a start-of-the-art probabilistic planner. We also propose a determinization approach for solving this class of problems. Finally, we present empirical results on a search and rescue mobile robot and three other problem domains in simulation. 6 pages, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2019 |
| Databáze: | OpenAIRE |
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