Solving path planning problems in urban environments based on a priori sensor availability and execution error propagation
| Autor: | Jean-Alexis Delamer, Yoko Watanabe, Caroline P. Carvalho Chanel |
|---|---|
| Přispěvatelé: | Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE), Office National d'Etudes et Recherches Aérospatiales - ONERA (FRANCE), Département Conception et conduite des véhicules Aéronautiques et Spatiaux - DCAS (Toulouse, France), ONERA / DTIS, Université de Toulouse [Toulouse], ONERA-PRES Université de Toulouse, Département Conception et conduite des véhicules Aéronautiques et Spatiaux (DCAS), Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
020901 industrial engineering & automation Probabilistic sensor's availability 0202 electrical engineering electronic engineering information engineering Neurosciences 020201 artificial intelligence & image processing [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO] 02 engineering and technology Mixed-Observability Markov Decision Process (MOMDP) Sequential decision making Path planning |
| Zdroj: | AIAA Scitech 2019 Forum AIAA Scitech 2019 Forum, Jan 2019, San Diego, CA, United States. pp.2202, ⟨10.2514/6.2019-2202⟩ |
| DOI: | 10.2514/6.2019-2202⟩ |
| Popis: | International audience; This paper addresses safe path planning problem in urban environments under onboard sensor availability uncertainty. In this context, an approach based on Mixed-Observability Markov Decision Process (MOMDP) is presented. Such a model enables the planner to deal with a priori probabilistic sensor availability and path execution error propagation, the which depends on the navigation solution. Due to modelling particularities of this safe path planning problem, such as bounded hidden and fully observable state variables, discrete actions and particular transition function form, the belief state update function becomes a complex step that cannot be ignored during planning. Recent advances in Partially Observable Markov Decision Process (POMDP) solving have proposed a planning algorithm called POMCP, which is based on Monte-Carlo Tree Search method. It allows the planner to work on the history of the action-observation pairs without the need to compute belief state updates. Thereby, this paper proposes to apply a POMCP-like algorithm to solve the addressed MOMDP safe path planning problem. The obtained results show the feasibility of the approach and the impact of considering different a priori probabilistic sensor availability on the result policy. |
| Databáze: | OpenAIRE |
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