Safe path planning for UAV urban operation under GNSS signal occlusion risk
| Autor: | Yoko Watanabe, Jean-Alexis Delamer, Caroline Ponzoni Carvalho Chanel |
|---|---|
| Přispěvatelé: | Université Saint-Francis-Xavier (CANADA), 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), ANR-19-P3IA-0004,ANITI,Artificial and Natural Intelligence Toulouse Institute(2019), Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE), Office National d'Etudes et Recherches Aérospatiales - ONERA (FRANCE), St Francis Xavier University - STFX (CANADA), Département Traitement de l’information et systèmes - DTIS (Toulouse, France) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Computer science General Mathematics UAV Real-time computing Satellite constellation 02 engineering and technology 03 medical and health sciences 020901 industrial engineering & automation 0302 clinical medicine 11. Sustainability Traitement du signal et de l'image Motion planning Path planning PO-SSP business.industry [SCCO.NEUR]Cognitive science/Neuroscience Probabilistic logic Neurosciences Partially observable Markov decision process POMDP Navigation Computer Science Applications Tree traversal Control and Systems Engineering GNSS applications 030220 oncology & carcinogenesis Benchmark (computing) Global Positioning System Safety business [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing Software |
| Zdroj: | Robotics and Autonomous Systems Robotics and Autonomous Systems, Elsevier, 2021, pp.103800. ⟨10.1016/j.robot.2021.103800⟩ |
| ISSN: | 0921-8890 1872-793X |
| DOI: | 10.1016/j.robot.2021.103800⟩ |
| Popis: | International audience; This paper introduces a concept of safe path planning for UAV’s autonomous operation in an urban environment where GNSS-positioning may become unreliable or even unavailable. If the operation environment is a priori known and geo-localized, it is possible to predict a GNSS satellite constellation and hence to anticipate its signal occlusions at a given point and time. Motivated from this, our main idea is to utilize such sensor availability map in path planning task for ensuring UAV navigation safety. The proposed concept is implemented by a Partially Observable Markov Decision Process (POMDP) model. It incorporates a low-level navigation and guidance module for propagating the UAV state uncertainty in function of the probabilistic sensor availability. A new definition of cost function is introduced in this model such that the resulting optimal policy respects a user-defined safety requirement. A goal-oriented version of Monte-Carlo Tree Search algorithm, called POMCP-GO, is proposed for POMDP solving. The developed safe path planner is evaluated on two simple obstacle benchmark maps as well as on a real elevation map of San Diego downtown, along with GPS availability maps. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect