POMDP-based online target detection and recognition for autonomous UAVsReportar como inadecuado




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1 Toulouse - ONERA - The French Aerospace Lab

Abstract : This paper presents a target detection and recognition mission by an autonomous Unmanned Aerial Vehicule UAV, modeled as a Partially Observable Markov Decision Process POMDP. The POMDP model deals in a single framework with both perception actions controlling the camera-s view angle, and mission actions moving between zones and flight levels, landing needed to achieve the goal of the mission, i.e. landing in a zone containing a car whose model is recognized as a desired target model with sufficient belief. We explain how we automatically learned the probabilistic observation POMDP model from statistical analysis of the image processing algorithm used on-board the UAV to analyze objects in the scene. We also present our -optimize-while-execute- framework, which drives a POMDP sub-planner to optimize and execute the POMDP policy in parallel under action duration constraints, reasoning about the future possible execution states of the robotic system. Finally, we present experimental results, which demonstrate that Artificial Intelligence techniques like POMDP planning can be successfully applied in order to automatically control perception and mission actions hand-in-hand for complex time-constrained UAV missions.

Keywords : PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES AERIAL AUTONOMOUS ROBOTICS ACTIVE PERCEPTION





Autor: Caroline Carvalho Chanel - F. Teichteil-Königsbuch - C. Lesire -

Fuente: https://hal.archives-ouvertes.fr/



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