Learning the hidden human knowledge of UAV pilots when navigating in a cluttered environment for improving path planning
Cita com:
hdl:2117/103148
Tipus de documentText en actes de congrés
Data publicació2016
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
ProjecteCOOPERACION ROBOTS HUMANOS EN AREAS URBANAS (MICINN-DPI2010-17112)
AEROARMS - AErial RObotic system integrating multiple ARMS and advanced manipulation capabilities for inspection and maintenance (EC-H2020-644271)
AEROARMS - AErial RObotic system integrating multiple ARMS and advanced manipulation capabilities for inspection and maintenance (EC-H2020-644271)
Abstract
We propose in this work a new model of how the hidden human knowledge (HHK) of UAV pilots can be incorporated in the UAVs path planning generation. We intuitively know that human’s pilots barely manage or even attempt to drive the UAV through a path that is optimal attending to some criteria as an optimal planner would suggest. Although human pilots might get close but not reach the optimal path proposed by some planner that optimizes over time or distance, the final effect of this differentiation could be not only surprisingly better, but also desirable. In the best scenario for optimality, the path that human pilots generate would deviate from the optimal path as much as the hidden knowledge that its perceives is injected into the path. The aim of our work is to use real human pilot paths to learn the hidden knowledge using repulsion fields and to incorporate this knowledge afterwards in the environment obstacles as cause of the deviation from optimality. We present a strategy of learning this knowledge based on attractor and repulsors, the learning method and a modified RRT* that can use this knowledge for path planning. Finally we do real-life tests and we compare the resulting paths with and without this knowledge.
Descripció
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CitacióAlzugaray , I., Sanfeliu, A. Learning the hidden human knowledge of UAV pilots when navigating in a cluttered environment for improving path planning. A: IEEE/RSJ International Conference on Intelligent Robots and Systems. "IROS 2016: 2016 IEEE/RSJ International Conference on Inteligent Robots and Systems, September 28-October 9-14, 2016, Daejeon, Korea". Daejeon: 2016, p. 1589-1594.
ISBN978-1-5090-3762-9
Versió de l'editorhttp://ieeexplore.ieee.org/document/7759257/
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