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A Vision and GPS-Based Real-Time Trajectory Planning for a MAV in Unknown and Low-Sunlight Environments

Abstract : In this paper we address the problem of real-time optimal trajectory generation of a micro Air Vehicle (MAV) in unknown and low-sunlight environments. The MAV is required to navigate from an initial and outdoor position to a final position inside of a building. In order to achieve this goal, the MAV must estimate a window of the building. For this purpose, we develop a safe path planning method using the information provided by the GPS and a consumer depth camera. With the aim of developing a safe path planning with obstacle avoidance capabilities, a model predictive control approach is developed, which uses the environment information acquired by the navigation system. The results are tested on simulations and some preliminary experimental results are given. Our system's ability to identify and estimate a window model and the relative position w.r.t. the window is demonstrated through video sequences collected from the experimental platform.
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Submitted on : Thursday, January 2, 2014 - 12:39:44 AM
Last modification on : Tuesday, November 16, 2021 - 4:30:52 AM
Long-term archiving on: : Saturday, April 8, 2017 - 8:39:08 AM


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Gerardo Ramon Flores Colunga, Shuting Zhuo, Rogelio Lozano, Pedro Castillo. A Vision and GPS-Based Real-Time Trajectory Planning for a MAV in Unknown and Low-Sunlight Environments. Journal of Intelligent and Robotic Systems, Springer Verlag, 2014, 74 (1), pp.59-67. ⟨10.1007/s10846-013-9975-7⟩. ⟨hal-00923131⟩



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