Abstract
Flying autonomously in a workspace populated by obstacles is one of the main goals when working with Unmanned Aerial Vehicles (UAV). To address this challenge, this paper presents a model predictive flight controller that drives the UAV through collision-free trajectories to reach a given pose or follow a waypoint path. The major advantage of this approach lies on the inclusion of three-dimensional obstacle avoidance in the control layer by adding ellipsoidal constraints to the optimal control problem. The obstacles can be added, moved and resized online, providing a way to perform waypoint navigation without the need of motion planning. In addition, the delays of the system are cosidered in the prediction by an experimental first order with delay model of the system. Successful experiments in 3D path tracking and obstacle avoidance validates its effectiveness for sense-and-avoid and surveillance applications presenting the proper structure to extent its autonomy and applications.
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Acknowledgements
This work was supported by the “Fonds National de la Recherche” (FNR), Luxembourg, under the project C15/15/10484117 (BEST-RPAS).
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Castillo-Lopez, M., Olivares-Mendez, M.A., Voos, H. (2018). Evasive Maneuvering for UAVs: An MPC Approach. In: Ollero, A., Sanfeliu, A., Montano, L., Lau, N., Cardeira, C. (eds) ROBOT 2017: Third Iberian Robotics Conference. ROBOT 2017. Advances in Intelligent Systems and Computing, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-70833-1_67
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DOI: https://doi.org/10.1007/978-3-319-70833-1_67
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