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Computation-Aware Adaptive Planning and Scheduling for Safe Unmanned Airborne Operations

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Abstract

Modern unmanned aerial vehicles (UAVs) rely on high-frequency periodic sensor measurements in order to safely operate in cluttered environments with both static and dynamic obstacles. However, periodic sensor checking operations are time and computation consuming and they are often not needed, especially in situations where the UAV can operate without violating the safety constraint (e.g., in uncluttered free space). In this paper, we introduce a computation-aware framework that limits sensor checking and replanning operations to instances in which such operations could be necessary. To this end, we propose an approach that utilizes reachability analysis to capture the future states of a UAV operating under the effects of noise and disturbance and performs self-triggered scheduling for sensor monitoring and replanning operations while guaranteeing safety. The replanning operation is further relaxed by performing an online reachable tube shrinking. This approach is supplemented with an online speed adaptation policy based on the curvature of the planned path to minimize deviation from the desired trajectory due to complex system dynamics and controller limitations. The proposed technique is validated with both simulations and experiments focusing on a quadrotor motion planning operation in environments consisting of both static and dynamic obstacles.

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Acknowledgements

This material is based upon work supported by the Air Force Research Laboratory and the Defense Advanced Research Projects Agency under Contract No. FA8750-18-C-0090, ONR under agreement number N000141712012, and NSF under grant #1816591. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Air Force Research Laboratory (AFRL), the Defense Advanced Research Projects Agency (DARPA), the Department of Defense, or the United States Government.

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Authors and Affiliations

  1. Autonomous Mobile Robots Lab, Link Lab, Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, USA

    Esen Yel & Nicola Bezzo

  2. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Tony X. Lin

  3. Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA

    Nicola Bezzo

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  1. Esen Yel
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  2. Tony X. Lin
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Correspondence to Esen Yel.

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Yel, E., Lin, T.X. & Bezzo, N. Computation-Aware Adaptive Planning and Scheduling for Safe Unmanned Airborne Operations. J Intell Robot Syst 100, 575–596 (2020). https://doi.org/10.1007/s10846-020-01192-2

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