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Receding horizon path planning for 3D exploration and surface inspection

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Abstract

Within this paper a new path planning algorithm for autonomous robotic exploration and inspection is presented. The proposed method plans online in a receding horizon fashion by sampling possible future configurations in a geometric random tree. The choice of the objective function enables the planning for either the exploration of unknown volume or inspection of a given surface manifold in both known and unknown volume. Application to rotorcraft Micro Aerial Vehicles is presented, although planning for other types of robotic platforms is possible, even in the absence of a boundary value solver and subject to nonholonomic constraints. Furthermore, the method allows the integration of a wide variety of sensor models. The presented analysis of computational complexity and thorough simulations-based evaluation indicate good scaling properties with respect to the scenario complexity. Feasibility and practical applicability are demonstrated in real-life experimental test cases with full on-board computation.

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Notes

  1. RotorS: An MAV gazebo simulator, https://github.com/ethz-asl/rotors_simulator.

  2. Ascending Technologies GmbH, http://www.asctec.de/.

  3. Bridge Model, 3D Warehouse, https://3dwarehouse.sketchup.com/.

  4. Pix4D, http://pix4d.com/.

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Acknowledgements

This work has received funding from (a) the European Union’s Horizon 2020 Research and Innovation Programme under the Grant Agreement No. 644128, AEROWORKS, (b) from the VPRI supporting account of UNR.

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Authors and Affiliations

  1. ETH Zurich, Leonhardstrasse 21, Zurich, Switzerland

    Andreas Bircher, Mina Kamel, Helen Oleynikova & Roland Siegwart

  2. University of Nevada, Reno, 1664 N. Virginia Street, Reno, NV, 89557, USA

    Kostas Alexis

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  1. Andreas Bircher
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  2. Mina Kamel
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  3. Kostas Alexis
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  4. Helen Oleynikova
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  5. Roland Siegwart
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Correspondence to Andreas Bircher.

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This is one of several papers published in Autonomous Robots comprising the Special Issue on Active Perception.

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Bircher, A., Kamel, M., Alexis, K. et al. Receding horizon path planning for 3D exploration and surface inspection. Auton Robot 42, 291–306 (2018). https://doi.org/10.1007/s10514-016-9610-0

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