Abstract
This paper focuses on real-world implementation and verification of a local, model-based stochastic automatic collision avoidance algorithm, with application in remotely-piloted (tele-operated) unmanned aerial vehicles (UAVs). Automatic collision detection and avoidance for tele-operated UAVs can reduce the workload of pilots to allow them to focus on the task at hand, such as searching for victims in a search and rescue scenario following a natural disaster. The proposed algorithm takes the pilot’s input and exploits the robot’s dynamics to predict the robot’s trajectory for determining whether a collision will occur. Using on-board sensors for obstacle detection, if a collision is imminent, the algorithm modifies the pilot’s input to avoid the collision while attempting to maintain the pilot’s intent. The algorithm is implemented using a low-cost on-board computer, flight-control system, and a two-dimensional laser illuminated detection and ranging sensor for obstacle detection along the trajectory of the robot. The sensor data is processed using a split-and-merge segmentation algorithm and an approximate Minkowski difference. Results from flight tests demonstrate the algorithm’s capabilities for tele-operated collision-free control of an experimental UAV.
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This material is based upon work supported by the National Science Foundation, Partnership for Innovation Program, Grant No. 1430328. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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Bareiss, D., Bourne, J.R. & Leang, K.K. On-board model-based automatic collision avoidance: application in remotely-piloted unmanned aerial vehicles. Auton Robot 41, 1539–1554 (2017). https://doi.org/10.1007/s10514-017-9614-4
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DOI: https://doi.org/10.1007/s10514-017-9614-4