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Generating minimum-snap quadrotor trajectories really fast

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Burke, Declan
Chapman, Airlie
Shames, Iman

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Institute of Electrical and Electronics Engineers Inc.

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We propose an algorithm for generating minimum-snap trajectories for quadrotors with linear computational complexity with respect to the number of segments in the spline trajectory. Our algorithm is numerically stable for large numbers of segments and is able to generate trajectories of more than 500, 000 segments. The computational speed and numerical stability of our algorithm makes it suitable for real-time generation of very large scale trajectories. We demonstrate the performance of our algorithm and compare it to existing methods, in which it is both faster and able to calculate larger trajectories than state-of-the-art. We also show the feasibility of the trajectories experimentally with a long quadrotor flight.

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2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020

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