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Creators/Authors contains: "Sun, Weidong"

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  1. ; ; (, IEEE Transactions on Robotics)
    null (Ed.)
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  2. ; ; (, Proceedings of the American Control Conference)
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  3. ; ; (, IEEE International Conference on Robotics and Automation)
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  4. ; ; ; ; ; ; ; ; ; (, Science Bulletin)
    null (Ed.)
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  5. ; ; (, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))
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  6. ; ; (, IEEE/RSJ Intl Conf on Intelligent Robots and Systems)
    Nonlinear optimal control problems are challenging to solve efficiently due to non-convexity. This paper introduces a trajectory optimization approach that achieves real-time performance by combining machine learning to predict optimal trajectories with refinement by quadratic optimization. First, a library of optimal trajectories is calculated offline and used to train a neural network. Online, the neural network predicts a trajectory for a novel initial state and cost function, and this prediction is further optimized by a sparse quadratic programming solver. We apply this approach to a fly-to-target movement problem for an indoor quadrotor. Experiments demonstrate that the technique calculates near-optimal trajectories in a few milliseconds, and generates agile movement that can be tracked more accurately than existing methods. 
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