Robust online motion planning via contraction theory and convex optimization
Name
Singh.Majumdar.Slotine.Pavone.ICRA17.pdf
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1.52 MB
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Author(s)
Singh, Sumeet
Majumdar, Anirudha
Slotine, Jean-Jacques E
Pavone, Marco
Date Issued
July 2017
Journal
2017 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Singh, Sumeet, Anirudha Majumdar, Jean-Jacques Slotine, and Marco Pavone. “Robust Online Motion Planning via Contraction Theory and Convex Optimization.” 2017 IEEE International Conference on Robotics and Automation (ICRA) (May 2017). doi:10.1109/icra.2017.7989693.
Version
Author's final manuscript
Abstract
We present a framework for online generation of robust motion plans for robotic systems with nonlinear dynamics subject to bounded disturbances, control constraints, and online state constraints such as obstacles. In an offline phase, one computes the structure of a feedback controller that can be efficiently implemented online to track any feasible nominal trajectory. The offline phase leverages contraction theory and convex optimization to characterize a fixed-size 'tube' that the state is guaranteed to remain within while tracking a nominal trajectory (representing the center of the tube). In the online phase, when the robot is faced with obstacles, a motion planner uses such a tube as a robustness margin for collision checking, yielding nominal trajectories that can be safely executed, i.e., tracked without collisions under disturbances. In contrast to recent work on robust online planning using funnel libraries, our approach is not restricted to a fixed library of maneuvers computed offline and is thus particularly well-suited to applications such as UAV flight in densely cluttered environments where complex maneuvers may be required to reach a goal. We demonstrate our approach through simulations of a 6-state planar quadrotor navigating cluttered environments in the presence of a cross-wind. We also discuss applications of our approach to Tube Model Predictive Control (TMPC) and compare the merits of our method with state-of-the-art nonlinear TMPC techniques.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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DOI of Published Version
https://doi.org/10.1109/ICRA.2017.7989693
