Trajectory Optimization Through Contacts and Automatic Gait Discovery for Quadrupeds. Neunert, M., Farshidian, F., Winkler, A. W., & Buchli, J. IEEE Robotics and Automation Letters (RA-L), 2:1502-1509, 2017.
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In this work we present a Trajectory Optimization framework for whole-body motion planning through contacts. We demonstrate how the proposed approach can be applied to automatically discover different gaits and dynamic motions on a quadruped robot. In contrast to most previous methods, we do not pre-specify contact-switches, -timings, -points or gait patterns, but they are a direct outcome of the optimization. Furthermore, we optimize over the entire dynamics of the robot, which enables the optimizer to fully leverage the capabilities of the robot. To illustrate the spectrum of achievable motions, we show eight different tasks, which would require very different control structures when solved with state-of-the-art methods. Using our Trajectory Optimization approach, we are solving each task with a simple, high level cost function and without any changes in the control structure. Furthermore, we fully integrate our approach with the robot’s control and estimation framework such that we are able to run the optimization online. Through several hardware experiments we show that the optimized trajectories and control inputs can be directly applied to physical systems.
@article{neunert2017,
  author    = {Michael Neunert and 
               Farbod Farshidian and 
               Alexander W. Winkler and 
               Jonas Buchli},
  title     = {Trajectory Optimization Through Contacts and 
               Automatic Gait Discovery for Quadrupeds},
  journal   = {IEEE Robotics and Automation Letters (RA-L)},
  year      = {2017},
  pages     = {1502-1509},
  volume    = {2},
  doi       = {10.1109/LRA.2017.2665685},
  abstract  = {In this work we present a Trajectory Optimization
               framework for whole-body motion planning through contacts.
               We demonstrate how the proposed approach can be applied to
               automatically discover different gaits and dynamic motions on a
               quadruped robot. In contrast to most previous methods, we do
               not pre-specify contact-switches, -timings, -points or gait 
               patterns, but they are a direct outcome of the optimization. 
               Furthermore, we optimize over the entire dynamics of the robot, 
               which enables the optimizer to fully leverage the capabilities of
               the robot. To illustrate the spectrum of achievable motions, we 
               show eight different tasks, which would require very different 
               control structures when solved with state-of-the-art methods. 
               Using our Trajectory Optimization approach, we are solving each 
               task with a simple, high level cost function and without any 
               changes in the control structure. Furthermore, we fully integrate
               our approach with the robot’s control and estimation framework 
               such that we are able to run the optimization online. Through 
               several hardware experiments we show that the optimized 
               trajectories and control inputs can be directly applied to 
               physical systems.},
  keywords  = {Multilegged Robots, Motion and Path Planning,
               Optimization and Optimal Control}, 
  url_pdf   = {mypdfs/17-ral-neunert.pdf},
  url_video = {https://www.youtube.com/watch?v=YDCi3negCwk},
  url_link  = {https://doi.org/10.1109/LRA.2017.2665685},
}
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