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Real-time Trajectory Generation via Dynamic Movement Primitives for Autonomous Racing.
Catherine Weaver, Roberto
Capobianco, Peter Wurman, Peter Stone,
and Masayoshi Tomizuka.
In American Control Conference (ACC), July 2024.
Project
page with video.
We employ sequences of high-order motion primitives for efficient online trajectory planning, enabling competitive racecar control even when the car deviates from an offline demonstration. Dynamic Movement Primitives (DMPs) utilize a target-driven non-linear differential equation combined with a set of perturbing weights to model arbitrary motion. The DMP's target-driven system ensures that online trajectories can be generated from the current state, returning to the demonstration. In racing, vehicles often operate at their han- dling limits, making precise control of acceleration dynamics essential for gaining an advantage in turns. We introduce the Acceleration goal (Acc. goal) DMP, extending the DMP's target system to accommodate accelerating targets. When sequencing DMPs to model long trajectories, our Acc. goal DMP explicitly models acceleration at the junctions where one DMP meets its successor in the sequence. Applicable to DMP weights learned by any method, the proposed DMP generates trajectories with less aggressive acceleration and jerk during transitions between DMPs compared to second-order DMPs. Our proposed DMP sequencing method can recover from trajectory deviations, achieve competitive lap times, and maintain stable control in autonomous vehicle racing within the high-fidelity racing game Gran Turismo Sport.
@inproceedings{weaver2024,
title={Real-time Trajectory Generation via Dynamic Movement Primitives for Autonomous Racing},
author={Catherine Weaver and Roberto Capobianco and Peter Wurman and Peter Stone and Masayoshi Tomizuka},
booktitle={American Control Conference (ACC)},
month={July},
year={2024},
organization={IEEE},
abstract=
{We employ sequences of high-order motion primitives for
efficient online trajectory planning, enabling competitive
racecar control even when the car deviates from an offline
demonstration. Dynamic Movement Primitives (DMPs) utilize a
target-driven non-linear differential equation combined
with a set of perturbing weights to model arbitrary
motion. The DMP's target-driven system ensures that online
trajectories can be generated from the current state,
returning to the demonstration. In racing, vehicles often
operate at their han- dling limits, making precise control
of acceleration dynamics essential for gaining an advantage
in turns. We introduce the Acceleration goal (Acc. goal)
DMP, extending the DMP's target system to accommodate
accelerating targets. When sequencing DMPs to model long
trajectories, our Acc. goal DMP explicitly models
acceleration at the junctions where one DMP meets its
successor in the sequence. Applicable to DMP weights
learned by any method, the proposed DMP generates
trajectories with less aggressive acceleration and jerk
during transitions between DMPs compared to second-order
DMPs. Our proposed DMP sequencing method can recover from
trajectory deviations, achieve competitive lap times, and
maintain stable control in autonomous vehicle racing within
the high-fidelity racing game Gran Turismo Sport.},
wwwnote={<a href="https://sites.google.com/berkeley.edu/racingdmp/home">Project page</a> with video.},
}
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