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Time-optimal cornering trajectory planning for car-like mobile robots containing actuator dynamics

Published online by Cambridge University Press:  12 November 2021

Yong Jin Byeon Open the ORCID record for Yong Jin Byeon [Opens in a new window]  and
Byung Kook Kim
Yong Jin Byeon
Affiliation:
School of Electrical Engineering, Korea Advanced Institute of Science and Technology(KAIST), Daejeon, Korea
Byung Kook Kim*
Affiliation:
School of Electrical Engineering, Korea Advanced Institute of Science and Technology(KAIST), Daejeon, Korea
*
*Corresponding author. E-mails: [email protected], [email protected]
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Abstract

We establish a highly feasible algorithm for time-optimal cornering trajectory planning (TP) for car-like mobile robots (CLMRs) based on a dynamic model that contains actuator dynamics. First, we formulate an accurate dynamic model of a robot that contains DC motor actuators; this includes steering braking (caused by the lateral force of the front steering wheel) and two types of friction (viscous and Coulomb) under a nonslip condition. Our TP algorithm can utilize the full power of the DC motor actuators within proper pulse width modulation bounds and generated torque limits. Then, we establish an algorithm for a time-optimal cornering trajectory planning for CLMRs (TOCTP-CLMR). Our algorithm divides the trajectory into five sections comprising three turnings and two translations to minimize the travel distance. Then, we utilize the quickest rotation when turning to construct the time-optimal trajectory that satisfies the bang-bang principle. In addition, simulations are performed to demonstrate the validity of this method. Finally, we conduct open-loop experiments to validate our dynamic model and a trajectory tracking experiment to demonstrate the feasibility of the TOCTP-CLMR trajectory.

Keywords

time-optimal trajectory planningcar-like mobile robot dynamicsDC motor actuator dynamicstorque constraintPWM constraint
Type
Research Article
Information
Robotica , Volume 40 , Issue 5 , May 2022 , pp. 1627 - 1649
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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