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A novel control paradigm for collision-free trajectory tracking in tractor-trailer robots

Published online by Cambridge University Press:  19 September 2025

Aliakbar Ghasemzadeh ,
Alireza Azimi ,
Roya Amjadifard  and
Ali Keymasi-Khalaji Open the ORCID record for Ali Keymasi-Khalaji [Opens in a new window]
Aliakbar Ghasemzadeh
Affiliation:
Department of Electrical and Computer Engineering, Engineering Faculty, Kharazmi University, Tehran, Iran
Alireza Azimi
Affiliation:
Department of Electrical and Computer Engineering, Engineering Faculty, Kharazmi University, Tehran, Iran
Roya Amjadifard
Affiliation:
Department of Electrical and Computer Engineering, Engineering Faculty, Kharazmi University, Tehran, Iran
Ali Keymasi-Khalaji*
Affiliation:
Department of Mechanical Engineering, Engineering Faculty, Kharazmi University, Tehran, Iran
*
Corresponding author: Ali Keymasi-Khalaji; Email: keymasi@khu.ac.ir
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Abstract

This paper presents a novel control framework for achieving collision-free trajectory tracking in tractor-trailer mobile robots (TTMRs) within both static and dynamic environments. This study addresses the challenges posed by nonholonomic constraints and kinematic coupling inherent in TTMR systems. An inverse kinematic control strategy, augmented with pure integral controllers, is proposed to ensure precise trajectory tracking. The control framework is further enhanced using three obstacle avoidance approaches: two customized artificial potential field (APF) approaches and a novel path planning mode (PPM) for continuous-time trajectory adjustment. APF methods, applied for the first time to TTMR trajectory tracking, incorporate gravitational and repulsive forces to guide the robot away from obstacles, whereas PPM dynamically generates a semi-circular trajectory when the robot approaches an obstacle. Case studies validate the effectiveness of the proposed strategy in accurate trajectory tracking and safe obstacle avoidance. Comparative analyses highlight the superior performance of PPM in managing complex environments.

Keywords

tractor-trailer mobile robottrajectory trackingobstacle avoidanceartificial potential fieldpath planning

Information

Type
Research Article
Information
Robotica , Volume 43 , Issue 9 , September 2025 , pp. 3395 - 3418
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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