SPACE-CONSTRAINED AUTONOMOUS REVERSING OF ARTICULATED VEHICLES
type: CreativeWork
description: This dissertation presents several modern control methods for autonomous reversing of long combination vehicles (LCVs). These approaches not only significantly improve the performance of previous autonomous reversing systems, but also address a major gap in the reversing control literature for LCVs. The methods were validated by implementing them at full-scale on experimental articulated vehicles owned by the ‘Cambridge Vehicle Dynamics Consortium’ (CVDC). Experimental results were in very good agreement with simulation results. Previous path following control methods for autonomous reversing of LCVs have focussed on minimising the tracking error between the rear-end of the combination and a desired path, irrespective of the motion of the rest of the vehicle. A significant disadvantage of these strategies is that the other parts of the vehicle, particularly the tractor unit, can experience large excursions from the reference path, thus making their implementation infeasible for manoeuvres in spatially-limited areas, such as warehouse yards and normal roads. The ‘Minimum Swept Path Control’ (MSPC) method was devised to reduce this problem by relaxing the requirement for very accurate path following, while minimising the maximum excursion of the vehicle. This strategy weights both the path-following error at the rear end of the vehicle and the swept path of the front end of the vehicle. MSPC enables the swept path to be reduced by about 50%, compared with path following control, which gives this method more realistic applications. The ‘Lane-bounded Reversing Control’ (LBRC) method requires a vehicle to satisfy the reversing objectives while constraining the motion to be within a specified ‘lane’. The LBRC controller is ‘intelligent’, which means it can pursue an optimum route without tracking a desired path generated by a path planner and can proactively avoid future potential clashes. Hence, this controller enables the autonomous reversing system to perform a precise…
abstract: This dissertation presents several modern control methods for autonomous reversing of long combination vehicles (LCVs). These approaches not only significantly improve the performance of previous autonomous reversing systems, but also address a major gap in the reversing control literature for LCVs. The methods were validated by implementing them at full-scale on experimental articulated vehicles owned by the ‘Cambridge Vehicle Dynamics Consortium’ (CVDC). Experimental results were in very good agreement with simulation results. Previous path following control methods for autonomous reversing of LCVs have focussed on minimising the tracking error between the rear-end of the combination and a desired path, irrespective of the motion of the rest of the vehicle. A significant disadvantage of these strategies is that the other parts of the vehicle, particularly the tractor unit, can experience large excursions from the reference path, thus making their implementation infeasible for manoeuvres in spatially-limited areas, such as warehouse yards and normal roads. The ‘Minimum Swept Path Control’ (MSPC) method was devised to reduce this problem by relaxing the requirement for very accurate path following, while minimising the maximum excursion of the vehicle. This strategy weights both the path-following error at the rear end of the vehicle and the swept path of the front end of the vehicle. MSPC enables the swept path to be reduced by about 50%, compared with path following control, which gives this method more realistic applications. The ‘Lane-bounded Reversing Control’ (LBRC) method requires a vehicle to satisfy the reversing objectives while constraining the motion to be within a specified ‘lane’. The LBRC controller is ‘intelligent’, which means it can pursue an optimum route without tracking a desired path generated by a path planner and can proactively avoid future potential clashes. Hence, this controller enables the autonomous reversing system to perform a precise…
author: Liu, Xuan Zuo
language: eng
keywords: Autonomous Driving, Articulated Vehicles, Model Predictive Control, Sequential Decision Making, Reinforcement Learning
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/74bac71d-5d79-49c8-9c18-f192f3481d52/download
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2021-02-27
date modified: 2026-07-22T22:24:18Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.64466