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Motion Planning for a Reversing Full-Scale Truck and Trailer System
Linköping University, Department of Electrical Engineering, Automatic Control.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this thesis improvements, implementation and evaluation have been done on a motion planning algorithm for a full-sized reversing truck and trailer system. The motion planner is based on a motion planning algorithm called Closed-Loop Rapidly-exploring Random Tree (CL-RRT).

An important property for a certain class of systems, stating that by selecting the input signals in a certain way the same result as reversing the time can be archived, is also presented. For motion planning this means that the problem of reversing from position A to position B can also be solved by driving forward from B to A and then reverse the solution. The use of this result in the motion planner has been evaluated and has shown to be very useful.

The main improvements made on the CL-RRT algorithm are a faster collision detection method, a more efficient way to draw samples and a more correct heuristic cost-to-go function. A post optimizing or smoothing method that brings the system to the exact desired configuration, based on numerical optimal control, has also been developed and implemented with successful results.

The motion planner has been implemented and evaluated on a full-scale truck with a dolly steered trailer prepared for autonomous operation with promising results.

Place, publisher, year, edition, pages
2016. , 45 p.
Keyword [en]
truck and trailer, general 2-trailer, motion planning, RRT, CL-RRT, numerical optimal control
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-130868ISRN: LiTH-ISY-EX–16/4983–SEOAI: oai:DiVA.org:liu-130868DiVA: diva2:956096
External cooperation
Scania CV AB
Subject / course
Automatic Control
Supervisors
Examiners
Available from: 2016-08-29 Created: 2016-08-29 Last updated: 2016-08-29Bibliographically approved

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