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.

This thesis work contains a stabilization and a motion planning strategy for a truck and trailer system. A dynamical model for a general 2-trailer with two rigid free joints and a kingpin hitching has been derived based on previous work. The model holds under the assumption of rolling without slipping of the wheels and has been used for control design and as a steering function in a probabilistic motion planning algorithm. A gain scheduled Linear Quadratic (LQ) controller with a Pure pursuit path following algorithm has been designed to stabilize the system around a given reference path. The LQ controller is only used in backward motion and the Pure pursuit controller is split into two parts which are chosen depending on the direction of motion. A motion planning algorithm called Closed-Loop Rapidly-exploring Random Tree (CL-RRT) has then been used to plan suitable reference paths for the system from an initial state configuration to a desired goal configuration with obstacle-imposed constraints. The motion planning algorithm solves a non-convex optimal control problem by randomly exploring the input space to the closed-loop system by performing forward simulations of the closed-loop system. Evaluations of performance is partly done in simulations and partly on a Lego platform consisting of a small-scale system. The controllers have been used on the Lego platform with successful results. When the reference path is chosen as a smooth function the closed-loop system is able to follow the desired path in forward and backward motion with a small control error. In the work, it is shown how the CL-RRT algorithm is able to plan non-trivial maneuvers in simulations by combining forward and backward motion. Beyond simulations, the algorithm has also been used for planning in open-loop for the Lego platform. / <p>Links to movies:</p><p>Reference tracking on Lego platform:</p><p>https://www.dropbox.com/s/ebtfgfo7aq9ij8w/reference_tracking.m4v?dl=0</p><p></p><p>Motion planning simulation with CL-RRT:</p><p>https://www.dropbox.com/s/z9kk27cxdxc1llp/CL_RRT_motion_planning.wmv?dl=0</p>