The Creation of Motion Planning Software for a Car-like Robot

As the demand for robots and vehicles capable of autonomously navigating through obstacle-rich environments increases, so will the need for software capable of achieving such functionality. This dissertation presents software capable of finding a solution to the motion planning problem for a car-like robot in an obstacle-rich, static environment and then controlling a real-world robot such that it closely tracks the prevailing best solution while the software continually searches for better solutions. The insights gained from this dissertation may be used to develop motion planning software for industrial robots and autonomous vehicles. To achieve the objective of this dissertation, the use of planning, control, perception and localisation software is necessary. The planning software uses an online Rapidly-exploring Random Tree Star (RRT*) algorithm, which attempts to improve upon the solutions generated by the RRT* algorithm while the robot is tracking the prevailing best solution. The control software uses a trajectory tracking control method that, through a Lyapunov-like analysis using Barbălat's Lemma, is shown to provide global asymptotic stability. The perception software relays wheel encoder and onboard camera information to the localisation software, which uses the extended Kalman filter to estimate the configuration and configuration covariance of the robot. The software is based on the Robot Operating System (ROS) and is tested using a Duckiebot (DB21M). Because the Duckiebot does not possess a rear-facing camera, the mathematical model used for the car-like robot is the Dubins car, where a compact set of closed-form equations that describe the set of Dubins paths is presented in this dissertation. These equations are derived by modelling the Dubins car as an underactuated system on the special Euclidean group in dimension 2 and then solving an associated set of inverse kinematics problems. A similar set of equations that describe the set of Reeds-Shepp paths is also presented in this dissertation. To demonstrate the efficacy of the software, three tests are conducted. In the first, second and third tests, the environment contains no obstacles, one obstacle and three obstacles, respectively, where the obstacles are wooden stands with AprilTags attached, which are used to help localise the Duckiebot. The results of these tests show the software searching for and finding a solution to the motion planning problem in these environments and then controlling the Duckiebot such that it closely tracks the prevailing best solution while the software continually searches for better solutions.

Grantor

Department of Mechanical Engineering

Year dc:date.issued

2023

Author dc:creator

• McAlpine, Liam

Advisor dc:contributor.advisor

• Pretorius, Arnold

• Engineering

Handle dc:identifier.uri

http://hdl.handle.net/11427/40562

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/40562