cropped-wordpress.jpg

FYP: MATLAB A* Based Path Planner for Autonomous Off-Road Vehicle

A picture says 1000 words

Synopsis

A new venture for the University of Bath and a collaboration with Mechanical and Electrical Engineering, the autonomous quad project aims to convert an ’off the shelf’ miniature quad bike to a driverless one. Generation of a least cost path to a pre-defined goal is required, which avoids known obstacles and will update for new obstacles detected during driving. The A* search algorithm is chosen and a comprehensive planner developed within MATLAB.

Abstract

A path planner is required as part of an autonomous off-road vehicle project at the University of Bath, which is able to generate both an initial path (off-line) around obstacles to a goal, and update using data from camera detection during driving (on-line). The A* search algorithm is selected as the program foundation (including a custom cost function with terrain traversability and steering penalty) and developed using the MATLAB com- puting package. Limitations of the algorithm are clear, with a 45◦ angle step making the method unsuitable for driving autonomy. Following this conclusion the planner is improved and better tailored to our project, by testing three ‘angle-angle’ methods: Line of Sight Path Smoothing, Theta* and Field D* Interpolation. The two former are found to create the desired line, unbounded by the nodal grid, but as ‘short-cut’ techniques still provide no method of limiting the degree of turning.

Field D* interpolation is successfully implemented and forms the final planner in both off-line and on-line routines, generating a path in average times 1.8s and 50ms respectively, for a 0.1km2 workspace, 1m node size, and a 7.5◦ interpolation step; parameters found to be most suiting to our vehicle with a 22.5◦ steering limit. Heuristic weighting is found to improve searching time by 130%, using a value 20. Steering weight is less decisive and depends on the workspace configuration, a marginal inclusion in f(n) using a value 15 is seen to reduce drive time. All results are simulated due to the project and other sub-systems not yet being at a testing stage; real-world testing remains as the principle of any future work.

FinalReport

Path Planner V1.0

cropped-JBRLogo.png

2 thoughts on “FYP: MATLAB A* Based Path Planner for Autonomous Off-Road Vehicle”

  1. Thanks. The heuristic is just a simple Pythagorean estimate, with a weighting factor k:

    h(n') = k\cdot \sqrt{(x_g - x_{n'})^2 + (y_g - y_{n'})^2}

    Where x_g, y_g are the goal coordinates and x_{n'}, y_{n'} are the node to be explored.

    I added other cost factors such as steering cost and traversability, along with the standard path cost but they are added as separate functions so be can excluded as and when.

Leave a Reply