Planning dynamic vehicle motion using move trees

Generating skilled and well-planned behaviours for autonomous agents is a challenging problem common to both computer animation and robotics. This thesis presents a system that uses motion graphs for online motion planning, resulting in skilled driving behaviours for a dynamic model of a car in a constrained environment. The result reproduces skilled driving behaviors. It is a particular challenge to get the cars to produce skidding-into-turn behaviors when approaching sharp corners, which can achieve the fastest speeds around a track. The techniques explored in this thesis are potentially generalizable to other dynamic vehicle behaviours, in computer games or simulations. We demonstrate that a well-formed move tree or motion graph, created from the output of a physics-based simulation can be used to produce realistic steering behaviours on a variety of tracks. We show that a finite-horizon A * search algorithm is well suited to this task. We have produced a number of smooth animations that demonstrate considerable anticipation and agility, be it through acceleration/deceleration around tricky obstacles, or a hard skidding turn into a corner after approaching at high speed. Finally, we offer a number of ways that we could speed up the algorithms for future work in this area.Science, Faculty ofComputer Science, Department ofGraduat