Motion Primitives Based Kinodynamic RRT for Autonomous Vehicle Navigation in Complex Environments

In this work, we have implemented a SLAM-assisted navigation module for a real autonomous vehicle with unknown dynamics. The navigation objective is to reach a desired goal configuration along a collision-free trajectory while adhering to the dynamics of the system. Specifically, we use LiDAR-based Hector SLAM for building the map of the environment, detecting obstacles, and for tracking vehicle's conformance to the trajectory as it passes through various states. For motion planning, we use rapidly exploring random trees (RRTs) on a set of generated motion primitives to search for dynamically feasible trajectory sequences and collision-free path to the goal. We demonstrate complex maneuvers such as parallel parking, perpendicular parking, and reversing motion by the real vehicle in a constrained environment using the presented approach.