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

TL;DR

This paper presents a motion primitives based kinodynamic RRT approach integrated with SLAM for autonomous vehicle navigation, enabling complex maneuvers in constrained environments while respecting vehicle dynamics.

Contribution

The work introduces a novel combination of SLAM-assisted navigation with kinodynamic RRT using motion primitives for real autonomous vehicles.

Findings

Successfully performed complex maneuvers like parking and reversing

Demonstrated collision-free, dynamically feasible trajectories in real environments

Integrated SLAM with kinodynamic planning for autonomous navigation

Abstract

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.