Real-time Autonomous Vehicle Navigation under Unknown Dynamics

In this work, we consider the problem of fully autonomous navigation of a vehicle to reach its desired goal in a constrained environment, specifically when the vehicle dynamics are unknown. To this end, we design a modular framework comprised of (i) LiDAR-based Hector SLAM for building a map of the environment, detecting obstacles, and tracking vehicle's conformance to any given trajectory, (ii) motion primitives-based kinodynamic Rapidly-exploring Random Trees (RRTs) for building an obstacle-free trajectory between a source and destination vehicle configuration, and finally, (iii) a Proportional Integral Derivative (PID) controller for trajectory tracking and disturbance rejection. We demonstrate the execution of our framework on an actual laboratory vehicle to perform complex maneuvers such as parallel parking, perpendicular parking, and reversing motion in a constrained environment under diverse weather and illumination conditions, such as sunny days, rainy days, and night-time. The video demonstrations are available here: https://youtu.be/0SsLzYKWIFs