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In this work, we have implemented a SLAM-assisted navigation module for areal autonomous vehicle with unknown dynamics. The navigation objective is toreach a desired goal configuration along a collision-free trajectory whileadhering to the dynamics of the system. Specifically, we use LiDAR-based HectorSLAM for building the map of the environment, detecting obstacles, and fortracking vehicle's conformance to the trajectory as it passes through variousstates. For motion planning, we use rapidly exploring random trees (RRTs) on aset of generated motion primitives to search for dynamically feasibletrajectory sequences and collision-free path to the goal. We demonstratecomplex maneuvers such as parallel parking, perpendicular parking, andreversing motion by the real vehicle in a constrained environment using thepresented approach.

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