Kinodynamic Trajectory Planning for Efficient UAV Exploration and Reconstruction of Unknown Environments

Abstract

Autonomous exploration of unknown 3D environments requires motion planners that can efficiently identify informative regions to explore while continuously adapting to the evolving map of the environment. While existing sampling-based methods have demonstrated strong real-time performance, they often ignore the robot’s kinodynamic model and constraints. Consequently, they generate only target positions, neglecting kinodynamic considerations in the next-best-view decision process. This results in frequent slowdowns and abrupt maneuvers, reducing coverage speed and exploration efficiency. In this work, we propose a kinodynamic motion planning framework designed for fast and efficient exploration of unknown environments. By incorporating the robot’s kinodynamic model and constraints into a kinodynamic RRT, our approach bridges the gap between dynamically feasible motion and effective viewpoint selection, producing smoother and faster trajectories that improve exploration performance. Additionally, we present an Iterative Minimum Gain (IMG) approach to improve global coverage, and a novel informed yaw optimization method that accelerates optimal yaw selection, capable of achieving up to more than twice the speed of state-of-the-art methods. We validate our framework through extensive simulation and real-world experiments, demonstrating improved exploration rates, higher average velocities, and better global coverage over existing methods.