Kinodynamic Trajectory Planning for Efficient UAV Exploration and Reconstruction of Unknown Environments
João Félix Mendes, Meysam Basiri, Rodrigo VENTURA
AI summary
Problem
Existing sampling-based UAV exploration planners often ignore kinodynamic constraints during viewpoint selection, leading to inefficient, abrupt maneuvers and poor global coverage.
Approach
The authors propose a kinodynamic RRT-based framework that couples Next-Best-View selection with velocity and acceleration constraints, featuring a global planner with Iterative Minimum Gain and an Informed Yaw Optimization method.
Key results
- Two kinodynamic planners (KRH-NBVP and KAEP) outperforming state-of-the-art methods
- Informed Yaw Optimization accelerating yaw selection by over twice the speed
- Iterative Minimum Gain ensuring full global coverage and escaping local minima
- Extensive simulation and real-world validation showing improved exploration rates and velocities
Why it matters
Enables faster, smoother, and more reliable autonomous UAV mapping for critical applications like search-and-rescue and hazardous environment assessment.
Abstract
Autonomous exploration of unknown 3D environ- ments 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 pro- cess. 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 dy- namically feasible motion and effective viewpoint selection, pro- ducing 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.