VRExplorer: An Efficient View-Region based Autonomous Exploration Method in Unknown Environments for UAV

Autonomous exploration plays a crucial role in robotics applications like rescue and scene reconstruction. This work addresses the challenges of autonomous exploration in intricate unknown environments by presenting a novel UAV autonomous exploration method based on a new concept of the view-region. Our proposed approach leverages the view-region to replace the conventional viewpoint generation and selection process, streamlining the planning process for exploration. Simultaneously, we model the problem of maximizing frontier coverage within the field of view during exploration, and jointly optimize it with the exploration path optimization problem. This approach ensures exploration path safety and effectiveness while being aggressive. Additionally, a gimbal is incorporated beneath the camera, with an associated optimization problem designed to minimize UAV self-rotation and enhance exploration efficiency. Simulations and real-world experiments demonstrate that the proposed method outperforms existing state-of-the-art methods in terms of runtime and distance traveled.

Authors

• Kai Xu
• Lanxiang Zheng
• Mingxin Wei
• Hui Cheng

Keywords

• Runtime
• Autonomous aerial vehicles
• Cameras
• Safety
• Planning
• Optimization
• Intelligent robots
• Unmanned Aerial Vehicles
• Unknown Environment
• Autonomous Exploration
• Optimization Problem
• Real-world Experiments
• Scene Reconstruction
• Exploration Path
• Simulation Experiments
• Shortest Path
• Bounding Box
• Information Gain
• Path Planning
• Convex Hull
• Penalty Function
• Depth Camera
• Global Plan
• Trajectory Optimization
• Shortest Path Length
• Exploration Time
• Benchmark Methods
• Final Trajectory
• Rapidly-exploring Random Tree
• Exploration Task
• Traveling Salesman Problem
• Sampling-based Methods
• Stable Speed
• Rapid Exploration
• Efficient Exploration
• Penalty Term
• Optimization Process