Obstacle-Aware Autonomous Coverage and Navigation for Outdoor Robots

2026-09-01

AI Summary

研究解决了户外机器人长时间作业中的定位漂移、障碍物避让等问题，通过融合RTK-GNSS和EKF的ROS 2架构实现精准覆盖规划与导航。

Abstract

Long-duration outdoor coverage with autonomous platforms remains challenging beyond classical planning: deployments face localization drift in open spaces, obstacles in cluttered sites, controller feasibility in turn-heavy maneuvers, and persistent autonomy with energy management. We propose a unified ROS 2 architecture for outdoor coverage that combines coverage planning, robust localization, and Nav2-based execution. A dual-antenna RTK-GNSS fused in an EKF keeps the robot pose, both position and heading, accurate across long missions; three controller-aware refinements are added to a mature coverage planner; a Behavior-Tree mission manager coordinates multi-goal execution, layered recovery, cost-aware goal management, and autonomous docking for return-to-charge. We validate the stack through simulation and real-world trials across multiple outdoor areas with varying geometries and obstacle densities. Overall, these results show that the proposed stack can reliably complete outdoor coverage missions across varied areas, sweeping 93.1% to 96.1% of the planned coverage area.

Problem

• Localization Drift
• Obstacle Handling
• Controller Feasibility
• Energy Management

Innovation

• ROS 2 architecture
• RTK-GNSS fusion
• controller-aware refinements
• Behavior-Tree mission manager