Online Exploration and Coverage Planning in Unknown Obstacle-Cluttered Environments
This addresses the challenge of efficient field monitoring or search and rescue with wheeled robots in unknown settings, representing an incremental improvement over prior online coverage algorithms.
The paper tackles the problem of online coverage planning for non-holonomic vehicles in unknown, obstacle-cluttered environments, proposing a hierarchical hex-decomposition algorithm that ensures resolution-complete coverage and can be tuned to outperform existing methods in either total covered area or exploration speed.
Online coverage planning can be useful in applications like field monitoring and search and rescue. Without prior information of the environment, achieving resolution-complete coverage considering the non-holonomic mobility constraints in commonly-used vehicles (e.g., wheeled robots) remains a challenge. In this paper, we propose a hierarchical, hex-decomposition-based coverage planning algorithm for unknown, obstacle-cluttered environments. The proposed approach ensures resolution-complete coverage, can be tuned to achieve fast exploration, and plans smooth paths for Dubins vehicles to follow at constant velocity in real-time. Gazebo simulations and hardware experiments with a non-holonomic wheeled robot show that our approach can successfully tradeoff between coverage and exploration speed and can outperform existing online coverage algorithms in terms of total covered area or exploration speed according to how it is tuned.