A Spiral Coverage Path Planning Algorithm for Nonomnidirectional Robots

The limited steering capabilities of nonomnidirectional robots introduce significant complexity into complete coverage tasks, often leading to increased path overlap or incomplete coverage of certain areas. Although recent research has made progress in optimizing coverage path planning, redundant coverage or omissions are still prone to occur in the target area to be covered. To address these persistent challenges, we propose a novel spiral coverage method. This approach not only conforms to the kinematic constraints of nonomnidirectional robots but also enhances coverage efficiency by dividing the target area into center and boundary regions and devising tailored coverage strategies for each. This method effectively reduces path redundancy and improves overall area coverage. Furthermore, we introduce a comprehensive metric that combines total path length and area coverage ratio to evaluate coverage efficiency, overcoming the limitations and computational complexity associated with existing metrics. For scenarios where maximizing the area coverage ratio is critical, we have developed a high-coverage-rate turning strategy that ensures 100% coverage. Through simulation tests in six representative areas and actual experiments on airport runways, our method shows an improvement of 0.238%–14.538% in coverage efficiency compared with parallel coverage method and 60.548%–76.339% compared with deep reinforcement learning-based method. Additionally, implementing high-coverage-rate turning strategies improves the area coverage ratio by 2.021%–6.732%. In field experiments, our method reduces execution time by 1.61% compared with parallel coverage method. These results show that our method has a significant effect in improving coverage efficiency and achieving complete coverage goals.