Coverage Path Planning for Mobile Robots with Kinematic and Dynamic Constraints

Coverage Path Planning (CPP) is a critical capability for mobile robots tasked with applications such as orchard management, cleaning, and surveillance. This paper proposes a novel CPP algorithm that explicitly incorporates kinematic and dynamic constraints to ensure feasible and efficient paths, with a focus on orchard environments. The approach features an adaptive partitioning strategy leveraging boustrophedon decomposition to align with orchard row structures, a multi-stage optimization pipeline integrating local coverage with global connectivity, and a robust theoretical framework for modeling robot motion constraints. Extensive simulations in diverse scenarios, including an orchard setting, demonstrate superior coverage efficiency, reduced path lengths, and strict adherence to motion constraints compared to state-of-the-art methods, making the algorithm ideal for precision agriculture and similar applications.