Game-based coverage path planning for multiple Dubins robots in agricultural fields

Abstract

Multi-robot coverage path planning (MCPP) is a key technology in the field of precision agriculture, providing fundamental support for autonomous agricultural vehicles and unmanned aerial vehicles (UAVs) to perform tasks such as crop monitoring, pesticide spraying, and seeding. Although existing MCPP methods can generate feasible coverage paths, they usually do not consider the nonholonomic kinematic constraints on robots during the planning stage but instead rely on post-processing methods to smooth the paths. This makes it difficult to evaluate the true costs of paths and easily causes an imbalance in task allocation, thereby prolonging coverage time. Therefore, this article studies the coverage path planning (CPP) problem of multiple curvature-constrained Dubins robots in agricultural scenarios. Firstly, inspired by the Generalized Traveling Salesman Problem, we propose an exact mathematical model to solve the optimal coverage paths. This model is more concise than existing ones as it does not use additional endpoint continuity constraints. Based on this, we further propose a game-based Dubins multi-robot coverage path planning algorithm, which achieves load balancing through task negotiation among robots and reduces the maximum coverage time. Experiments conducted in various agricultural scenarios show that the proposed algorithm significantly improves the system load balancing and effectively reduces the overall coverage time. Specifically, compared with existing algorithms, the algorithm in this article reduces the coverage time by an average of