Cohesion-Based Flocking Formation Using Potential Linked Nodes Model for Multi-Robot Agricultural Swarms

Accurately modeling and representing the collective dynamics of large-scale robotic systems remains one of the fundamental challenges in swarm robotics. Within the context of agricultural robotics, swarm-based coordination schemes enable scalable and adaptive control of multi-robot teams performing tasks such as crop monitoring and autonomous field maintenance. This paper introduces a cohesive Potential Linked Nodes (PLNs) framework, an adjustable formation structure that employs Artificial Potential Fields (APFs), and virtual node–link interactions to regulate swarm cohesion and coordinated motion (CM). The proposed model governs swarm formation, modulates structural integrity, and enhances responsiveness to external perturbations. The PLN framework facilitates swarm stability, maintaining high cohesion and adaptability while the system’s tunable parameters enable online adjustment of inter-agent coupling strength and formation rigidity. Comprehensive simulation experiments were conducted to assess the performance of the model under multiple swarm conditions, including static aggregation and dynamic flocking behavior using differential-drive mobile robots. Additional tests within a simulated cropping environment were performed to evaluate the framework’s stability and cohesiveness under agricultural constraints. Swarm cohesion and formation stability were quantitatively analyzed using density-based and inter-robot distance metrics. The experimental results demonstrate that the PLN model effectively maintains formation integrity and cohesive stability throughout all scenarios.