Efficient and reliable fruit handover between robotic arms is a critical bottleneck in automated harvesting systems. This paper aims to present a novel dynamic visual servoing framework that reformulates handover as an active tracking problem: a collection arm uses Position-Based Visual Servoing (PBVS) to robustly follow a fruit carried by a picking arm. This paradigm shift reduces the need for precise interarm synchronization and complex trajectory planning, offering a simpler and more adaptive solution for dynamic manipulation in unstructured environments.
The system is implemented on a heterogeneous two-arm platform, coordinated by a finite state machine. A PBVS controller is designed to maintain millimeter-level relative positioning under real-world conditions. The performance of the proposed two-arm dynamic handover system is quantitatively evaluated against a traditional single-arm serial picking approach through comparative experiments.
Physical experiments demonstrate that the PBVS controller achieves an average steady-state positioning error of 1.33 mm and converges within 0.85 s. Comparative results show that the two-arm system improves the picking success rate by 9.1 percentage points, reduces the total task time by 39.2% and decreases the picking arm’s movement amplitude by 39.5% compared to the single-arm baseline.
This work provides a practical, vision-driven handover solution for agricultural robotics and presents a control framework applicable to other dynamic handover scenarios. The experimental validation highlights the system’s efficiency and robustness, offering a tangible pathway toward closing the productivity gap in automated harvesting.
