Design Optimization of Dexterous and Energy-Efficient Robotic Manipulators for Vineyard Spraying
Abstract
This paper presents a novel mechanical design framework for robotic manipulators used in precision agriculture operations, specifically vineyard spraying. Objective functions were formulated from the kinematic and dynamic models to assess kinematic dexterity and actuator power requirements, while accounting for the nozzle’s ability to orient at a wide range of angles to target grape clusters and effectively avoid foliage occlusion. A multi-objective optimization framework was formulated to simultaneously optimize kinematic dexterity and the actuators’ dynamic power consumption along operational trajectories. Multi-Objective Evolutionary Algorithms (MOEAs) were used to solve this multi-objective problem, considering eight different 6-DOF manipulator architectures. By applying this framework, the Pareto-optimal fronts for the eight architectures were obtained, enabling a comparative performance analysis of their kinematic and dynamic characteristics. The evaluation reveals that the fully revolute (RRR + wrist) architecture achieves superior balancing, showing a 40% higher Global Posture Index than hybrid joint architectures such as RPR + wrist and RPP + wrist and fully prismatic manipulator PPP + wrist while maintaining a competitive kinematic dexterity and actuator power requirements. Finally, a comprehensive performance analysis of the optimal design was carried out to establish a robust criterion for selecting the most appropriate manipulator architecture for automated agricultural spraying.