Research Status and Challenges of Robot Arms and Motion Path Planning for Ground-Based Fruit and Vegetable Harvesting Robots

Robotic arms and motion planning are key technologies that enable fruit and vegetable harvesting robots to operate autonomously while improving harvesting efficiency and operational safety. This paper reviews and analyzes the suitability of robotic arms with different structural configurations for harvesting typical fruits and vegetables, including apples, citrus fruits, strawberries, and tomatoes, in terms of their degrees of freedom, structural configurations, and variable-stiffness technologies. The review shows that six-DOF serial manipulators provide greater motion flexibility, whereas low-DOF, parallel, and Cartesian manipulators are more suitable for structured environments with relatively simple obstacle distributions. Single-target geometric motion planning has been relatively well validated, while field validation remains limited for motion-constrained trajectory generation, online replanning, learning-based planning, and multi-arm coordination. Existing motion-planning algorithms still face several challenges in complex orchard environments, including limited real-time planning capability, insufficient robustness, difficulties in coordinating multiple optimization objectives and constraints, and limited generalization across different tasks and environments. Overcoming these limitations is therefore a critical direction for future research. This study provides theoretical and technical guidance for the selection and integration of motion-planning algorithms, the optimization of robotic arm configurations, and the development of fully autonomous fruit and vegetable harvesting systems.