Research on operational trajectory control methods and performance testing of multi-degree-of-freedom robotic arms for fruit tree pruning

• Multi degree of freedom pruning robotic arm. • Method for controlling homework trajectory. • Improve RRT algorithm. • Joint torque model. Addressing the challenges of high labor intensity, high missed-cut rates, and low cutting success rates in fruit tree branch pruning, this study focuses on apple tree branches and proposes a multi-degree-of-freedom fruit tree pruning robotic arm along with a method for controlling its operational trajectory. Based on an investigation of dwarf self-rooted apple orchard planting patterns, an overall trajectory control strategy was formulated. Joint torque expressions were derived using Lagrange dynamics, and joint driving forces were determined via quintic interpolation. Environmental data were captured and reconstructed using a Kinect V2 depth camera. Trajectory planning and collision analysis were performed in simulation, leading to an improved Rapidly-exploring Random Tree (RRT) algorithm that reduces planning time. A prototype was built and tested for pruning trajectory planning and control. Results show that with a collision distance of 150 mm, the improved RRT algorithm achieved a 90% obstacle avoidance success rate and an average path execution time of 18.8 s. This study offers new insights for pruning device development and pro-vides academic reference for multi-degree-of-freedom robotic arm trajectory control.