Path Integral Policy Improvement and Dynamic Movement Primitives Fusion-Based Impedance Force Control With Error Loop Correction

Path Integral Strategy Improvement(PI2)-based impedance control is a superior scheme for preventing damage to the physical structure of the fruit during the harvesting process. However, it is highly sensitive to disturbances and has limited generalization ability during the parameter learning process, making it difficult to apply the correct gripping force to fruits with uncertain stiffness. To solve this problem, this paper proposes a variable impedance force control method that integrates PI2 with Dynamic Movement Primitives (DMPs), supplemented by a force error correction loop. Firstly, an adaptive impedance parameter matching mechanism based on gain schedules is designed to facilitate dynamic estimation of impedance parameters and enable precise force control. To further accelerate impedance parameter matching in unknown environments, the PI2 algorithm is introduced to optimize gain schedules, and DMPs are integrated to suppress disturbances, thereby improving the generalization ability of the impedance model’s parameter learning. In addition, an additional force error control loop has been designed to minimize the deviation between the desired and actual gripping force. Finally, the effectiveness of the proposed method is verified through simulation and experiment in fruit teleoperation picking robot. In the local-remote heterogeneous fruit teleoperation picking robot system, preventing fruit damage is critical to improving harvesting efficiency and quality. However, the existing PI2 algorithm suffers from limited generalization ability during the parameter learning process and is highly susceptible to environmental disturbances, presenting challenges in force control. To address this issue, this study proposes integrating DMPs into PI2 to optimize gain schedules and designing a force error control loop to refine trajectories, thereby achieving the force control objectives and effectively compensating for the shortcomings of PI2. Simulation and experimental results validate the effectiveness of the proposed method. Future research will focus on enhancing the algorithm’s adaptability to tackle the challenges posed by environmental variability in force control.