Adaptive Force Control Strategies for Collaborative Robotic Manipulation

Abstract

Collaborative robotic manipulation has become a critical technology in modern industrial automation, healthcare, logistics, precision manufacturing, and service robotics by enabling safe human–robot collaboration within shared workspaces. Unlike conventional industrial robots operating with fixed, pre-programmed motions, collaborative robots (cobots) require adaptive force control to ensure stable interaction, precise manipulation, and human safety under dynamic and uncertain environments. This paper proposes an Adaptive Force Control Strategy for Collaborative Robotic Manipulation (AFCS-CRM) that integrates multi-modal sensing, sensor fusion, intelligent feature engineering, adaptive impedance control, machine learning-based force prediction, and reinforcement learning into a unified control framework. The system continuously acquires force, torque, tactile, vision, position, and velocity data, applies advanced preprocessing and feature extraction, predicts optimal interaction forces, and adaptively updates control parameters in real time. By combining predictive learning with impedance-based force control and continuous feedback optimization, AFCS-CRM maintains stable contact forces despite uncertainties, varying loads, and object deformations. Compared with conventional PID and fixed impedance controllers, the proposed framework significantly improves force tracking accuracy, manipulation stability, grasp reliability, response time, energy efficiency, and human safety. The scalable and intelligent architecture demonstrates strong potential for next-generation smart manufacturing, robotic assembly, precision surgery, warehouse automation, and assistive robotics, providing a robust foundation for safe, adaptive, and autonomous human–robot collaboration.