A Hybrid Framework Based on Bio-Signal and Built-in Force Sensor for Human-Robot Active Co-Carrying

Human-robot collaboration represents a promising avenue for applications in future factory scenarios. Existing work predominantly concentrates on passive assistance based on real-time sensing sensor like force sensors, while only a few studies have ventured into the exploration and realization of active assistance provided by robots with the assistance of predictive sensors. This paper proposes an innovative hybrid framework that combines human bio-signal information with built-in robot force sensors to implement human-robot active collaboration. First, a Hill-type muscle-skeleton model is adopted and calibrated through partial swarm optimization (PSO). With this model, surface electromyography(sEMG) is used to estimate human limb stiffness. Then, an Radial Basis Function neural network (RBFNN) compensator is developed to account for the uncertainty in human-object-robot dynamics. Subsequently, we propose an adaptive variable impedance controller, incorporating a global bias into the neural network architecture. This innovative modification serves to augment the system’s robustness, streamline the network configuration by curtailing the number of hidden neurons, and consequently, facilitate more consistent and efficient human-robot interaction behavior. Finally, we substantiate the effectiveness of the proposed methodology through a two-link robotic simulation experiment and a real-world co-carrying task employing with the Baxter robot and human partner. These rigorous evaluations unveil a significant alleviation of task-related human workload attributed to our proposed framework. Note to Practitioners—This framework aims to address the existing research gap in human-robot collaboration, particularly involving bio-signal utilization, to facilitate perceptive active assistance within a typical industrial assembly scenario. In such representative tasks, many studies primarily employ real-time sensors such as force, position. These sensors, while essential, are limited by their detection principles and require collaborative operation to ascertain stiffness and realize passive assistance. Conversely, bio-signals intrinsically contain stiffness information and exhibit prospective characteristics that can be leveraged for stiffness prediction. In this typical task, we design a human-robot co-transport system with two crucial characteristics: first, the robot is capable of detecting human stiffness tendencies and comprehending human intent, leading to self-adjusting robotic behavior that provides enhanced protection for the transported object. Secondly, the newly proposed controller can manage sudden disturbances and execute self-repairs, thus increasing the task success rate and ensuring worker safety.