Data-Driven Modelling of Human-Human Co-Manipulation Using Force and Muscle Surface Electromyogram Activities

Abstract

With collaborative robots and the recent developments in manufacturing technologies, physical interactions between humans and robots represent a vital role in performing collaborative tasks. Most previous studies have focused on robot motion planning and control during the execution of the task. However, further research is required for direct physical contact for human-robot or robot-robot interactions, such as co-manipulation. In co-manipulation, a human operator manipulates a shared load with a robot through a semi-structured environment. In such scenarios, a multi-contact point with the environment during the task execution results in a convoluted force/toque signature that is difficult to interpret. Therefore, in this paper, a muscle activity sensor in the form of an electromyograph (EMG) is employed to improve the mapping between force/torque and displacements in co-manipulation tasks. A suitable mapping was identified by comparing the root mean square error amongst data-driven models, mathematical models, and hybrid models. Thus, a robot was shown to effectively and naturally perform the required co-manipulation with a human. This paper’s proposed hypotheses were validated using an unseen test dataset and a simulated co-manipulation experiment, which showed that the EMG and data-driven model improved the mapping of the force/torque features into displacements.

An Incremental Learning Approach to Detect Muscular Fatigue in Human– Robot Collaboration

“…The EMG signals are typically acquired from upper-limbs, since they are mainly involved in the physical interactions with a robot [10]. Subsequently, the EMG data can provide insights on human intentions, such as applying forces in a certain direction [20]. It can also be utilized to detect human muscle fatigue due to high payloads or endurance stress [19], [21].…”

“…In the case of human sensor data, the main idea is to stream raw data into the classifier. Due to their advanced nature, the ANNs are expected to identify relevant patterns by themselves [20], [23]. In these approaches, time-series based ANNs such as Recurrent Neural Networks (RNNs) and Long-Short Term Memory RNNs (LSTM-RNNs) delivered promising results.…”