Abstract
Research on human-robot interactions has been driven by the increasing employment of robotic manipulators in manufacturing and production. Toward developing more effective human-robot collaboration during shared tasks, this paper proposes an interaction scheme by employing machine learning algorithms to interpret biosignals acquired from the human user and accordingly planning the robot reaction. More specifically, a force myography (FMG) band was wrapped around the user's forearm and was used to collect information about muscle contractions during a set of collaborative tasks between the user and an industrial robot. A recurrent neural network model was trained to estimate the user's hand movement pattern based on the collected FMG data to determine whether the performed motion was random or intended as part of the predefined collaborative tasks. Experimental evaluation during two practical collaboration scenarios demonstrated that the trained model could successfully estimate the category of hand motion, i.e., intended or random, such that the robot either assisted with performing the task or changed its course of action to avoid collision. Furthermore, proximity sensors were mounted on the robotic arm to investigate if monitoring the distance between the user and the robot had an effect on the outcome of the collaborative effort. While further investigation is required to rigorously establish the safety of the human worker, this study demonstrates the potential of FMG-based wearable technologies to enhance human-robot collaboration in industrial settings.
Highlights
Incorporating robotic technology in the industrial environment has facilitated the manufacturing process by increasing flexibility and productivity (Finkemeyer and Kiel, 2017)
We have previously shown that the support vector machine (SVM) model trained with two features extracted from force myography (FMG) data, namely power spectral density and likelihood, could classify six different hand gestures with an accuracy of above 90% (Anvaripour and Saif, 2018b)
Force myography data can be collected with force sensing resistors (FSRs), the output of each sensor depends on the amount of force applied to the active area
Summary
Incorporating robotic technology in the industrial environment has facilitated the manufacturing process by increasing flexibility and productivity (Finkemeyer and Kiel, 2017). This paper builds upon our previous works to incorporate an estimation of human intentions to improve work flow during performing the shared task, i.e., the task continues without interruptions when the human is performing movements required to complete the task To this end, an FMG band was placed around the forearm to record changes in the muscle volume. This method requires an ad-hoc sensory system for each individual, noting that FMG is a relatively inexpensive technology, the proposed approach does not considerably increase the hardware or computational cost Such a method can be used as augmentative to the more established image-based methods, for example to compensate for an obstructed view or to enhance the real-time estimation of human intentions and planning of the robot trajectory
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