Abstract
Manipulation of heavy objects in industries is very necessary, but manual manipulation is tedious, adversely affects a worker’s health and safety, and reduces efficiency. On the contrary, autonomous robots are not flexible to manipulate heavy objects. Hence, we proposed human–robot systems, such as power assist systems, to manipulate heavy objects in industries. Again, the selection of appropriate control methods as well as inclusion of human factors in the controls is important to make the systems human friendly. However, existing power assist systems do not address these issues properly. Hence, we present a 1-DoF (degree of freedom) testbed power assist robotic system for lifting different objects. We also included a human factor, such as weight perception (a cognitive cue), in the robotic system dynamics and derived several position and force control strategies/methods for the system based on the human-centric dynamics. We developed a reinforcement learning method to predict the control parameters producing the best/optimal control performance. We also derived a novel adaptive control algorithm based on human characteristics. We experimentally evaluated those control methods and compared the system performance between the control methods. Results showed that both position and force controls produced satisfactory performance, but the position control produced significantly better performance than the force controls. We then proposed using the results to design control methods for power assist robotic systems for handling large and heavy materials and objects in various industries, which may improve human–robot interactions (HRIs) and system performance.
Highlights
The main contributions and novelties are (i) including a human user’s weight perception in the dynamics and control of the system, (ii) deriving a novel adaptive control strategy for the system for lifting objects based on the human-centric dynamics and a predictive method producing the best/optimal control performance following a reinforcement learning method, (iii) comparing the performance between position and force control methods for the same experimental setting, and so forth, with the aim of improving the human friendliness and performance of the system [29]
We assume that the favorable results in experiment 1 were the benefits/advantages of the inclusion of a human user’s weight perception in the dynamics and controls by using the optimum/best mass values in the control systems obtained through the machine learning model [26,27]
We considered a human user’s weight perceptions and reflected those perceptions in modeling the robotic system dynamics, and derived a position control and two different force control strategies/methods based on the dynamics models
Summary
We posit that suitable robotic systems/devices with human intervention/involvement, such as power assist robotic systems, may be the most suitable for handling heavy and large objects in relevant industries, especially in industries with unstructured environments [3]. In such a case, the strength of a robotic system and the intelligence of a human coworker may jointly make the human–robot collaborative system far superior to an individual robotic system or a human [4]. We posit that diversifications and novelty in power assist applications can be brought if power assist robotic systems are proposed to be used to manipulate heavy and large objects in various industries [7]. Such human-friendly power assist robotic devices have not been enormously employed in the mentioned industries yet
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