Calibrating intuitive and natural human–robot interaction and performance for power-assisted heavy object manipulation using cognition-based intelligent admittance control schemes

Abstract

In the first step, a one degree of freedom power assist robotic system is developed for lifting lightweight objects. Dynamics for human-robot co-manipulation is derived that includes human cognition, for example, weight perception. A novel admittance control scheme is derived using the weight perception-based dynamics. Human subjects lift a small-sized, lightweight object with the power assist robotic system. Human-robot interaction and system characteristics are analyzed. A comprehensive scheme is developed to evaluate the human-robot interaction and performance, and a constrained optimization algorithm is developed to determine the optimum human-robot interaction and performance. The results show that the inclusion of weight perception in the control helps achieve optimum human-robot interaction and performance for a set of hard constraints. In the second step, the same optimization algorithm and control scheme are used for lifting a heavy object with a multidegree of freedom power assist robotic system. The results show that the human-robot interaction and performance for lifting the heavy object are not as good as that for lifting the lightweight object. Then, weight perception-based intelligent controls in the forms of model predictive control and vision-based variable admittance control are applied for lifting the heavy object. The results show that the intelligent controls enhance human-robot interaction and performance, help achieve optimum human-robot interaction and performance for a set of soft constraints, and produce similar human-robot interaction and performance as obtained for lifting the lightweight object. The human-robot interaction and performance for lifting the heavy object with power assist are treated as intuitive and natural because these are calibrated with those for lifting the lightweight object. The results also show that the variable admittance control outperforms the model predictive control. We also propose a method to adjust the variable admittance control for three degrees of freedom translational manipulation of heavy objects based on human intent recognition. The results are useful for developing controls of human friendly, high performance power assist robotic systems for heavy object manipulation in industries.

Experimental investigation into the basic application of force and position control for human-machine team lifting operations in manufacturing

“…The experimental setup replicated a lifting task that is similar to what is found in material handling operations in manufacturing environments. Although similar in nature to Rahman et al, 2–4 the presented research in this article experimentally examined fundamental control approaches for a machine lifting a container in conjunction with a human. The research is unique because the experimental setup created a human-machine team that simulated a lifting operation that has historically been performed by a two-person team.…”

“…They also noted that having input force sensors near the human grasping locations lead to increased performance. Continued work by Rahman and Ikeura 4 developed a control scheme using weight perception as the key element. They were able to demonstrate that the inclusion of weight perception in the control scheme produces optimum human-robot interaction and performance.…”

Development of a Real-Time Human-Robot Collaborative System Based on 1 kHz Visual Feedback Control and Its Application to a Peg-in-Hole Task

“…As a result, the system can collaborate with human motion in the true sense, which means that robot can react instantly and flexibly to human motion. In addition, in the conventional methods using a force sensor [ 29 , 30 , 31 ], it takes time for the sensor to measure the reaction force, to determine that the object was actually operated by the subject and to recognize the direction in which the object was moved. It can be considered that it is difficult to achieve these tasks and to speed up this approach.…”

Instruction Design of a Mechatronics Course Based on Closed-loop 7E Model Refined with DBR Method

“…In education, under the DBR method, the design and implementation of a lesson on a particular topic can be divided into several consecutive iterations, the outcomes of each iteration are assessed and analyzed, the outcomes are compared with the desired or targeted outcomes [37], shortcomings are identified, and necessary changes and actions for refinement and improvements in the design are determined to implement in the next and/or remaining iterations [4].…”