Heavy Objects In Industries Research Articles

Machine Learning-Based Cognitive Position and Force Controls for Power-Assisted Human–Robot Collaborative Manipulation

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.

Weight-perception-based fixed and variable admittance control algorithms for unimanual and bimanual lifting of objects with a power assist robotic system

Weight-perception-based fixed admittance control algorithm and variable admittance control algorithm are proposed for unimanual and bimanual lifting of objects with a power assist robotic system. To include weight perception in controls, the mass parameter for the inertial force is hypothesized as different from that for the gravitational force in the dynamics model for lifting objects with the system. For the bimanual lift, two alternative approaches of force sensor arrangements are considered: a common force sensor and two separate force sensors between object and human hands. Computational models for power assistance, excess in load forces, and manipulation efficiency and precision are derived. The fixed admittance control algorithm is evaluated in a 1-degree-of-freedom power assist robotic system. Results show that inclusion of weight perception in controls produce satisfactory performance in terms of power assistance, system kinematics and kinetics, human–robot interactions, and manipulation efficiency and precision. The fixed admittance control algorithm is then augmented to variable admittance control algorithm as a tool of active compliance to vary the admittance with inertia instead of with gravity. The evaluation shows further improvement in the performance for the variable admittance control algorithm. The evaluation also shows that bimanual lifts outperform unimanual lifts and bimanual lifts with separate force sensors outperform bimanual lifts with a common force sensor. Then, the results are proposed to develop power assist robotic systems for handling heavy objects in industries.

MPC to optimise performance in power‐assisted manipulation of industrial objects

A 1-DOF power-assist robotic system (PARS) is developed using a linear actuator for vertical manipulation of heavy objects in collaboration with its human user. Human's differential perception of inertia and gravity is considered in the manipulation dynamics, and an admittance-type feedback position control scheme is designed to control the motion of the system. A human manipulates a heavy object with the robot in harmonic motion (the object is repeatedly lifted up and lowered down). A common performance index for the human and the robot for the collaborative task is derived in terms of manipulation velocity and precision. An event-triggered model predictive control (MPC) scheme is designed to maintain optimal performance of the robot and the human through maintaining optimum robot velocity and precision at the events when low performance is observed. Results show that the weight-perception-based control helps produce satisfactory collaborative performance, and the MPC is proven effective to maintain optimal performance and improved human–robot interaction. The findings are useful to develop predictive control strategies for human-friendly PARSs for manipulating heavy objects in industries in particular, and for other variable-speed electric drive systems for industrial applications, in general.

Weight-Prediction-Based Predictive Optimal Position and Force Controls of a Power Assist Robotic System for Object Manipulation

In the first step, a novel approach of inclusion of weight prediction in the dynamics of a power assist robotic system (PARS) for lifting objects is proposed. Then, a position control and two force control algorithms are derived using the weight-prediction-based dynamics so that the inclusion of weight prediction in the controls can mitigate the effects of the erroneous feedforward input force programming of the human. In the second step, a variable-inertia-based empirical predictive control strategy is proposed to augment the performance of the controls. The control algorithms are tested using a 1-DOF PARS for lifting lightweight objects. A novel scheme is proposed to evaluate and optimize the performance of the controls. The experimental results show that weight-prediction-based predictive controls help optimize human–robot interaction (HRI) and manipulation performance. The results show that the position control produces better HRI and performance than the force controls, and the force control designed with force feedback performs better than the force control designed with acceleration feedback. The results seem to be very useful to design predictive controls of power assist robots for handling heavy objects in industries.

Optimizing Perceived Heaviness and Motion for Lifting Objects with a Power Assist Robot System Considering Change in Time Constant

Abstract Power assist robots are usually used for disabled and elderly people to augment their abilities and skills. This paper proposes to use these robots to handle heavy objects in industries, and thus brings a novelty in the applications of power assist robots. However, it is difficult to optimize perceived heaviness and motion either independently or simultaneously for lifting objects with power-assist. Hence, this paper investigates the techniques to optimize perceived heaviness and motion following bionic and psychophysical approaches. We developed two systems-one was used to lift objects manually, and another was a power assist system to lift objects with it. Several hypotheses and strategies related to weight perception and time constant were adopted. Humans lifted objects manually and with power-assist independently. Analyses showed that load force rate for power-assisted lifting were lower than that for manual lifting. We hypothesized that time constant of the assist system might be responsible for this. We changed time constant and found that increase in time constant reduced perceived heaviness and load force. Then, objects were lifted with power-assist in some selected conditions pertaining to time constant. Analyses showed that perceived heaviness was related to load force rate while object motion (acceleration) was related to load force magnitude. It was then demonstrated how to independently optimize perceived heaviness and motion by optimizing load force rate and its magnitude respectively. Techniques for simultaneous optimization of motion and perceived heaviness were also presented. Finally, we proposed to use the findings to develop power assist robots for manipulating heavy objects in industries that may enhance interactions with humans in terms of maneuverability, safety etc.

Improving Interactions between a Power-Assist Robot System and Its Human User in Horizontal Transfer of Objects Using a Novel Adaptive Control Method

Power assist systems are usually used for rehabilitation, healthcare, and so forth.This paper puts emphasis on the use of power assist systems for object transfer and thus brings a novelty in the power-assist applications. However, the interactions between the systems and the human users are usually not satisfactory because human features are not included in the control design. In this paper, we present the development of a 1-DOF power assist system for horizontal transfer of objects. We included human features such as weight perception in the system dynamics and control. We then simulated the system using MATLAB/Simulink for transferring objects with it and (i) determined the optimum maneuverability conditions for object transfer, (ii) determined psychophysical relationships between actual and perceived weights, and (iii) analyzed load forces and motion features. We then used the findings to design a novel adaptive control scheme to improve the interactions between the user and the system. We implemented the novel control (simulated the system again using the novel control), the subjects evaluated the system, and the results showed that the novel control reduced the excessive load forces and accelerations and thus improved the human-system interactions in terms of maneuverability, safety, and so forth. Finally, we proposed to use the findings to develop power assist systems for manipulating heavy objects in industries that may improve interactions between the systems and the users.

Estimating and Validating Relationships between Actual and Perceived Weights for Lifting Objects with a Power Assist Robot: The Psychophysical Approach

A power assist robot reduces the perceived weights of objects lifted with it. However, the relationships between actual and perceived weights have not been estimated yet that result in inappropriate force programming, improper interactions between robots and human users in terms of safety, maneuverability, motion etc. In this paper, we present the development of a power assist robot system for lifting objects. We estimated relationships between actual and perceived weights for the objects lifted with the system by comparing the perceived weights of the power-assist-lifted objects to some reference weights following psychophysics. The results showed that the perceived weights were 40% of the actual weights. However, the power-assist-lifted objects were constrained objects as they were tied to the force sensor and the objects for reference weights were unconstrained objects, which might affect the accuracy and reliability of the relationships. This is why, we conducted another experiment where we made two objects with identical appearance. One was lifted by humans in constrained condition, and another was lifted in unconstrained condition, and weight perception between the two conditions was compared. The results showed that weight perception for constrained lifting was not much different from that for unconstrained lifting. Results of this experiment validated the psychophysical relationships between actual and perceived weights for the power-assistlifted objects, and also confirmed the accuracy of the relationships. Finally, we proposed to use the findings to develop power assist robots for manipulating heavy objects in industries that would improve interactions between robots and their users.

Weight-Perception-Based Novel Control of a Power-Assist Robot for the Cooperative Lifting of Light-Weight Objects

We developed a 1-DOF power assist robot system for lifting objects by two humans cooperatively. We hypothesized that weight perception due to inertia might be different from that due to gravity when lifting an object with power-assist because the perceived weight differs from the actual weight. The system was simulated and two humans cooperatively lifted objects with it. We analyzed human features such as weight perception, load forces, motions etc. We found that the robot reduced the perceived weights to 25% of the actual weights, and the load forces were 8 times larger than the actual requirements. The excessive load forces resulted in excessive accelerations that jeopardized the performances. We then implemented a novel control based on the human features, which was such that a virtual mass exponentially declined from a large value to a small one when subjects lifted objects with the robot and the command velocity exceeded a threshold. The novel control reduced excessive load forces and accelerations and thus enhanced performances in terms of maneuverability, safety etc. The findings may be used to develop power assist robots for manipulating heavy objects in industries that may augment human's abilities and skills and may improve interactions between robots and users.

Design and Control of a Power Assist System for Lifting Objects Based on Human Operator's Weight Perception and Load Force Characteristics

This paper deals with the design and control of a power assist system for lifting objects. It was hypothesized that an operator's perception of weight due to inertial force might be different from the perceived weight due to gravitational force. The system was simulated for different sizes of objects. Results show that an increase in mechanical time constant reduces perceived heaviness and load forces. The results also show the technique for simultaneous optimization of motion and perceived heaviness. Psychophysical studies show that the power assist system reduces the weights of the lifted objects to 40%, 30%, and 25% of the actual weights for unimanual, bimanual, and cooperative lifting tasks, respectively. The studies also show that operators apply 6.30, 6.22, and 6.14 times larger than the actually required load forces for the unimanual, bimanual, and cooperative lifting tasks, respectively. Therefore, a novel control strategy was proposed that reduced the excessive load forces and thus enhanced maneuverability, safety, naturalness, etc., of the system. The feasibility of zero gravity and zero inertia was studied. The operator's load force characteristics in some worst cases were studied. The effects of friction between an object and the operator's hand on weight perception and load forces were also studied. Power-assisted manipulation of objects in harmonic motion was also considered. Finally, it was proposed to use the findings to design power assist systems for lifting heavy objects in industries such as manufacturing, logistics, transport, construction, military and rescue operations, mining, etc., that would enhance interactions with human users.

Design guidelines for power assist robots for lifting heavy objects considering weight perception, grasp differences and worst-cases

This paper presents design guidelines for power assist robots for lifting heavy objects in industries. A 1-DOF power assist robot system was developed and human's weight perception was included in the dynamics and control. The robot system was simulated and subjects lifted objects of different sizes with the robot under three distinct lifting schemes – unimanual, bimanual and cooperative lift in usual working conditions. Then the subjects lifted objects with the robot in some worst-cases (unusual working conditions). Weight perception, load forces and object's motions in both usual and worst-cases were analysed and the findings were compared to each other. A novel control was applied to reduce excessive load forces in both usual and worst-cases that improved the system performances. The findings were then proposed as design guidelines to develop power assist robots for lifting heavy objects in industries such as manufacturing and assembly, mining, construction, forestry, agriculture, transport and logistics, etc.

Towards developing a human-friendly power assist robot for manipulating heavy objects: special focus on manoeuvrability and object's surface friction

A power assist robot system was developed for manipulating objects in cooperation with human. Weight perception was included in robot dynamics and control. The robot was simulated for different conditions. Optimum manoeuvrability conditions for vertical lifting and horizontal manipulation of objects were determined. Psychophysical relationships between actual and perceived weights were determined, and load forces and motion features were analysed for unimanual and bimanual lifting of objects. Then a novel control scheme was implemented that reduced the excessive load forces and accelerations, and thus improved the system performances for unimanual and bimanual lifts. Motions were also analysed for lowering objects with the robot. A feed-forward friction model was introduced that addressed the effects of friction between human’s hand and object’s surfaces on weight perception and load force. The findings can be used to develop human-friendly power assist robots for manipulating heavy objects in industries.