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1. Two-Dimensional Symmetric Box Delivery Motion Prediction and Validation: Subtask-Based Optimization Method

   https://doi.org/10.3390/app10248798  

   Xiang, Yujiang; Tahmid, Shadman; Owens, Paul; Yang, James (December 2020, Applied Sciences)

   null (Ed.)

   Box delivery is a complicated manual material handling task which needs to consider the box weight, delivering speed, stability, and location. This paper presents a subtask-based inverse dynamic optimization formulation for determining the two-dimensional (2D) symmetric optimal box delivery motion. For the subtask-based formulation, the delivery task is divided into five subtasks: lifting, the first transition step, carrying, the second transition step, and unloading. To render a complete delivering task, each subtask is formulated as a separate optimization problem with appropriate boundary conditions. For carrying and lifting subtasks, the cost function is the sum of joint torque squared. In contrast, for transition subtasks, the cost function is the combination of joint discomfort and joint torque squared. Joint angle profiles are validated through experimental results using Pearson’s correlation coefficient (r) and root-mean-square-error (RMSE). Results show that the subtask-based approach is computationally efficient for complex box delivery motion simulation. This research outcome provides a practical guidance to prevent injury risks in joint torque space for workers who deliver heavy objects in their daily jobs. 

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2. Design Human-Robot Collaborative Lifting Task Using Optimization

   https://doi.org/10.1115/DETC2021-71818  

   Arefeen, Asif; Xiang, Yujiang (August 2021, ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Abstract In this paper, an optimization-based dynamic modeling method is used for human-robot lifting motion prediction. The three-dimensional (3D) human arm model has 13 degrees of freedom (DOFs) and the 3D robotic arm (Sawyer robotic arm) has 10 DOFs. The human arm and robotic arm are built in Denavit-Hartenberg (DH) representation. In addition, the 3D box is modeled as a floating-base rigid body with 6 global DOFs. The interactions between human arm and box, and robot and box are modeled as a set of grasping forces which are treated as unknowns (design variables) in the optimization formulation. The inverse dynamic optimization is used to simulate the lifting motion where the summation of joint torque squares of human arm is minimized subjected to physical and task constraints. The design variables are control points of cubic B-splines of joint angle profiles of the human arm, robotic arm, and box, and the box grasping forces at each time point. A numerical example is simulated for huma-robot lifting with a 10 Kg box. The human and robotic arms’ joint angle, joint torque, and grasping force profiles are reported. These optimal outputs can be used as references to control the human-robot collaborative lifting task. 

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3. Dynamic-joint-strength-based two-dimensional symmetric maximum weight-lifting simulation: Model development and validation

   https://doi.org/10.1177/0954411920913374  

   Rakshit, Ritwik; Xiang, Yujiang; Yang, James (July 2020, Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine)

   This article presents an optimization formulation and experimental validation of a dynamic-joint-strength-based two-dimensional symmetric maximum weight-lifting simulation. Dynamic joint strength (the net moment capacity as a function of joint angle and angular velocity), as presented in the literature, is adopted in the optimization formulation to predict the symmetric maximum lifting weight and corresponding motion. Nineteen participants were recruited to perform a maximum-weight-box-lifting task in the laboratory, and kinetic and kinematic data including motion and ground reaction forces were collected using a motion capture system and force plates, respectively. For each individual, the predicted spine, shoulder, elbow, hip, knee, and ankle joint angles, as well as vertical and horizontal ground reaction force and box weight, were compared with the experimental data. Both root-mean-square error and Pearson’s correlation coefficient ( r) were used for the validation. The results show that the proposed two-dimensional optimization-based motion prediction formulation is able to accurately predict all joint angles, box weights, and vertical ground reaction forces, but not horizontal ground reaction forces. 

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4. Fatigue Prediction for Repetitive Lifting

   https://doi.org/10.1115/DETC2023-116839  

   Barman, Shuvrodeb; Xiang, Yujiang; Rakshit, Ritwik; Yang, James (August 2023, ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Abstract In this study, the fatigue progression and optimal motion trajectory during repetitive lifting task is predicted by using a 10 degrees of freedom (DOFs) two-dimensional (2D) digital human model and a three-compartment controller (3CC) fatigue model. The numerical analysis is further validated by conducting an experiment under similar conditions. The human is modeled using Denavit-Hartenberg (DH) representation. The task is mathematically formulated as a nonlinear optimization problem where the dynamic effort of the joints is minimized subjected to physical and task specific constraints. A sequential quadratic programming method is used for the optimization process. The design variables include control points of (1) quartic B-splines of the joint angle profiles; and (2) the three compartment sizes profiles for the six physical joints of interest — spine, shoulder, elbow, hip, knee, and ankle. Both numerical and experimental liftings are performed with a 15.2 kg box as external load. The simulation reports the human joint torque profiles and the progression of joint fatigue. The joint torque profiles show periodic trends. A maximum of 17 cycles are predicted before the repetitive lifting task fails, which also reasonably agrees with that of the experimental results (16 cycles). This formulation is also a generalized one, hence it can be used for other repetitive motion studies as well. 

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5. Human Grasping Force Prediction, Measurement, and Validation for Human-Robot Lifting

   https://doi.org/10.1115/DETC2022-89752  

   Arefeen, Asif; Quarnstrom, Joel; Syed, Shahbaz P; Bai, He; Xiang, Yujiang (August 2022, Proceedings of the ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Abstract In this study, a 13 degrees of freedom (DOFs) three-dimensional (3D) human arm model and a 10 DOFs 3D robotic arm model are used to validate the grasping force for human-robot lifting motion prediction. The human arm and robotic arm are modeled in Denavit-Hartenberg (DH) representation. In addition, the 3D box is modeled as a floating-base rigid body with 6 global DOFs. The human-box and robot-box interactions are characterized as a collection of grasping forces. The joint torque squares of human arm and robot arm are minimized subjected to physics and task constraints. The design variables include (1) control points of cubic B-splines of joint angle profiles of the human arm, robotic arm, and box; and (2) the discretized grasping forces during lifting. Both numerical and experimental human-robot liftings were performed with a 2 kg box. The simulation reports the human arm’s joint angle profiles, joint torque profiles, and grasping force profiles. The comparisons of the joint angle profiles and grasping force profiles between experiment and simulation are presented. The simulated joint angle profiles have similar trends to the experimental data. It is concluded that human and robot share the load during lifting process, and the predicted human grasping force matches the measured experimental grasping force reasonably well. 

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