Abstract
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.
Highlights
The box delivery process includes multi-task jobs
For the optimization of the components, the lifting and carrying subtasks were first simulated; the final postures and velocities of the lifting and the initial postures and velocities of the carrying were imported into the first transition subtask to make a smooth motion
The unloading subtask was simulated, and the initial/final postures and velocities of the carrying and the initial postures and velocities of the unloading were imported into the second transition subtask to generate a seamless delivery motion
Summary
The box delivery process includes multi-task jobs (e.g., that involves box lifting, carrying, and lowering it). Biomechanical modeling provides a useful tool to investigate the cause and effect without worrying about injury risks. It proves that simulating a human delivering motion is a challenging task. The objective of this study is to explore the subtask-based inverse dynamic optimization formulation to efficiently analyze and predict dynamic human delivering motion in ergonomic applications. The box delivery task can be divided into four main subtasks: lifting, carrying, transition, and unloading. The transition task includes the motion from lifting to carrying and carrying to unloading with varied boundary conditions. Each subtask can be formulated as an optimization problem by associating boundary conditions between the two adjacent subtasks
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