![]() ![]() Such a DHM would fill the gap between measurements made on the operator performing the task and simulations made using a static DHM.In this paper, we introduce the principles of a new autonomous dynamic DHM, then describe an application and validation case based on an industrial assembly task adapted and implemented in the laboratory. Furthermore, the most common DHM used in the design process are controlled through inverse kinematic techniques, which may not be suitable for all situations to be simulated.A dynamic DHM automatically controlled in force and acceleration would therefore be an important contribution to analysing ergonomic aspects, especially when it comes to movement, applied forces and joint torques evaluation. However, a number of limitations concerning the use of DHM have been identified, for example biomechanical approximations, static calculation, description of the probable future situation or statistical data on human performance characteristics. A basic analysis can rely on questionnaires and video analysis, but more accurate comprehensive analysis generally requires complex expensive instrumentation, which may hamper movement task performance.In recent years, it has become possible to study the ergonomic aspects of a workstation from the initial design process, by using digital human model (DHM) software packages such as Pro/ENGINEER Manikin, JACK, RAMSIS or CATIA-DELMIA Human. ![]() Physical risk factors assessment is usually conducted by analysing postures and forces implemented by the operator during a work-task performance. The relevance of the proposed control method to model human motor adaptation has been demonstrated by various simulations. An interesting property of our controller is that it is implemented in Cartesian space with joint stiffness, damping and torque learning in a multi-objective control framework. It is very useful to deal with unstable manipulations, such as tool-use tasks, and to compensate for perturbations. This controller performs multiple tasks simultaneously (balance, non-sliding contacts, manipulation) in real time and adapts feedforward force as well as impedance to counter environmental disturbances. The novelty of our controller is that it combines multi-objective control based on human properties (combined feedforward and feedback controller) with a learning technique based on human learning properties (human-being’s ability to learn novel task dynamics through the minimization of instability, error and effort). This paper presents a new learning control framework for digital human models in a physics-based virtual environment. ![]()
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December 2022
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