Virtual actuator control

Pratt, Jerry; Torres, A L Miguel; Dilworth, P.; Pratt, Gill A.

doi:10.1109/iros.1996.568974

Cited by 51 publications

(27 citation statements)

References 10 publications

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“…Based on the notion underlying the acceleration-based control method (Abe et al, 2007;Colette et al, 2008) and the Jacobian-Transpose (JT) control method (Pratt et al, 1996;Liu et al, 2011;De Magistris et al, 2011, 2013b, we developed a combined anticipatory feed-forward and feed-back control system. This controller was formulated as two successive quadratic programming (QP) problems with multiple dof which were used to simultaneously solve all the constraint equations.…”

Section: Human-like Dynamic Dhm Controlsmentioning

confidence: 99%

Dynamic digital human models for ergonomic analysis based on humanoid robotics techniques

Magistris¹,

Micaelli²,

Savin³

et al. 2015

IJDH

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Digital human models can be used for biomechanical risk factors assessment of a workstation and work activity design for which there is no physical equipment that can be tested using actual human postures and forces. Yet, using digital human model software packages is usually complex and time-consuming. A challenging aim therefore consists in developing an easy-to-use digital human model capable of computing dynamic, realistic movements and internal characteristics in quasi-real time, based on a simple description of future work tasks, in order to achieve reliable ergonomics assessments of various work task scenarios. We developed such a dynamic digital human model, which is automatically controlled in force and acceleration and inspired by human motor control and based on robotics and physics simulation. In our simulation framework, the digital human model motion was controlled by real-world Newtonian physical and mechanical laws. We also simulated and assessed experimental insert-fitting activities according to the occupational repetitive actions (OCRA) ergonomic index. Simulation led to satisfactory results: experimental and simulated ergonomics evaluations were consistent, and both joint torques and digital human model movements were realistic and coherent with human-like behaviours and performances.

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Section: Human-like Dynamic Dhm Controlsmentioning

confidence: 99%

Dynamic digital human models for ergonomic analysis based on humanoid robotics techniques

Magistris¹,

Micaelli²,

Savin³

et al. 2015

IJDH

View full text Add to dashboard Cite

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“…4), based on this type of combined anticipatory feedforward and feedback control systems based on the notion underlying the acceleration-based control method (Abe et al, 2007) (Colette et al, 2008) and the Jacobian-Transpose (JT) control method (Pratt et al, 1996) (Liu et al, 2011)(DeMagistris et al, 2011. This controller is formulated as two successive Quadratic Programming (QP) problems involving multiple degrees of freedom for simultaneously solving all the constraint equations.…”

Section: Dynamic Dhm Controlmentioning

confidence: 99%

Dynamic control of DHM for ergonomic assessments

Magistris

Micaelli

Evrard

et al. 2013

International Journal of Industrial Ergonomics

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Physical risk factors assessment is usually conducted by analysing postures and forces implemented by the operator during a work-task performance. 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. 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. 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. 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. An ergonomic assessment of both the real task and the simulation was conducted based on analysing the operator/manikin's joint angles and applied force in accordance with machinery safety standards (Standard NF EN ISO 1005-1 to 5 and OCcupational Repetitive Actions (OCRA) index). Given minimum description parameters of the task and subject, our DHM provides a simulation whose ergonomic assessment agrees with experimental evaluation.

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“…We adopt Open Dynamics Engine (ODE) for forward dynamics simulation and use the velocity-driven control method [25] to track the reference motion. Additionally, we apply the virtual actuator control [22] to assist trajectory tracking. The basic idea is to apply an "external" force generated by a virtual actuator to control a body segment.…”

Section: Smoothing and Dynamics Filteringmentioning

confidence: 99%

Animating rising up from various lying postures and environments

Lin

Huang

2011

Vis Comput

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Generating rising up motions is an important problem but has less been addressed in computer animation. This problem is challenging as rising motions involve complex motor skills and exhibit wide varieties due to various lying postures and environments. In this paper, we present an approach that utilizes motion planning and dynamics filtering to produce physically plausible rising motions. Our motion planning algorithm connects a given posture to a closest posture in a database of 14 rising motions. Then the dynamics filtering generates a physically plausible motion from a planned motion path. Our experiments show that a variety of motions of rising from various lying postures and different environments with obstacles can be generated easily by our approach.

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Virtual actuator control

Cited by 51 publications

References 10 publications

Dynamic digital human models for ergonomic analysis based on humanoid robotics techniques

Dynamic digital human models for ergonomic analysis based on humanoid robotics techniques

Dynamic control of DHM for ergonomic assessments

Animating rising up from various lying postures and environments

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