Different constraint handling techniques have been used with multi-objective evolutionary algorithms (MOEA) to solve constrained multi-objective optimization problems. It is impossible for a single constraint handling technique to outperform all other constraint handling techniques always on every problem irrespective of the exhaustiveness of the parameter tuning. To overcome this selection problem, an ensemble of constraint handling methods (ECHM) is used to tackle constrained multi-objective optimization problems. The ECHM is integrated with a multi-objective differential evolution (MODE) algorithm. The performance is compared between the ECHM and the same single constraint handling methods using the same MODE (using codes available from http://www3.ntu.edu.sg/home/EPNSugan/index.htm). The results show that ECHM overall outperforms the single constraint handling methods.

Constrained multi-objective optimization algorithm with an ensemble of constraint handling methods

https://hal-lirmm.ccsd.cnrs.fr/lirmm-00800829/document

Planning contact points for humanoid robots

This paper presents work from an ongoing project towards developing a new generation of virtual human models that are highly realistic in appearance, movement, and feedback. Santos™, an anatomically correct human model with more than 100 degrees of freedom, is an avatar that exhibits extensive modelling and simulation capabilities, resides in a virtual environment, and conducts human-factors analysis. The paper presents an optimisation-based approach to posture and motion prediction that allows the avatar to operate with autonomy rather than depending on stored animations and data or being restricted by inverse kinematics. It also presents approaches to determining reach envelopes and workspace zone differentiation, and discusses methods for evaluating the physiological status of the virtual human as it completes tasks. Muscle modelling including muscle wrapping, muscle force and stress determination is also discussed. Finally, the process of building a 25-DOF hand model is described. The result is an exciting step towards a virtual human that is more extensive and complete than any other.

Towards a new generation of virtual humans

The potential of digital human modeling to improve the design of products and workspaces has been limited by the time-consuming manual manipulation of figures that is required to perform simulations. Moreover, the inaccuracies in posture and motion that result from manual procedures compromise the fidelity of the resulting analyses. This paper presents a new approach to the control of human figure models and the analysis of simulated tasks. The new methods are embodied in an algorithmic framework developed in the Human Motion Simulation (HUMOSIM) laboratory at the University of Michigan. The framework consists of an interconnected, hierarchical set of posture and motion modules that control aspects of human behavior, such as gaze or upper-extremity motion. Analysis modules, addressing issues such as shoulder stress and balance, are integrated into the framework. The framework encompasses many individual innovations in motion simulation algorithms, but the primary innovation is in the development of a comprehensive system for motion simulation and ergonomic analysis that is specifically designed to be independent of any particular human modeling system. The modules are developed as lightweight algorithms based on closed-form equations and simple numerical methods that can be communicated in written form and implemented in any computer language. The modules are independent of any particular figure model structure, requiring only basic forward-kinematics control and public-domain numerical algorithms. Key aspects of the module algorithms are “behavior-based,” meaning that the large amount of redundancy in the human kinematic linkage is resolved using empirical models based on laboratory data. The implementation of the HUMOSIM framework in human figure models will allow much faster and more accurate simulation of human interactions with products and workspaces using high-level, task-based control. INTRODUCTION Digital human figure models (DHM) are now widely used for ergonomic analysis of products and workplaces. In many organizations, DHM software is a tool of first resort for answering questions relating to physical interaction between people and objects. Yet any objective appraisal of the technology would conclude that the current reality of DHM software capability is far from the promise of a “digital human” that can interact realistically with products and environments. This paper is focused on efforts to improve the ability of DHM software to simulate physical posture and motion. Nearly every other aspect of DHM functionality also warrants improvement, including body shape representation, strength simulation, and cognitive function, but posture and motion are critical to the primary applications of DHM to the assessment of physical tasks. Posture simulation is as old as computerized manikins, because the manikin must be postured before an analysis can be conducted. Important early work was performed by Ryan for the U.S. Navy (Ryan 1970). Porter et al. (1993) summarized applications of digital human models in vehicle ergonomics during the early years of personal computers, at which time few of the current commercial DHM software tools were in use. Chaffin (2001) presented case studies of the expanding use of DHM for both product and workplace design and assessment. As evidence of the importance of posture and motion simulation, dozens of papers in the SAE literature and in other forums have presented a wide variety of methods for human simulating postures and motions, including multiple-regression (Snyder et al. 1972); analytic and numerical inverse kinematics (Jung et al. 1995; Tolani et al. 2000); optimization-based inverse kinematics (Wang and Verriest 1998); differential inverse kinematics (Zhang and Chaffin, 2000); functional regression on stretch-pivot parameters (Faraway 2000); scaling, warping, and blending of motion-capture data (Park et al. 2002; Faraway 2003; Monnier et al. 2003; Park et al. 2004; Dufour and Wang 2005); and many 2006-01-2365 The HUMOSIM Ergonomics Framework: A New Approach to Digital Human Simulation for Ergonomic Analysis Matthew P. Reed, Julian Faraway, Don B. Chaffin and Bernard J. Martin University of Michigan

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The HUMOSIM Ergonomics Framework: A New Approach to Digital Human Simulation for Ergonomic Analysis

Although automatic techniques have been employed in manufacturing industries to increase productivity and efficiency, there are still lots of manual handling operations, especially for assembly and maintenance operations. In these operations, musculoskeletal disorder (MSD) is one of the major health problems due to overload and cumulative physical fatigue. With combination of conventional posture analysis techniques, digital human modeling and simulation (DHM) techniques have been developed and commercialized to evaluate the potential physical exposures. However, those ergonomic analysis tools are mainly based on posture analysis techniques, and until now there is still no fatigue index available in commercial software to evaluate the physical fatigue effectively. In this paper, a new muscle fatigue and recovery model is used to evaluate joint fatigue level in manual handling operations. The physical fatigue in a special application case is described and analyzed using digital human simulation techniques.

https://hal.archives-ouvertes.fr/hal-00530230/document

A new muscle fatigue and recovery model and its ergonomics application in human simulation

*† ‡ § ** The demand for realistic autonomous virtual humans is increasing, with potential application to prototype design and analysis for a reduction in design cycle time and cost. In addition, virtual humans that function independently, without input from a user or a database of animations, provide a convenient tool for biomechanical studies. However, development of such avatars is limited. In this paper, we capitalize on the advantages of optimization-based posture prediction for virtual humans. We extend this approach by incorporating multi-objective optimization (MOO) in two capacities. First, the objective sum and lexicographic approaches for MOO are used to develop new human performance measures that govern how an avatar moves. Each measure is based on a different concept with different potential applications. Secondly, the objective sum, the min-max, and the global criterion methods are used as different means to combine these performance measures. It is found that although using MOO to combine the performance measures generally provides reasonable results especially with a target point located behind the avatar, there is no significant difference between the results obtained with different MOO methods.

Multi-objective Optimization for Upper Body Posture Prediction

With sufficient fidelity, the use of virtual humans can save time, money, and lives through improved product design, process design, and understanding of behaviour. Optimization-based posture prediction is a unique tool, and this article presents a study that advances posture prediction with a multi-objective optimization (MOO) approach. MOO is used to both develop and combine the following human performance measures: joint displacement; musculoskeletal discomfort; and a variation on potential energy. The following MOO methods are studied in the context of human modelling: objective sum; min–max; and global criterion. Using MOO yields realistic results. Of the independent performance measures, discomfort generally provides the most accurate postures. Potential energy, however, is not a significant factor in governing human posture and should be combined with other performance measures. The three MOO methods for combining performance measures yield similar results, but the objective sum provides slightly m...

Use of multi-objective optimization for digital human posture prediction

: Presented in this paper is an on-going project to develop a new generation of virtual human models that are highly realistic in terms of appearance, movement, and feedback (evaluation of the human body during task execution) Santos(Trademark) is an avatar that exhibits extensive modeling and simulation capabilities It is an anatomically correct human model with more than 100 degrees of freedom Santos(Trademark) resides in a virtual environment and can conduct human-factors analysis This analysis entails, among other things, posture prediction, motion prediction, gain analysis, reach envelope analysis, and ergonomics studies There are essentially three stages to developing virtual humans: (1) basic human modeling (representing how a human functions independently); (2) input functionality (awareness and analysis of the human's environment); and (3) intelligent reaction to input (memory, reasoning, etc) This paper addresses the first stage Specifically, we discuss a new human model in terms of mechanics and appearance We present an optimization-based approach to kinematic and dynamic motion analysis This approach allows the avatar to operate with complete autonomy rather than with dependence on stored animations and data, or restrictions associated with inverse kinematics With dynamic analysis, it is not necessary to solve equations of motion A novel approach for determining reach envelopes is also presented, and this approach provides a unique tool for ergonomic studies Methods for evaluating the physiological status of the virtual human as tasks are completed are discussed Finally, additional on-going research is summarized The result is an exciting step towards a virtual human that is more extensive and more complete than any other

Santos™: A New Generation of Virtual Humans

Alternative Formulations for Optimization-based-Digital Human Motion Prediction

Ionic polymer metal composites with a flexible large deformation have been used as biomimetic actuators and sensors in various fields. This work mainly focuses on the validation of the proposed the...

HyungJoo Kim

Papers

Multi-objective Optimization for Upper Body Posture Prediction

Use of multi-objective optimization for digital human posture prediction

Santos™: A New Generation of Virtual Humans

Alternative Formulations for Optimization-based-Digital Human Motion Prediction

Theoretical analysis and design for a multilayered ionic polymer metal composite actuator