A model for concept evaluation in design––an application to mechatronics design of robot grippers

Moulianitis, Vassilis C.; Aspragathos, Nikos Α.; Dentsoras, A.J.

doi:10.1016/j.mechatronics.2003.09.001

Cited by 54 publications

(26 citation statements)

References 16 publications

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“…Conceptual design, fuzzy set methods and mechatronic indices are all used in the mechatronic design of robot grippers for handling fabrics in [25]. Grasping performance quality characteristics, such as wrench space quality measure and robustness measure are described and explored in [26] where the focus is on automatic grasp generation and learning for industrial bin-picking.…”

Section: Computing Gripper Design Parameters By Optimizationmentioning

confidence: 99%

Task and Context Sensitive Gripper Design Learning Using Dynamic Grasp Simulation

Wolniakowski

Miatliuk

Gosiewski

et al. 2017

J Intell Robot Syst

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In this work, we present a generic approach to optimize the design of a parametrized robot gripper including both selected gripper mechanism parameters, and parameters of the finger geometry. We suggest six gripper quality indices that indicate different aspects of the performance of a gripper given a CAD model of an object and a task description. These quality indices are then used to learn task-specific finger designs based on dynamic simulation. We demonstrate our gripper optimization on a parallel finger type gripper described by twelve parameters. We furthermore present a parametrization of the grasping task and context, which is essential as an input to the computation of gripper performance. We exemplify important aspects of the indices by looking at their performance on subsets of the parameter space by discussing the decoupling of parameters and show optimization results for two use cases for different task contexts. We provide a qualitative evaluation of the obtained results based on existing design guidelines and our engineering experience. In addition, we show that with our method we achieve superior alignment properties compared to a naive approach with a cutout based on the "inverse of an object". Furthermore, we provide an experimental evaluation of our proposed method by verifying the simulated grasp outcomes through a real-world experiment.

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Section: Computing Gripper Design Parameters By Optimizationmentioning

confidence: 99%

Task and Context Sensitive Gripper Design Learning Using Dynamic Grasp Simulation

Wolniakowski

Miatliuk

Gosiewski

et al. 2017

J Intell Robot Syst

View full text Add to dashboard Cite

show abstract

“…Conceptual design, fuzzy set methods and mechatronic indices are all used in the mechatronic design of robot grippers for handling fabrics in [10]. Grasping performance quality characteristics, such as wrench space quality measure and robustness measure are described and explored in [11] where the focus is on automatic grasp generation and learning for industrial bin-picking.…”

Section: Related Work and System Overviewmentioning

confidence: 99%

Task and context sensitive optimization of gripper design using dynamic grasp simulation

Wolniakowski

Jørgensen

Miatliuk

et al. 2015

2015 20th International Conference on Methods and Models in Automation and Robotics (MMAR)

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In this work, we present a generic approach to optimize the design of a parametrized robot gripper including both gripper parameters and parameters of the finger geometry. We demonstrate our gripper optimization on a parallel jaw type gripper which we have parametrized in a 11 dimensional space. We furthermore present a parametrization of the grasping task and context, which is essential as input to the computation of gripper performance. We exemplify the feasibility of our approach by computing several optimized grippers on a real world industrial object in three different scenarios.

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“…This ill decision making generally occurs due to improperly-defined performance criteria and lack of knowledge about the co-influences between criteria and the functionality to be provided by neighbouring disciplines. Moulianitis et al [7] proposed a methodology for decision making in conceptual mechatronic design based upon an evaluation index including three criteria: intelligence, flexibility, and complexity. Weight factors were applied to highlight the importance of each criterion.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Decision Making for Conceptual Design of a Visual Servoing System Using Mechatronic Multi-Criteria Profile (MMP)

Mohebbi

Achiche

Baron

et al. 2014

Volume 11: Systems, Design, and Complexity

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Mechatronic systems are of increased importance in engineering and their relevance goes hand in hand with the increasing complexity of the tasks they perform. Due to the inherent complexity of mechatronic systems, a concurrent systematic and multi-objective design thinking methodology is crucial to replace the often used sequential design approach that tends to deal with the different domains separately. In this research we present a new multi-criteria profile (MMP) for mechatronic system performance evaluation in the stage of conceptual design. Based on the assessed MMP for each of generated design concepts and using a method of aggregation for interacting criteria, a global performance index will be calculated. Best mechatronic configurations are determined by maximizing this performance index. In the presented paper two nonlinear fuzzy integrals called 2-additive Choquet and Sugeno will be used for the aggregation of criteria and fitting the intuitive requirements for decision-making in the presence of interacting criteria. Finally, the effectiveness of the proposed design method alongside each of the decision making models will be validated via a case study of designing a robotic visual servoing system. The comparative simulation results for the overall system performance will also be presented for both cases of using Choquet and Sugeno integrals.

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A model for concept evaluation in design––an application to mechatronics design of robot grippers

Cited by 54 publications

References 16 publications

Task and Context Sensitive Gripper Design Learning Using Dynamic Grasp Simulation

Task and Context Sensitive Gripper Design Learning Using Dynamic Grasp Simulation

Task and context sensitive optimization of gripper design using dynamic grasp simulation

Fuzzy Decision Making for Conceptual Design of a Visual Servoing System Using Mechatronic Multi-Criteria Profile (MMP)

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