Evolutionary multi criteria design optimization of robot grippers

Saravanan, R.; Ramabalan, S.; Ebenezer, N. Godwin Raja; Dharmaraja, C.

doi:10.1016/j.asoc.2008.04.001

Cited by 80 publications

(38 citation statements)

References 8 publications

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“…Genetic Algorithm (GA) is used to solve the non-linear multi-objective optimization problem. Some extension of the formulation by [1] can be found in [11], [12]. Datta and Deb [12] have also used multi-objective genetic algorithm to solve the above mentioned problem.…”

Section: Robot Gripper Designmentioning

confidence: 99%

Analysis of a seven link robot gripper with an integrated piezoelectric actuation system

Datta

Pradhan

Bhattacharya

2014

2014 13th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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Robot gripper design is an active research area due to its wide spread applicability in automation. The present work deals with the actuator analysis of a non-linear, multi-modal and multi-objective optimization problem which is originally formulated by Osyczka [1]. The previous work [1] had treated the actuator as a blackbox. In the present work, the formulation has been changed by incorporating actuator analysis into the robotic gripper problem. The piezoelectric actuating system is modeled as a stack consisting of the individual actuator elements arranged in series and parallel, leading to four different cases. With the incorporation of voltage as an input (it is related to both the actuator force and the manipulator displacement), the problem becomes more realistic and can be integrated with any real life situation. The relationships connecting the force developed and the voltage applied are arrived at by using the basic constitutive equations and the geometry of the problem. The future work aims to take up an existing optimization problem with actuator analysis and make it more realistic and wider in scope, by incorporating actuator analysis into it.

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Section: Robot Gripper Designmentioning

confidence: 99%

Analysis of a seven link robot gripper with an integrated piezoelectric actuation system

Datta

Pradhan

Bhattacharya

2014

2014 13th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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“…The robot gripper optimization problem is defined in (Saravanan et al (2009); Datta and Deb (2011)). Five objectives are formulated in these papers.…”

Section: Definition Of the Robot Gripper Optimizationmentioning

confidence: 99%

An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions

et al. 2019

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This paper proposes an improved epsilon constraint-handling mechanism, and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). The proposed constrained multi-objective evolutionary algorithm (CMOEA) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of feasible to total solutions (RFS) in the current population. In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible regions (relative to the feasible regions), and they are called LIR-CMOPs. Then the fourteen benchmarks, including LIR-CMOP1-14, are used to test MOEA/D-IEpsilon and four other decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP and C-MOEA/D. The experimental results indicate that MOEA/D-IEpsilon is significantly better than the other four CMOEAs on all of the test instances, which shows that MOEA/D-IEpsilon is more suitable for solving CMOPs with large infeasible regions. Furthermore, a real-world problem, namely the robot gripper optimization problem, is used to test the five CMOEAs.

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“…GA is used to solve the nonlinear multiobjective optimization problem. Some extensions of the formulation by Osyczka [1] can be found in [13] and [14]. Datta and Deb [14] have also used multiobjective GA to solve the above mentioned problem.…”

mentioning

confidence: 99%

Analysis and Design Optimization of a Robotic Gripper Using Multiobjective Genetic Algorithm

Datta

Pradhan

Bhattacharya

2016

IEEE Trans. Syst. Man Cybern, Syst.

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Robot gripper design is an active research area due to its wide spread applicability in automation, especially for high-precision micro-machining. This paper deals with a multiobjective optimization problem which is nonlinear, multimodal, and originally formulated. The previous work, however, had treated the actuator as a blackbox. The system model has been modified by integrating an actuator model into the robotic gripper problem. A generic actuation system (for example, a voice coil actuator) which generates force proportional to the applied voltage is considered. The actuating system is modeled as a stack consisting of the individual actuator elements arranged in series and parallel arrays in four different combinations. With the incorporation of voltage into the problem, which is related to both actuator force and manipulator displacement, the problem becomes more realistic and can be integrated with many reallife gripper simulations. Multiobjective evolutionary algorithm is used to solve the modified biobjective problem and to optimally find the dimensions of links and the joint angle of a robot gripper. A force voltage relationship can be obtained from each of the nondominated solutions which helps the user to determine the voltage to be applied depending on the application. An innovization study is further carried out to find suitable relationships between the decision variables and the objective functions.

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Evolutionary multi criteria design optimization of robot grippers

Cited by 80 publications

References 8 publications

Analysis of a seven link robot gripper with an integrated piezoelectric actuation system

Analysis of a seven link robot gripper with an integrated piezoelectric actuation system

An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions

Analysis and Design Optimization of a Robotic Gripper Using Multiobjective Genetic Algorithm

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