Analysis and multi-objective optimization of a kind of teaching manipulator

Abstract Designing and setting manipulator trajectories in a programming system can be a tedious and time-consuming task for manufacturers. In this paper, one kind of six degree-of-freedom (DOF) teaching manipulator is designed and developed for conveniently setting and recording trajectories for industrial robots. A constrained multi-objective optimization problem is formulated to optimize the design of the teaching manipulator. Two performance indexes, i.e. the magnitude of the peak operating force and difference between the maximum and minimum magnitude of operating forces are adopted as the objectives. Two PPS-based (push and pull search) algorithms, including PPS-MOEA/D and PPS-M2M, are suggested to solve the formulated CMOP. Several state-of-the-art CMOEAs, including MOEA/D-ACDP, MOEA/D-CDP, NSGA-II-CDP and CM2M, are also tested. The experimental results indicate that PPS-MOEA/D has the best performance among the six compared algorithms, and the PPS-based methods as a group outperform their counterparts without adopting the PPS framework, which demonstrates the superiority of the PPS framework for solving real-world optimization problems.

[1]  John Doherty,et al.  Hierarchical Surrogate-Assisted Evolutionary Multi-Scenario Airfoil Shape Optimization , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

[2]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[3]  Marco Laumanns,et al.  Combining Convergence and Diversity in Evolutionary Multiobjective Optimization , 2002, Evolutionary Computation.

[4]  Mehdi Tarkian.  Design Reuse and Automation : On High Level CAD Modeling for Multidisciplinary Design and Optimization , 2009 .

[5]  Oussama Khatib,et al.  Springer Handbook of Robotics , 2007, Springer Handbooks.

[6]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[7]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[8]  M. Spong,et al.  Robot Modeling and Control , 2005 .

[9]  Kalyanmoy Deb,et al.  Multi-objective design and analysis of robot gripper configurations using an evolutionary-classical approach , 2011, GECCO '11.

[10]  Marcus Pettersson,et al.  Drive Train Optimization for Industrial Robots , 2009, IEEE Transactions on Robotics.

[11]  J. Denavit,et al.  A kinematic notation for lower pair mechanisms based on matrices , 1955 .

[12]  Marcus Pettersson,et al.  Design Optimization in Industrial Robotics Methods and Algorithms for Drive Train Design , 2008 .

[13]  Bishakh Bhattacharya,et al.  A piezoelectric model based multi-objective optimization of robot gripper design , 2016 .

[14]  Tetsuyuki Takahama,et al.  Constrained Optimization by the ε Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[15]  Michael Rygaard Hansen,et al.  Design optimization on the drive train of a light-weight robotic arm , 2011 .

[16]  Erik D. Goodman,et al.  MOEA/D with Angle-based Constrained Dominance Principle for Constrained Multi-objective Optimization Problems , 2018, Appl. Soft Comput..

[17]  James U. Korein,et al.  Robotics , 2018, IBM Syst. J..

[18]  Bishakh Bhattacharya,et al.  Analysis and Design Optimization of a Robotic Gripper Using Multiobjective Genetic Algorithm , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[19]  Qingsong Xu,et al.  GA-Based Multi-Objective Optimal Design of a Planar 3-DOF Cable-Driven Parallel Manipulator , 2006, 2006 IEEE International Conference on Robotics and Biomimetics.

[20]  Jordan B. Pollack,et al.  Automatic design and manufacture of robotic lifeforms , 2000, Nature.

[21]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[22]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[23]  Hai-Lin Liu,et al.  A Constrained Multi-Objective Evolutionary Algorithm Based on Boundary Search and Archive , 2016, Int. J. Pattern Recognit. Artif. Intell..

[24]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[25]  Lelai Zhou,et al.  A New Approach to Design of a Lightweight Anthropomorphic Arm for Service Applications , 2015 .

[26]  S. Ivvan Valdez,et al.  Comparison Between a Concurrent and a Sequential Optimization Methodology for Serial Manipulators Using Metaheuristics , 2018, IEEE Transactions on Industrial Informatics.

[27]  Qingfu Zhang,et al.  Push and Pull Search for Solving Constrained Multi-objective Optimization Problems , 2017, Swarm Evol. Comput..

[28]  Xiaolong Feng,et al.  Multidisciplinary Design Optimization of Modular Industrial Robots by Utilizing High Level CAD Templates , 2012 .

[29]  Muhammad Asif Jan,et al.  A study of two penalty-parameterless constraint handling techniques in the framework of MOEA/D , 2013, Appl. Soft Comput..

[30]  Mehdi Tarkian,et al.  Design Automation for Multidisciplinary Optimization : A High Level CAD Template Approach , 2012 .

[31]  Erik D. Goodman,et al.  An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions , 2017, Soft Computing.