Virtual maintenance system with a two-staged ant colony optimization algorithm

Virtual reality (VR) now a day is progressively being used in our manufacturing environments. In production or maintenance processes, the most important VR application can be assembly areas as the efficiency of a production or maintenance scheme primarily depends on the assembly and disassembly (A/D) sequence, number of gripper changes and the path used in an A/D process. In this paper, a novel optimum assembly algorithm with ant colony algorithm is proposed to solve the maintenance assembly process for 3D objects and complex environments, and to find both optimal sequence and 3D path planning. Sequentially, the assembly sequence was optimized by a traditional ant colony optimization algorithm and then, the 3D path planning with the optimized sequence information was optimized by combining an ant colony algorithm enhanced by potential field concepts. Simulation results showed that proposed algorithm has faster convergence rate towards the optimal solution when compared with existing algorithms based on genetics and traditional ant colony approach.

[1]  Rajeev Sharma,et al.  Interactive evaluation of assembly sequences using augmented reality , 1999, IEEE Trans. Robotics Autom..

[2]  Holger Graf,et al.  CAD2VR or How to Efficiently Integrate VR into the Product Development Process , 2002, CAD.

[3]  Jean-Claude Latombe,et al.  Planning motions with intentions , 1994, SIGGRAPH.

[4]  Jungwon Yoon,et al.  Haptic based optimized path planning approach to virtual maintenance assembly / disassembly (MAD) , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Ahmad A. Masoud,et al.  Managing the Dynamics of a Harmonic Potential Field-Guided Robot in a Cluttered Environment , 2016, IEEE Transactions on Industrial Electronics.

[6]  M Dorigo,et al.  Ant colonies for the travelling salesman problem. , 1997, Bio Systems.

[7]  Jungwon Yoon,et al.  A novel optimal assembly algorithm for the haptic interface application of a virtual maintenance system , 2008, 2008 IEEE International Conference on Robotics and Automation.

[8]  Bijan Shirinzadeh,et al.  Solid modelling in a virtual reality environment , 2004, The Visual Computer.

[9]  Christian Bierwirth,et al.  Production Scheduling and Rescheduling with Genetic Algorithms , 1999, Evolutionary Computation.

[10]  He Xu,et al.  Towards the development of a desktop virtual reality‐based system for modular fixture configuration design , 2009 .

[11]  Narendra Ahuja,et al.  A potential field approach to path planning , 1992, IEEE Trans. Robotics Autom..

[12]  Ying-Tung Hsiao,et al.  Ant colony optimization for best path planning , 2004, IEEE International Symposium on Communications and Information Technology, 2004. ISCIT 2004..

[13]  Damien Chablat,et al.  A distributed approach for access and visibility task with a manikin and a robot in a virtual reality environment , 2003, IEEE Trans. Ind. Electron..

[14]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[15]  Li Huijun,et al.  Virtual-Environment Modeling and Correction for Force-Reflecting Teleoperation With Time Delay , 2007 .

[16]  Junfeng Wang,et al.  A novel ant colony algorithm for assembly sequence planning , 2005 .

[17]  Wolfgang Müller-Wittig,et al.  Intuitive and Precise Solid Modeling in a Virtual Reality Environment , 2003 .

[18]  Tsai-Yen Li,et al.  Assembly maintainability study with motion planning , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.