Enhanced online programming for industrial robots

The use of robots and automation levels in the industrial sector is expected to grow, and is driven by the on-going need for lower costs and enhanced productivity. The manufacturing industry continues to seek ways of realizing enhanced production, and the programming of articulated production robots has been identified as a major area for improvement. However, realizing this automation level increase requires capable programming and control technologies. Many industries employ offline-programming which operates within a manually controlled and specific work environment. This is especially true within the high-volume automotive industry, particularly in high-speed assembly and component handling. For small-batch manufacturing and small to medium-sized enterprises, online programming continues to play an important role, but the complexity of programming remains a major obstacle for automation using industrial robots. Scenarios that rely on manual data input based on real world obstructions require that entire production systems cease for significant time periods while data is being manipulated, leading to financial losses. The application of simulation tools generate discrete portions of the total robot trajectories, while requiring manual inputs to link paths associated with different activities. Human input is also required to correct inaccuracies and errors resulting from unknowns and falsehoods in the environment. This study developed a new supported online robot programming approach, which is implemented as a robot control program. By applying online and offline programming in addition to appropriate manual robot control techniques, disadvantages such as manual pre-processing times and production downtimes have been either reduced or completely eliminated. The industrial requirements were evaluated considering modern manufacturing aspects. A cell-based Voronoi generation algorithm within a probabilistic world model has been introduced, together with a trajectory planner and an appropriate human machine interface. The robot programs so achieved are comparable to manually programmed robot programs and the results for a Mitsubishi RV-2AJ five-axis industrial robot are presented. Automated workspace analysis techniques and trajectory smoothing are used to accomplish this. The new robot control program considers the working production environment as a single and complete workspace. Non-productive time is required, but unlike previously reported approaches, this is achieved automatically and in a timely manner. As such, the actual cell-learning time is minimal.

[1]  Thomas A. Fuhlbrigge,et al.  Automated industrial robot path planning for spray painting process: A review , 2008, 2008 IEEE International Conference on Automation Science and Engineering.

[2]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.

[3]  A.G. Pipe,et al.  Nonholonomic Motion Planning by Means of Particles , 2006, 2006 International Conference on Mechatronics and Automation.

[4]  Charles W. Warren,et al.  Global path planning using artificial potential fields , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[5]  Hongyan Wang,et al.  Nonuniform Discretization for Kinodynamic Motion Planning and its Applications , 2000, SIAM J. Comput..

[6]  P. Strevens.  Iii , 1985 .

[7]  Marina L. Gavrilova,et al.  Roadmap-Based Path Planning - Using the Voronoi Diagram for a Clearance-Based Shortest Path , 2008, IEEE Robotics & Automation Magazine.

[8]  Yoshifumi Kitamura,et al.  3-D path planning in a dynamic environment using an octree and an artificial potential field , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[9]  L. Dubins.  On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents , 1957 .

[10]  Heinz Wörn,et al.  Online path planning with optimal C-space discretization , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[11]  Ellips Masehian,et al.  A voronoi diagram-visibility graph-potential field compound algorithm for robot path planning , 2004, J. Field Robotics.

[12]  Yoram Koren,et al.  Potential field methods and their inherent limitations for mobile robot navigation , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[13]  John Norrish,et al.  Recent Progress on Programming Methods for Industrial Robots , 2010, ISR/ROBOTIK.

[14]  Mark H. Overmars,et al.  A Comparative Study of Probabilistic Roadmap Planners , 2002, WAFR.

[15]  Kyu Ho Park,et al.  A fast path planning by path graph optimization , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[16]  A. K. Garga,et al.  A neural network approach to the construction of Delaunay tessellation of points in R/sup d/ , 1994 .

[17]  Michael Jenkin,et al.  Exploiting Hierarchical Probabilistic Motion Planning for Robot Reachable Workspace Estimation , 2011 .

[18]  Magnus Egerstedt,et al.  An exploration strategy by constructing Voronoi diagrams with provable completeness , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[19]  Gerd Hirzinger,et al.  Capturing robot workspace structure: representing robot capabilities , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Marina L. Gavrilova,et al.  Voronoi diagram in optimal path planning , 2007, 4th International Symposium on Voronoi Diagrams in Science and Engineering (ISVD 2007).

[21]  Irene Gargantini,et al.  Linear octtrees for fast processing of three-dimensional objects , 1982, Comput. Graph. Image Process..

[22]  Peter Norvig,et al.  Artificial intelligence - a modern approach, 2nd Edition , 2003, Prentice Hall series in artificial intelligence.

[23]  M. Overmars,et al.  Approximating generalized Voronoi diagrams in any dimension , 1995 .

[24]  Joost N. Kok,et al.  Motion planning using a colored Kohonen network , 1993 .

[25]  Li Tao,et al.  Virtual engineering: challenges and solutions for intuitive offline programming for industrial robot , 2008, 2008 IEEE Conference on Robotics, Automation and Mechatronics.

[26]  Pierre Payeur.  Improving robot path planning efficiency with probabilistic virtual environment models , 2004, 2004 IEEE Symposium on Virtual Environments, Human-Computer Interfaces and Measurement Systems, 2004. (VCIMS)..

[27]  Steven Fortune,et al.  A sweepline algorithm for Voronoi diagrams , 1986, SCG '86.

[28]  Aude Billard,et al.  Special Issue on Robot Learning by Observation, Demonstration, and Imitation , 2007, IEEE Trans. Syst. Man Cybern. Part B.