Tool-path generation for industrial robotic surface-based application

Abstract

Industrial robots are widely used in various applications such as machining, painting, and welding. There is a pressing need for a fast and straightforward robot programming method, especially for surface-based tasks. At present, these tasks are time-consuming and expensive, and it requires an experienced and skilled operator to program the robot for a specific task. Hence, it is essential to automate the tool-path generation in order to eliminate the manual planning. This challenging research has attracted great attention from both industry and academia. In this paper, a tool-path generation method based on a mesh model is introduced. The bounding box tree and kd-tree are adopted in the algorithm to derive the tool path. In addition, the algorithm is integrated into an offline robot programming system offering a comprehensive solution for robot modeling, simulation, as well as tool-path generation. Finally, a milling experiment is performed by creating tool paths on the surface thereby demonstrating the effectiveness of the system.

Keywords

Industrial robot / Tool path generation / Simulation / Intelligent manufacturing

References

[1] Lee S, Li C, Kim D et al (2009) The direct teaching and playback method for robotic deburring system using the adaptive force-control. In: IEEE international symposium on assembly and manufacturing, 17–20 Nov 2009, Seoul, South Korea, pp 235–241

[2] Kim HJ, Back J, Song JB. Direct teaching and playback algorithm for peg-in-hole task using impedance control. J Inst Control Robot Syst, 2009, 15(5): 538-542.

[3] Asakawa N, Toda K, Takeuchi Y. Automation of chamfering by an industrial robot; for the case of hole on free-curved surface. Robot Comput Integr Manuf, 2002, 18(5–6): 379-385.

[4] Nagata F, Kusumoto Y, Fujimoto Y, et al. Robotic sanding system for new designed furniture with free-formed surface. Robot Comput Integr Manuf, 2007, 23(4): 371-379.

[5] Buckmaster DJ, Newman WS, Somes SD (2008) Compliant motion control for robust robotic surface finishing. In: World congress on intelligent control and automation, 25–27 June 2008, Chongqing, China, pp 559–564

[6] Minetto R, Volpato N, Stolfi J, et al. An optimal algorithm for 3D triangle mesh slicing. Comput Aided Des, 2017, 92: 1-10.

[7] Sun YW, Guo DM, Jia ZY, et al. Iso-parametric tool path generation from triangular meshes for free-form surface machining. Int J Adv Manuf Technol, 2006, 28(7–8): 721-726.

[8] Ding S, Mannan M, Poo AN, et al. Adaptive iso-planar tool path generation for machining of free-form surfaces. Comput Aided Des, 2003, 35(2): 141-153.

[9] Ericson C. Real-time collision detection, 2004, Boca Raton: CRC Press.

[10] Bergen GVD. Efficient collision detection of complex deformable models using AABB trees. J Graph Tools, 1997, 2(4): 1-13.

[11] Gottschalk S, Lin MC, Manocha D (1996) OBB tree: a hierarchical structure for rapid interference detection. In: Proceedings of the 23rd annual conference on computer graphics and interactive techniques, New Orleans, LA, USA, 4–9 August, pp 171–180

[12] Klosowski JT, Held M, Mitchell JS, et al. Efficient collision detection using bounding volume hierarchies of k-dops. IEEE Trans Vis Comput Graph, 1998, 4(1): 21-36.

[13] Larsen E, Gottschalk S, Lin MC, et al. Fast distance queries with rectangular swept sphere volumes. Proc IEEE Int Conf Robot Autom, 2000, 4: 3719-3726.

[14] Quinlan S (1994) Efficient distance computation between non- convex objects. In: Proceedings of the IEEE international conference on robotics and automation, 8–13 May, San Diego, USA, pp 3324–3329

[15] Tropp O, Tal A, Shimshoni I. A fast triangle to triangle intersection test for collision detection. Comput Anim Virtual Worlds, 2006, 17(5): 527-535.

[16] Sabharwal CL, Leopold JL, McGeehan D. Triangle-triangle intersection determination and classification to support qualitative spatial reasoning. Polibits, 2013, 48: 13-22.

[17] Wald I, Havran V (2006) On building fast kd-trees for ray tracing, and on doing that in O(N log N). In: IEEE symposium on interactive ray tracing, Salt Lake City, USA, 18–20 Sept, pp 61–69

[18] Lyu H, Song X, Dai D et al (2017) Tool path interpolation and redundancy optimization of manipulator. In: The 13th IEEE conference on automation science and engineering (CASE), 20–23 Aug, Xi’an, China, pp 770–775

[19] The 3D modeling and visualization platform. http://www.anycad.net/. Accessed 30 March 2018

[20] Murray RM, Li ZX, Sastry SS, et al. A mathematical introduction to robotic manipulation, 1994, Boca Raton: Chemical Rubber Company Press.

[21] RS020N product detail. https://robotics.kawasaki.com.cn/cn1/products/robots/small-medium-payloads/RS020N/. Accessed 2 Sept 2018

[22] Marvie controller product detail. http://www.googoltech.com.cn/product/mcp/marvie/142/. Accessed 2 Sept 2018

Funding

Research Grants Council, University Grants Committee http://dx.doi.org/10.13039/501100002920(16205915)

Innovation and Technology Commission (HK)(TS/216/17FP)