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Behaviour-Based Inverse Kinematics Solver on FPGA

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Advances in Service and Industrial Robotics (RAAD 2017)

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 49))

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Abstract

Due to their parallel nature, behaviour-based control architectures can strongly benefit from an implementation on FPGAs. The problem is, to find out the real benefit of such an implementation, since general calculations are difficult, due to the heterogeneity of such systems. In this paper, we present early results of the integration of a behaviour-based inverse kinematics solver, based on the iB2C-architecture, on a common FPGA. It is shown, that this implementation is feasible and that the resulting performance is satisfying. Further benefits are evaluated.

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References

  1. Brooks R (1986) A robust layered control system for a mobile robot. IEEE J Robot Autom RA–2(1):14–23

    Article  Google Scholar 

  2. Burattinia E, Gregoriob MD, Rossi S (2010) An adaptive oscillatory neural architecture for controlling behavior based robotic systems. In: Vellasco M, Souto MC, de Carvalho AC (eds) 10th brazilian symposium on neural networks, vol 73, pp 2829–2836

    Google Scholar 

  3. Gustafson JL (1988) Reevaluating amdahl’s law. Commun ACM 31:532–533

    Article  Google Scholar 

  4. Kongmunvattana A, Chongstitvatana P (1998) A fpga-based behavioral control system for a mobile robot. In: IEEE asia-pacific conference on circuits and systems. Microelectronics and integrating systems. IEEE, Chiangmai, pp 759–762

    Google Scholar 

  5. Li T, Lin IF, Hung TM (2002) Behavior-based fuzzy logic control for a one-on-one robot soccer competition. In: Proceedings of the 2002 IEEE international conference on fuzzy systems, FUZZ-IEEE 2002, vol 1. IEEE, Honolulu, pp 470–475

    Google Scholar 

  6. Nicolescu MN, Matarić MJ (2002) A hierarchical architecture for behavior-based robots. In: Proceedings of the first international joint conference on autonomous agents and multi-agent systems, Bologna, Italy, pp 227–233

    Google Scholar 

  7. Pluzhnikov S, Schmidt D, Hirth J, Berns K (2012) Behavior-based arm control for an autonomous bucket excavator. In: Berns K, Schindler C, Dreßler K, Jörg B, Kalmar R, Zolynski G (eds) Proceedings of the 2nd commercial vehicle technology symposium (CVT 2012). Shaker Verlag, Kaiserslautern, pp 251–261

    Google Scholar 

  8. Proetzsch M, Luksch T, Berns K (2007) The behaviour-based control architecture iB2C for complex robotic systems. In: Proceedings of the 30th annual german conference on artificial intelligence (KI), Osnabrück, Germany, pp 494–497

    Google Scholar 

  9. Proetzsch M, Luksch T, Berns K (2010) Development of complex robotic systems using the behavior-based control architecture iB2C. Robot Auton Syst 58(1):46–67. doi:10.1016/j.robot.2009.07.027

    Article  Google Scholar 

  10. Rosenblatt JK (1997) Damn: a distributed architecture for mobile navigation. J Exper Theor Artif Intell 9(2–3):339–360

    Article  Google Scholar 

  11. Sánchez DF, Munoz DM, Llanos CH, Motta JM (2010) A reconfigurable system approach to the direct kinematics of a 5 d.o.f robotic manipulator. Int J Reconfig Comput 2010 (Article ID 727909)

    Google Scholar 

  12. Schwiegelshohn F, Kästner F, Hübner M (2016) Fpga design of numerical methods for the robotic motion control task exploiting high-level synthesis. In: IEEE international conference on the science of electrical engineering (ICSEE). IEEE, Eilat (2016)

    Google Scholar 

  13. Volder JE (1959) The cordic trigonometric computing techniqu. IRE Trans Electron Comput EC–8(3):330–334

    Article  Google Scholar 

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Correspondence to Alexander Köpper .

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Köpper, A., Berns, K. (2018). Behaviour-Based Inverse Kinematics Solver on FPGA. In: Ferraresi, C., Quaglia, G. (eds) Advances in Service and Industrial Robotics. RAAD 2017. Mechanisms and Machine Science, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-319-61276-8_7

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  • DOI: https://doi.org/10.1007/978-3-319-61276-8_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61275-1

  • Online ISBN: 978-3-319-61276-8

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