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3D Robot Formations Path Planning with Fast Marching Square

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Abstract

This work presents a path planning algorithm for 3D robot formations based on the standard Fast Marching Square (FM2) path planning method. This method is enlarged in order to apply it to robot formations motion planning. The algorithm is based on a leader-followers scheme, which means that the reference pose for the follower robots is defined by geometric equations that place the goal pose of each follower as a function of the leader’s pose. Besides, the Frenet-Serret frame is used to control the orientation of the formation. The algorithm presented allows the formation to adapt its shape so that the obstacles are avoided. Additionally, an approach to model mobile obstacles in a 3D environment is described. This model modifies the information used by the FM2 algorithm in favour of the robots to be able to avoid obstacles. The shape deformation scheme allows to easily change the behaviour of the formation. Finally, simulations are performed in different scenarios and a quantitative analysis of the results has been carried out. The tests show that the proposed shape deformation method, in combination with the FM2 path planner, is robust enough to manage autonomous movements through an indoor 3D environment.

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References

  1. Martin, M., Klupar, P., Kilberg, S., Winter, J.: TechSat 21 and revolutionizing space missions using microsatellites. In: AIAA/USU Conference on Small Satellites (2001)

  2. Dewan, A., Mahendran, A., Soni, N., Krishna, K.M.: Heterogeneous UGV-MAV exploration using integer programming. In: AIAA/USU Conference on Small Satellites on Intelligent Robots and Systems (2013)

  3. Hauert, S., Zufferey, J.C., Floreano, D.: Reverse-engineering of artificially evolved controllers for swarms of robots. In: IEEE Congress on Evolutionary Computation (2009)

  4. Acevedo, J.J., Arrue, B.C., Maza, I., Ollero, A.: Cooperative large area surveillance with a team of aerial mobile robots for long endurance missions. J. Intell. Robot. Syst. 70(1–4), 329–345 (2013)

    Article  Google Scholar 

  5. Likhachev, M., Keller, J., Kumar, V., Dobrokhodov, V., Jones, K., Wurz, J., Kaminer, I.: Planning for opportunistic surveillance with multiple robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5750–5757. Tokyo (2013)

  6. Bouabdallah, S.: Design and Control of Quadrotors with Applicationto Autonomous Flying, Theses 3727. École polytechnique fédérale de Lausanne (2007)

  7. Hrabar, S.: Reactive obstacle avoidance for rotorcraft UAVs. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4967–4974. San Francisco (2011)

  8. Shen, S., Michael, N., Kumar, V.: 3D estimation and control for autonomous flight with constrained computation. In: IEEE International Conference of Robotics and Automation. Shanghai (2011)

  9. Hino, T.: Simple formation control scheme tolerant to communication failures for small unmanned air vehicles. In: International Congress of the Aeronautical Sciences. Nice (2010)

  10. Balch, T., Arkin, R.C.: Behaviour-based formation control for multi-robot teams. IEEE Trans. Robot. Autom. 14(6), 926–939 (1998)

    Article  Google Scholar 

  11. Naffin, D., Sukhatme, G.: Negotiated formations. In: Proceedings of the International Conference on Intelligent Autonomous Systems, pp. 181–190. Amsterdam (2004)

  12. Fredslund, J., Matari, M.J.: A general algorithm for robot formations using local sensing and minimal communication. IEEE Trans. Robot. Autom. 18(5), 837–846 (2002)

    Article  Google Scholar 

  13. Lemay, M, Michaud, F., Létourneau, D., Valin, J.-M.: Autonomous initialization of robot formations. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 3, pp. 3018–3023. New Orleans (2004)

  14. Ogren, P., Egerstedt, M., Hu, X.: A control Lyapunov function approach to multiagent coordination. IEEE Trans. Robot. Autom. 18(5), 847–851 (2002)

    Article  Google Scholar 

  15. Zhang, M., Shen, Y., Wang, Q., Wang, Y.: Dynamic artificial potential field based multi-robot formation control. In: IEEE Instrumentation and Measurement Technology Conference, pp. 1530–1534. Austin (2010)

  16. Cao, Z., Xie, L., Zhang, B., Wang, S., Tan, M.: Formation constrained multi-robot system in unknown environments. Proceedings Conference on Robotics and Automation, vol. 1, pp. 735–740 (2003)

  17. Tan, K.H., Lewis, M.A.: Virtual structures for high-precision cooperative mobile robotic control. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1, pp. 132–139 (1996)

  18. Ren, W., Beard, R.W.: Decentralized scheme for spacecraft formation flying via the virtual structure approach. AIAA J. Guid. Control. Dyn. 1(1), 73–82 (2004)

    Article  Google Scholar 

  19. Ahmad, A., Nascimento, T., Conceiçao, A.G.S., Moreira, A.P., Lima, P.: Perception-driven multi-robot formation control. In: IEEE International Conference on Robotics and Automation, pp. 1851–1856. Karlsruhe (2013)

  20. Kanjanawanishkul, K., Zell, A.: A model-predictive approach to formation control of omnidirectional mobile robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2771–2776. Nice (2008)

  21. Álvarez, D., Gómez, J.V., Garrido, S., Moreno, L.: 3D Robot formations planning with fast marching square. In: IEEE International Conference on Autonomous Robot Systems and Competitions. Espinho (2014)

  22. Yu, W., Chen, G., Cao, M.: Distributed leader-follower flocking control for multi-agent dynamical systems with time-varying velocities. Syst. Control Lett. 59(9), 543–552 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  23. Garrido, S., Moreno, L., Lima, P.: Robot formation motion planning using fast marching. J. Robot. Auton. Syst. 59(9), 675–683 (2011)

    Article  Google Scholar 

  24. Álvarez, D., Lumbier, A., Gómez, J.V., Garrido, S., Moreno, L.: Precision grasp planning with Gifu hand III based on fast marching square. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4549–4554. Tokyo (2013)

  25. Valero, A., Gómez, J.V., Garrido, S., Moreno, L.: The path to efficiency. IEEE Robot. Autom. Mag. 20(4), 111–120 (2013)

    Article  Google Scholar 

  26. Gómez, J.V., Lumbier, A., Garrido, S., Moreno, L.: Planning Robot Formations with Fast Marching Square Including Uncertainty Conditions. J. Robot. Auton. Syst. 61(2), 137–152 (2013)

    Article  Google Scholar 

  27. Sethian, J.A.: A fast marching level set method for monotonically advancing fronts. In: Proceedings National Academy of Science, vol. 93, pp. 1591–1595 (1996)

  28. Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79, 12–49 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  29. Garrido, S., Moreno, L., Abderrahim, M., Blanco, D.: FM2: A Real-time Sensor-based Feedback Controller for Mobile Robots. Intl. J. Robot. Autom. 24(1), 3169–3192 (2009)

    Google Scholar 

  30. Yatziv, L., Bartesaghi, A., Sapiro, G.: A fast O(n) implementation of the fast marching algorithm. J. Comput. Phys. 212(1), 393–399 (2005)

    Google Scholar 

  31. Garrido, S., Moreno, L., Gómez, J.V., Lima, P.U.: General path planning methodology for leader-followers based robot formations. Intl. J. Adv. Robot. Syst. 10(64), 1–10 (2013)

    Article  Google Scholar 

  32. Gómez, J.V., Garrido, S., Moreno, L.: Adaptive robot formations using fast marching square working under uncertainty conditions. In: IEEE Workshop on Advanced Robotics and Its Social Impacts, pp. 68–71. California (2011)

  33. Frenet, F.: Sur les Courbes à Double Courbure. Journal de Mathématiques Pures et Appliquées 1(17), 437–447 (1852)

    Google Scholar 

  34. Serret, J.A.: Sur Quelques Formules Relatives à la Théorie des Courbes à Double Courbure. Journal de Mathématiques Pures et Appliquées 1(16), 193–207 (1851)

    Google Scholar 

  35. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893. San Diego (2005)

  36. Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern. Anal. Mach. Intell. 32(9), 1627–1645 (2009)

    Article  Google Scholar 

  37. Volpe, R., Khosla, P.: Manipulator control with superquadric artificial potential functions: Theory and experiments. IEEE Trans. Syst. Man Cybern. 20, 1423–1436 (1990)

    Article  Google Scholar 

  38. El-Khoury, S., Sahbani, A.: A new strategy combining empirical and analytical approaches for grasping unknown 3d objects. J. Robot. Auton. Syst. 58(5), 497–507 (2010)

    Article  Google Scholar 

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Álvarez, D., Gómez, J.V., Garrido, S. et al. 3D Robot Formations Path Planning with Fast Marching Square. J Intell Robot Syst 80, 507–523 (2015). https://doi.org/10.1007/s10846-015-0187-1

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  • DOI: https://doi.org/10.1007/s10846-015-0187-1

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