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Real-Time and Efficient Collision Avoidance Planning Approach for Safe Human-Robot Interaction

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Abstract

With the rapid development of robot perception and planning technology, robots are gradually getting rid of fixed fences and working closely with humans in a shared workspace. The safety of human-robot coexistence becomes critical. Although various safety-related motion planning methods have been proposed to prevent robots from colliding with obstacles, collision avoidance planning in highly dynamic environments is still an open problem. In this paper, we propose a robust and efficient collision avoidance planning method that generates a collision-free trajectory in real-time by leveraging the complementary strengths of the potential field and optimization. Our approach starts with a new repulsive force generation method that quickly generates collision avoidance actions even if obstacles are moving faster than the robot. To ensure that the robot avoids collisions while converging toward the goal, an optimization method based on quadratic programming is designed to minimize the deviation of the post-optimized trajectory from the reference trajectory by fusing the whole body collision avoidance constraints and constraints dimensionality reduction. Finally, a closed-loop safety protection framework is presented, including obstacle perception, collision avoidance planning, and multi-task optimization. Compared with the existing state-of-the-art collision avoidance planners as well as advanced trajectory optimization methods, our method can generate a shorter collision-free trajectory in less time with a higher success rate. Detailed simulation comparison experiments, as well as real-world comparison experiments, are reported to verify the effectiveness of our method.

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Code generated or used during the study is available from the corresponding author on reasonable request.

References

  1. Krüger, J., Lien, T.K., Verl, A.: Cooperation of human and machines in assembly lines. CIRP Ann. 58(2), 628–646 (2009). https://doi.org/10.1016/j.cirp.2009.09.009

    Article  Google Scholar 

  2. Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O.: Progress and prospects of the human-robot collaboration. Auton. Robot. 42(5), 957–975 (2018). https://doi.org/10.1007/s10514-017-9677-2

    Article  Google Scholar 

  3. Haddadin, S., De Luca, A., Albu-Schäffer, A.: Robot collisions: A survey on detection, isolation, and identification. IEEE Trans. Robot. 33(6), 1292–1312 (2017). https://doi.org/10.1109/TRO.2017.2723903

    Article  Google Scholar 

  4. Li, S., Han, K., Li, X., Zhang, S., Xiong, Y., Xie, Z.: Hybrid trajectory replanning-based dynamic obstacle avoidance for physical human-robot interaction. J. Intell. Robot. Syst. 103(3), 1–14 (2021). https://doi.org/10.1007/s10846-021-01510-2

    Article  Google Scholar 

  5. LaValle, S.M.: Rapidly-exploring random trees : a new tool for path planning. The annual research report (1998)

  6. Kuffner, J.J., LaValle, S.M.: Rrt-connect: An efficient approach to single-query path planning. In: Proceedings 2000 IEEE International Conference on Robotics and Automation., vol. 2, pp. 995–10012 (2000). https://doi.org/10.1109/ROBOT.2000.844730

  7. Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30(7), 846–894 (2011). https://doi.org/10.1177/0278364911406761

    Article  MATH  Google Scholar 

  8. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: Proceedings 1985 IEEE International Conference on Robotics and Automation, vol. 2, pp. 500–505 (1985). https://doi.org/10.1109/ROBOT.1985.1087247

  9. Flacco, F., Kröger, T., De Luca, A., Khatib, O.: A depth space approach to human-robot collision avoidance. In: 2012 IEEE International Conference on Robotics and Automation, pp. 338–345 (2012). https://doi.org/10.1109/ICRA.2012.6225245

  10. Lin, H., Fan, Y., Tang, T., Tomizuka, M.: Human guidance programming on a 6-dof robot with collision avoidance. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2676–2681 (2016). https://doi.org/10.1109/IROS.2016.7759416

  11. Kalakrishnan, M., Chitta, S., Theodorou, E., Pastor, P., Schaal, S.: Stomp: Stochastic trajectory optimization for motion planning. In: 2011 IEEE International Conference on Robotics and Automation, pp. 4569–4574 (2011). https://doi.org/10.1109/ICRA.2011.5980280

  12. Zucker, M., Ratliff, N., Dragan, A.D., Pivtoraiko, M., Klingensmith, M., Dellin, C.M., Bagnell, J.A., Srinivasa, S.S.: Chomp: Covariant hamiltonian optimization for motion planning. Int. J. Robot. Res. 32(9–10), 1164–1193 (2013). https://doi.org/10.1177/0278364913488805

    Article  Google Scholar 

  13. Schulman, J., Ho, J., Lee, A., Awwal, I., Bradlow, H., Abbeel, P.: Finding locally optimal, collision-free trajectories with sequential convex optimization. (2013). https://doi.org/10.15607/RSS.2013.IX.031

  14. Mukadam, M., Dong, J., Yan, X., Dellaert, F., Boots, B.: Continuous-time gaussian process motion planning via probabilistic inference. Int. J. Robot. Res. 37(11), 1319–1340 (2018)

    Article  Google Scholar 

  15. Liu, C., Tomizuka, M.: Algorithmic safety measures for intelligent industrial co-robots. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 3095–3102 (2016). https://doi.org/10.1109/ICRA.2016.7487476

  16. Tarbouriech, S., Suleiman, W.: On bisection continuous collision checking method: Spherical joints and minimum distance to obstacles. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 7613–7619 (2018). https://doi.org/10.1109/ICRA.2018.8463199

  17. Safeea, M., Neto, P., Bearee, R.: Efficient calculation of minimum distance between capsules and its use in robotics. IEEE Access 7, 5368–5373 (2019). https://doi.org/10.1109/ACCESS.2018.2889311

    Article  Google Scholar 

  18. Han, D., Nie, H., Chen, J., Chen, M.: Dynamic obstacle avoidance for manipulators using distance calculation and discrete detection. Robotics and Computer-Integrated Manufacturing 49, 98–104 (2018)

    Article  Google Scholar 

  19. Mronga, D., Knobloch, T., de Gea Fernández, J., Kirchner, F.: A constraint-based approach for human-robot collision avoidance. Adv. Robot. 34(5), 265–281 (2020)

    Article  Google Scholar 

  20. Kappler, D., Meier, F., Issac, J., Mainprice, J., Cifuentes, C.G., Wüthrich, M., Berenz, V., Schaal, S., Ratliff, N., Bohg, J.: Real-time perception meets reactive motion generation. IEEE Robot. Autom. Lett. 3(3), 1864–1871 (2018)

    Article  Google Scholar 

  21. Han, L., Gao, F., Zhou, B., Shen, S.: Fiesta: Fast incremental euclidean distance fields for online motion planning of aerial robots. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4423–4430 (2019). IEEE

  22. Meguenani, A., Padois, V., Da Silva, J., Hoarau, A., Bidaud, P.: Energy based control for safe human-robot physical interaction. In: 2016 International Symposium on Experimental Robotics, vol. 1 (2017). Springer

  23. Chen, J., Song, K.: Collision-free motion planning for human-robot collaborative safety under cartesian constraint. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 4348–4354 (2018). https://doi.org/10.1109/ICRA.2018.8460185

  24. Kinects Human Tracking. https://github.com/kuka-isir/kinects_human_tracking

  25. Flacco, F., Kroeger, T., De Luca, A., Khatib, O.: A depth space approach for evaluating distance to objects. J. Intell. Robot. Syst. 80(1), 7–22 (2015). https://doi.org/10.1007/s10846-014-0146-2

    Article  Google Scholar 

  26. Nascimento, H., Mujica, M., Benoussaad, M.: Collision avoidance in human-robot interaction using kinect vision system combined with robot’s model and data. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 10293–10298 (2020). https://doi.org/10.1109/IROS45743.2020.9341248

  27. Tulbure, A., Khatib, O.: Closing the loop: Real-time perception and control for robust collision avoidance with occluded obstacles. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5700–5707 (2020). https://doi.org/10.1109/IROS45743.2020.9341663

  28. Lin, H.-C., Liu, C., Fan, Y., Tomizuka, M.: Real-time collision avoidance algorithm on industrial manipulators. In: 2017 IEEE Conference on Control Technology and Applications (CCTA), pp. 1294–1299 (2017). https://doi.org/10.1109/CCTA.2017.8062637

  29. Lacevic, B., Rocco, P.: Kinetostatic danger field - a novel safety assessment for human-robot interaction. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2169–2174 (2010). https://doi.org/10.1109/IROS.2010.5649124

  30. Lacevic, B., Rocco, P., Zanchettin, A.M.: Safety assessment and control of robotic manipulators using danger field. IEEE Trans. Robot. 29(5), 1257–1270 (2013). https://doi.org/10.1109/TRO.2013.2271097

    Article  Google Scholar 

  31. Zanchettin, A.M., Lacevic, B., Rocco, P.: A novel passivity-based control law for safe human-robot coexistence. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2276–2281 (2012). https://doi.org/10.1109/IROS.2012.6385797

  32. Parigi Polverini, M., Zanchettin, A.M., Rocco, P.: Real-time collision avoidance in human-robot interaction based on kinetostatic safety field. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4136–4141 (2014). https://doi.org/10.1109/IROS.2014.6943145

  33. Parigi Polverini, M., Zanchettin, A.M., Rocco, P.: A computationally efficient safety assessment for collaborative robotics applications. Robot. Comput.-Integr. Manuf. 46, 25–37 (2017). https://doi.org/10.1016/j.rcim.2016.11.002

    Article  Google Scholar 

  34. Zanchettin, A.M., Rocco, P.: Path-consistent safety in mixed human-robot collaborative manufacturing environments. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1131–1136 (2013). https://doi.org/10.1109/IROS.2013.6696492

  35. Zanchettin, A.M., Ceriani, N.M., Rocco, P., Ding, H., Matthias, B.: Safety in human-robot collaborative manufacturing environments: Metrics and control. IEEE Trans. Autom. Sci. Eng. 13(2), 882–893 (2016). https://doi.org/10.1109/TASE.2015.2412256

    Article  Google Scholar 

  36. Zanchettin, A.M., Rocco, P.: Motion planning for robotic manipulators using robust constrained control. Control Eng. Practice 59, 127–136 (2017). https://doi.org/10.1016/j.conengprac.2016.11.010

    Article  Google Scholar 

  37. Ragaglia, M., Zanchettin, A.M., Rocco, P.: Trajectory generation algorithm for safe human-robot collaboration based on multiple depth sensor measurements. Mechatronics 55, 267–281 (2018). https://doi.org/10.1016/j.mechatronics.2017.12.009

    Article  Google Scholar 

  38. Wang, C., Bingham, J., Tomizuka, M.: Trajectory splitting: A distributed formulation for collision avoiding trajectory optimization. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 8113–8120 (2021). https://doi.org/10.1109/IROS51168.2021.9636846

  39. Alwala, K.V., Mukadam, M.: Joint sampling and trajectory optimization over graphs for online motion planning. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4700–4707 (2021). https://doi.org/10.1109/IROS51168.2021.9636064

  40. Brito, B., Floor, B., Ferranti, L., Alonso-Mora, J.: Model predictive contouring control for collision avoidance in unstructured dynamic environments. IEEE Robot. Autom. Lett. 4(4), 4459–4466 (2019). https://doi.org/10.1109/LRA.2019.2929976

    Article  Google Scholar 

  41. Luis, C.E., Vukosavljev, M., Schoellig, A.P.: Online trajectory generation with distributed model predictive control for multi-robot motion planning. IEEE Robot. Autom. Lett. 5(2), 604–611 (2020). https://doi.org/10.1109/LRA.2020.2964159

    Article  Google Scholar 

  42. Gaertner, M., Bjelonic, M., Farshidian, F., Hutter, M.: Collision-free mpc for legged robots in static and dynamic scenes. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 8266–8272 (2021). https://doi.org/10.1109/ICRA48506.2021.9561326

  43. Realtime URDF Filter. https://github.com/blodow/realtime_urdf_filter

  44. Liu, H., Qu, D., Xu, F., Zou, F., Song, J., Jia, K.: Approach for accurate calibration of rgb-d cameras using spheres. Opt. Express 28(13), 19058–19073 (2020). https://doi.org/10.1364/OE.392414

    Article  Google Scholar 

  45. Rusu, R.B., Cousins, S.: 3d is here: Point cloud library (pcl). In: 2011 IEEE International Conference on Robotics and Automation, pp. 1–4 (2011). https://doi.org/10.1109/ICRA.2011.5980567

  46. Chen, S.Y.: Kalman filter for robot vision: A survey. IEEE Trans. Ind. Electron. 59(11), 4409–4420 (2012). https://doi.org/10.1109/TIE.2011.2162714

    Article  Google Scholar 

  47. Richards, F..J..: A flexible growth function for empirical use. Journal of Experimental Botany 10(29), 290–300 (1959)

    Article  Google Scholar 

  48. Otte, M., Frazzoli, E.: Rrtx: Asymptotically optimal single-query sampling-based motion planning with quick replanning. Int. J. Robot. Res. 35(7), 797–822 (2016)

    Article  Google Scholar 

  49. Kröger, T., Wahl, F.M.: Online trajectory generation: Basic concepts for instantaneous reactions to unforeseen events. IEEE Trans. Robot. 26(1), 94–111 (2010). https://doi.org/10.1109/TRO.2009.2035744

    Article  Google Scholar 

  50. Zhou, B., Pan, J., Gao, F., Shen, S.: Raptor: Robust and perception-aware trajectory replanning for quadrotor fast flight. IEEE Trans. Robot. 37(6), 1992–2009 (2021). https://doi.org/10.1109/TRO.2021.3071527

    Article  Google Scholar 

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Funding

This work was supported in part by the Science and Technology Plan of Liaoning Province-Major Industrial Project under Grant 2019JH1/10100005, in part by the National Key R&D Program of China under Grant 2017YFB1301100, and in part by the Xingliao Talents Program of Liaoning Province under Grant XLYC1807110, and in part by the “Hundreds, Thousands, Thousands of Talents Project” Funded Project of Liaoning Province under Grant 2020921001.

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Authors and Affiliations

  1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, 110169, Shenyang, China

    Hongyan Liu, Daokui Qu, Fang Xu, Kai Jia & Jilai Song

  2. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, 110169, Shenyang, China

    Hongyan Liu, Daokui Qu, Fang Xu, Kai Jia & Jilai Song

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Hongyan Liu

  4. SIASUN Robot & Automation CO.,Ltd, Shenyang, 110169, China

    Daokui Qu, Fang Xu, Zhenjun Du, Kai Jia, Jilai Song & Mingmin Liu

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  1. Hongyan Liu
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Contributions

Conceptualization: Hongyan Liu; Methodology: Hongyan Liu; Formal analysis and investigation: Xiaofeng Wang; Writing - original draft preparation: Hongyan Liu; Writing - review and editing: Zhenjun Du, Jilai Song, Mingmin Liu; Funding acquisition: Zhenjun Du; Resources: Fang Xu, Zhenjun Du; Supervision: Daokui Qu, Fang Xu.

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Correspondence to Hongyan Liu.

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Liu, H., Qu, D., Xu, F. et al. Real-Time and Efficient Collision Avoidance Planning Approach for Safe Human-Robot Interaction. J Intell Robot Syst 105, 93 (2022). https://doi.org/10.1007/s10846-022-01687-0

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