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Risk-Aware Path Planning Under Uncertainty in Dynamic Environments

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Abstract

This study develops a novel sampling-based path planning approach, simultaneously achieving uncertainty reduction of localization and avoidance of dynamic obstacles. The proposed path planner can generate a set of path primitives and the path selection takes into account the localization uncertainty, the collision-risk, and the cost-to-go to the target area. The weights of these quantities for selecting an optimal path are tuned adaptively by using the entropy weight method. In the process of path primitive generation, we propose an adaptive planning horizon scheme that can generate a longer path with lower localization uncertainty. Particularly, to further reduce the localization uncertainty of the path primitive, we propose a sampling strategy that is capable of biasing the sampling points to the information-rich areas. To reduce the collision-risk, we propose to calculate the probability of collision by taking the uncertainty of both the robot and the dynamic objects into consideration. The proposed approach and its key components are verified in extensive experiments in both simulation and real-world environments. The proposed method is demonstrated to be capable of efficiently guiding the robot to the designated location with lower localization uncertainty and higher success rate in obstacle avoidance.

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References

  1. Bopardikar, S.D., Englot, B., Speranzon, A.: Multiobjective path planning: Localization constraints and collision probability. IEEE Trans. Robot. 31(3), 562–577 (2015)

    Article  Google Scholar 

  2. Wang, C., Meng, L., She, S., Mitchell, I.M., Li, T., Tung, F., Wan, W., Meng, M. Q. -H., de Silva, C.W.: Autonomous mobile robot navigation in uneven and unstructured indoor environments. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 109–116. IEEE (2017)

  3. Li, Y., Cui, R., Li, Z., Xu, D.: Neural network approximation based near-optimal motion planning with kinodynamic constraints using rrt. IEEE Trans. Ind. Electron. 65(11), 8718–8729 (2018)

    Article  Google Scholar 

  4. Wang, Y., Zhao, Y., Bortoff, S.A., Ueda, K.: A real-time energy-optimal trajectory generation method for a servomotor system. IEEE Trans. Ind. Electron. 62(2), 1175–1188 (2014)

    Article  Google Scholar 

  5. Kim, Y., Kim, B.K.: Time-optimal trajectory planning based on dynamics for differential-wheeled mobile robots with a geometric corridor. IEEE Trans. Ind. Electron. 64(7), 5502–5512 (2017)

    Article  Google Scholar 

  6. Sun, Y., Liu, M., Meng, M. Q. -H.: Improving rgb-d slam in dynamic environments: A motion removal approach. Robot. Auton. Syst. 89, 110–122 (2017)

    Article  Google Scholar 

  7. Van Den Berg, J., Patil, S., Alterovitz, R.: Motion planning under uncertainty using differential dynamic programming in belief space. In: Robotics Research, pp 473–490. Springer (2017)

  8. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: Autonomous Robot Vehicles, pp 396–404. Springer (1986)

  9. Borenstein, J., Koren, Y.: The vector field histogram-fast obstacle avoidance for mobile robots. IEEE Trans. Robot. Autom. 7(3), 278–288 (1991)

    Article  Google Scholar 

  10. Liu, Z., Jiang, Z., Xu, T., Cheng, H., Xie, Z., Lin, L.: Avoidance of high-speed obstacles based on velocity obstacles. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp 7624–7630. IEEE (2018)

  11. Snape, J., Van Den Berg, J., Guy, S.J., Manocha, D.: The hybrid reciprocal velocity obstacle. IEEE Trans. Robot. 27(4), 696–706 (2011)

    Article  Google Scholar 

  12. Levy, A., Keitel, C., Engel, S., McLurkin, J.: The extended velocity obstacle and applying orca in the real world. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp 16–22. IEEE (2015)

  13. Van Den Berg, J., Guy, S.J., Lin, M., Manocha, D.: Reciprocal n-body collision avoidance. In: Robotics Research, pp 3–19. Springer (2011)

  14. Claes, D., Tuyls, K.: Multi robot collision avoidance in a shared workspace. Auton. Robot. 42(8), 1749–1770 (2018)

    Article  Google Scholar 

  15. Platt, R., Tedrake, R., Kaelbling, L., Lozano-Perez, T.: Belief space planning assuming maximum likelihood observations, in Proc. Robot., Sci. Syst., Zaragoza, Spain, pp. 1–9 (2010)

  16. Van Den Berg, J., Patil, S., Alterovitz, R.: Motion planning under uncertainty using iterative local optimization in belief space. Int. J. Robot. Res. 31(11), 1263–1278 (2012)

    Article  Google Scholar 

  17. Kim, S.K., Thakker, R., Agha-Mohammadi, A.A.: Bi-directional value learning for risk-aware planning under uncertainty. IEEE Robot. Autom. Lett. 4(3), 2493–2500 (2019)

    Article  Google Scholar 

  18. Agha-mohammadi, A.A., Agarwal, S., Kim, S. -K., Chakravorty, S., Amato, N.M.: Slap: Simultaneous localization and planning under uncertainty via dynamic replanning in belief space. IEEE Trans. Robot. 34(5), 1195–1214 (2018)

    Article  Google Scholar 

  19. Nardi, L., Stachniss, C.: Uncertainty-aware path planning for navigation on road networks using augmented mdps. In: 2019 International Conference on Robotics and Automation (ICRA), pp 5780–5786. IEEE (2019)

  20. Fulgenzi, C., Spalanzani, A., Laugier, C., Tay, C.: Risk based motion planning and navigation in uncertain dynamic environment, Res. Rep., 2010, p. 14. [Online]. Available: https://hal.inria.fr/inria-00526601 (2010)

  21. Bopardikar, S.D., Englot, B., Speranzon, A.: Multiobjective path planning: Localization constraints and collision probability. IEEE Trans. Robot. 31(3), 562–577 (2015)

    Article  Google Scholar 

  22. Houénou, A., Bonnifait, P., Cherfaoui, V.: Risk assessment for collision avoidance systems. In: 17th International, IEEE Conference on Intelligent Transportation Systems (ITSC), pp 386–391. IEEE (2014)

  23. Pereira, A.A., Binney, J., Jones, B.H., Ragan, M., Sukhatme, G.S.: Toward risk aware mission planning for autonomous underwater vehicles. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 3147–3153. IEEE (2011)

  24. Feyzabadi, S., Carpin, S.: Risk-aware path planning using hirerachical constrained markov decision processes. In: 2014 IEEE International Conference on Automation Science and Engineering (CASE), pp 297–303. IEEE (2014)

  25. Zhang, Z., Scaramuzza, D.: Perception-aware receding horizon navigation for mavs. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp 2534–2541. IEEE (2018)

  26. Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30(7), 846–894 (2011)

    Article  Google Scholar 

  27. Cai, K., Wang, C., Li, C., Song, S., Meng, M.Q.-H.: Adaptive sampling for human-aware path planning in dynamic environments. In: 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp 1987–1994. IEEE (2019)

  28. Wang, C., Meng, M. Q.-H.: Variant step size rrt: An efficient path planner for uav in complex environments. In: 2016 IEEE International Conference on Real-time Computing and Robotics (RCAR), pp 555–560. IEEE (2016)

  29. Bry, A., Roy, N.: Rapidly-exploring random belief trees for motion planning under uncertainty. In: 2011 IEEE international conference on robotics and automation, pp 723–730. IEEE (2011)

  30. Pilania, V., Gupta, K.: A localization aware sampling strategy for motion planning under uncertainty. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 6093–6099. IEEE (2015)

  31. Chi, W., Meng, M. Q. -H.: Risk-rrt* a robot motion planning algorithm for the human robot coexisting environment. In: 2017 18th International Conference on Advanced Robotics (ICAR), pp 583–588. IEEE (2017)

  32. Wang, X., Vozar, S., Olson, E.: Flag: Feature-based localization between air and ground. In: 2017 IEEE international conference on robotics and automation (ICRA), pp 3178–3184. IEEE (2017)

  33. Schaefer, A., Luft, L., Burgard, W.: An analytical lidar sensor model based on ray path information. IEEE Robot. Autom. Lett. 2(3), 1405–1412 (2017)

    Article  Google Scholar 

  34. Pierson, A., Vasile, C. -I., Gandhi, A., Schwarting, W., Karaman, S., Rus, D.: Dynamic risk density for autonomous navigation in cluttered environments without object detection. In: 2019 International Conference on Robotics and Automation (ICRA), pp 5807–5814. IEEE (2019)

  35. Müller, J., Sukhatme, G.S.: Risk-aware trajectory generation with application to safe quadrotor landing. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 3642–3648. IEEE (2014)

  36. Zhu, Y., Tian, D., Yan, F.: Effectiveness of entropy weight method in decision-making. Math. Probl. Eng. https://doi.org/10.1155/2020/3564835 (2020)

  37. Kapania, N.R., Gerdes, J.C.: Design of a feedback-feedforward steering controller for accurate path tracking and stability at the limits of handling. Veh. Syst. Dyn. 53(12), 1687–1704 (2015)

    Article  Google Scholar 

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Funding

This work was supported the Hong Kong ITC ITSP Tier 2 grant # ITS/105/18FP: An intelligent Robotics System for Autonomous Airport Passenger Trolley Deployment, awarded to Max Q.-H. Meng.

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Authors and Affiliations

  1. Department of Electronic Engineering, The Chinese University of Hong Kong, N.T., Hong Kong, SAR, China

    Kuanqi Cai & Max Q.-H. Meng

  2. School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, China

    Kuanqi Cai, Shuang Song, Haoyao Chen & Max Q.-H. Meng

  3. School of Control Science and Engineering, Shandong University, Shandong, China

    Chaoqun Wang

  4. Shenzhen Key Laboratory of Robotics Perception and Intelligence, Southern University of Science and Technology, Shenzhen, China

    Max Q.-H. Meng

Authors
  1. Kuanqi Cai
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  2. Chaoqun Wang
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  3. Shuang Song
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  4. Haoyao Chen
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  5. Max Q.-H. Meng
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Contributions

The authors contributions are as follows: Conceptualization: [Kuanqi Cai], [Chaoqun Wang], [Max Q.-H. Meng]; Methodology: [Chaoqun Wang],[Kuanqi Cai]; Software: [Kuanqi Cai], [Chaoqun Wang]; Writing - original draft preparation: [Kuanqi Cai], [Chaoqun Wang]; Writing - review and editing: [Kuanqi Cai], [Chaoqun Wang]; Funding acquisition: [Shuang Song]; [Max Q.-H. Meng]; Supervision: [Shuang Song]; [Haoyao Chen], [Max Q.-H. Meng].

Corresponding author

Correspondence to Max Q.-H. Meng.

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This manuscript describes original work and is not under consideration for publication elsewhere. All authors approved the manuscript and this submission and we have no conflicts of interest to disclose.

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Kuanqi Cai and Chaoqun Wang contribute equally to this paper.

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Cai, K., Wang, C., Song, S. et al. Risk-Aware Path Planning Under Uncertainty in Dynamic Environments. J Intell Robot Syst 101, 47 (2021). https://doi.org/10.1007/s10846-021-01323-3

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