Skip to main content
SpringerLink
Log in

Online Safe Trajectory Generation of Quadrotors Autonomous Landing in Complex Dynamic Environments

  • Conference paper
  • First Online:
Intelligent Robotics and Applications (ICIRA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13458))

Included in the following conference series:

  • 2656 Accesses

Abstract

This paper regards obstacle avoidance and landing as a whole process and proposes a motion planning framework for the quadrotor, which can real-time calculate and enable the quadrotor to avoid obstacles while landing on the mobile platform. Assuming the platform’s motion state is known, the Move-RRT and Move-B-RRT sampling algorithms can establish the static and dynamic trees to obtain the collision-free path containing time information. The algorithm adopts proportional sampling to make the time allocation between path points reasonable. We can determine the static obstacles and mobile platform positions and generate the flight corridor according to the position and time information contained in the path points. After optimization, trajectories represented by piecewise Bézier curves satisfying dynamic constraints are obtained. This paper verifies the motion planning framework in a complex simulation environment.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Jin, S., Zhang, J., Shen, L., Li, T.: On-board vision autonomous landing techniques for quadrotor: a survey. In: Chinese Control Conference CCC, pp. 10284–10289 (2016)

    Google Scholar 

  2. Baca, T., et al.: Autonomous landing on a moving vehicle with an unmanned aerial vehicle. J. Field Robot. 36(5), 874–891 (2019)

    Article  Google Scholar 

  3. Beul, M., Behnke, S.: Fast full state trajectory generation for multirotors. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS) ICUAS 2017, no. June, pp. 408–416 (2017)

    Google Scholar 

  4. Battisti, T., Muradore, R.: A velocity obstacles approach for autonomous landing and teleoperated robots. Auton. Robot. 44(2), 217–232 (2019). https://doi.org/10.1007/s10514-019-09887-8

    Article  Google Scholar 

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

    Google Scholar 

  6. Karaman, S., Frazzoli, E.: Incremental sampling-based algorithms for optimal motion planning. Robot. Sci. Syst. 6, 267–274 (2011)

    MATH  Google Scholar 

  7. Yang, Y., Merkt, W., Ivan, V., Vijayakumar, S.: Planning in time-configuration space for efficient pick-and-place in non-static environments with temporal constraints. In: IEEE-RAS International Conference on Humanoid Robots, vol. 2018, pp. 893–900 (2019)

    Google Scholar 

  8. Harabor, D., Grastien, A.: Online graph pruning for pathfinding on grid maps. In: Proceedings of National Conference on Artificial Intelligence, vol. 2, pp. 1114–1119 (2011)

    Google Scholar 

  9. Likhachev, M., Gordon, G., Thrun, S.: ARA*: Anytime A* with provable bounds on sub-optimality. In: Proceedings of Advances in Neural Information Processing Systems (NIPS 2003), pp. 767–774 (2003)

    Google Scholar 

  10. Dolgov, D., Thrun, S., Montemerlo, M., Diebel, J.: Path planning for autonomous vehicles in unknown semi-structured environments. Int. J. Robot. Res. 29(5), 485–501 (2010)

    Article  Google Scholar 

  11. Cowley, A., Cohen, B., Marshall, W., Taylor, C.J., Likhachev, M.: Perception and motion planning for pick-and-place of dynamic objects. In: IEEE International Conference on Intelligent Robots and Systems, pp. 816–823 (2013)

    Google Scholar 

  12. Menon, A., Cohen, B., Likhachev, M.: Motion planning for smooth pickup of moving objects. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 453–460 (2014)

    Google Scholar 

  13. Islam, F., Salzman, O., Agarwal, A., Likhachev, M.: Provably constant-time planning and replanning for real-time grasping objects off a conveyor belt. Int. J. Robot. Res. 40, 1370–1384 (2021)

    Article  Google Scholar 

  14. Ratliff, N., Zucker, M., Bagnell, J.A., Srinivasa, S.: CHOMP: gradient optimization techniques for efficient motion planning, pp. 489–494 (2009)

    Google Scholar 

  15. Mellinger, D., Kumar, V.: Minimum snap trajectory generation and control for quadrotors. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 2520–2525 (2011)

    Google Scholar 

  16. Chen, J., Liu, T., Shen, S.: Online generation of collision-free trajectories for quadrotor flight in unknown cluttered environments. In: Proceedings of IEEE International Conference on Robotics and Automation, vol. 2016, pp. 1476–1483 (2016)

    Google Scholar 

  17. Gao, F., Shen, S.: Online quadrotor trajectory generation and autonomous navigation on point clouds. In: SSRR 2016 - International Symposium on Safety, Security, and Rescue Robotics, pp. 139–146 (2016)

    Google Scholar 

  18. Gao, F., Wu, W., Lin, Y., Shen, S.: Online safe trajectory generation for quadrotors using fast marching method and Bernstein basis polynomial. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 344–351 (2018)

    Google Scholar 

  19. Mueller, M.W., Hehn, M., Andrea, R.D.: A computationally efficient motion primitive for quadrocopter trajectory generation. IEEE Trans. Robot. 31(6), 1294–1310 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Bo Liu & Shimin Wei

Authors
  1. Bo Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shimin Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shimin Wei .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Honghai Liu

  2. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  3. Shenyang Institute of Automation, Shenyang, Liaoning, China

    Lianqing Liu

  4. Harbin Institute of Technology, Harbin, China

    Li Jiang

  5. Shanghai Jiao Tong University, Shanghai, China

    Guoying Gu

  6. Shenzhen Institute of Advanced Technology, Shenzhen, China

    Xinyu Wu

  7. Harbin Institute of Technology, Shenzhen, China

    Weihong Ren

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, B., Wei, S. (2022). Online Safe Trajectory Generation of Quadrotors Autonomous Landing in Complex Dynamic Environments. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13458. Springer, Cham. https://doi.org/10.1007/978-3-031-13841-6_47

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-13841-6_47

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13840-9

  • Online ISBN: 978-3-031-13841-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation