Abstract
Autonomous navigation of unmanned aerial vehicles (UAVs) in unknown and complex environments is still a challenge. Because the environment is partially observable to the drone, it is hard to consider trajectory safety and exploration efficiency simultaneously in autonomous navigation. In this paper, we present a motion planning method composed of a geometrically topological waypoints searching method and an adaptive trajectory replanning framework, which improves trajectory safety without sacrificing navigation efficiency. Our waypoint searching approach considers the safety distance and reduces pathfinding’s search space by extracting some feasible path points on both sides of the obstacle. And this is based on the ESDF gradient and geometry information of a given obstacle. Besides, the found waypoints keep a safe distance from the obstacles, making the method work well in a scene that contains large obstacles. Based on the waypoint searching method, we proposed an adaptive trajectory replanning framework to improve trajectory safety and navigation efficiency further. The replanning procedure is event-triggered. When the planned trajectory is too close to an obstacle according to our safe condition, the trajectory will be re-planned. The proposed method is tested extensively in various simulation environments. Results show that the trajectory safety of our method is improved by 27.8%, and the computing time for replanning is reduced by 90.8% compared to the state-of-the-art method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, J., Liu, T., Shen, S.: Online generation of collision-free trajectories for quadrotor flight in unknown cluttered environments. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1476–1483. IEEE (2016)
Ding, W., Gao, W., Wang, K., Shen, S.: An efficient B-spline-based Kinodynamic replanning framework for quadrotors. IEEE Trans. Rob. 35(6), 1287–1306 (2019)
Gao, F., Lin, Y., Shen, S.: Gradient-based online safe trajectory generation for quadrotor flight in complex environments. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3681–3688. IEEE (2017)
Gao, F., Shen, S.: Online quadrotor trajectory generation and autonomous navigation on point clouds. In: 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 139–146. IEEE (2016)
Gao, F., Wu, W., Lin, Y., Shen, S.: Online safe trajectory generation for quadrotors using fast marching method and Bernstein basis polynomial. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 344–351. IEEE (2018)
Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)
Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30(7), 846–894 (2011)
LaValle, S.M., Kuffner, J.J.: Rapidly-exploring random trees: progress and prospects. Algorithmic Comput. Robot. New Dir. 5, 293–308 (2001)
Liu, S., Atanasov, N., Mohta, K., Kumar, V.: Search-based motion planning for quadrotors using linear quadratic minimum time control. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2872–2879. IEEE (2017)
Liu, S., et al.: Planning dynamically feasible trajectories for quadrotors using safe flight corridors in 3-D complex environments. IEEE Robot. Autom. Lett. 2(3), 1688–1695 (2017)
Mellinger, D., Kumar, V.: Minimum snap trajectory generation and control for quadrotors. In: 2011 IEEE International Conference on Robotics and Automation, pp. 2520–2525. IEEE (2011)
Oleynikova, H., Burri, M., Taylor, Z., Nieto, J., Siegwart, R., Galceran, E.: Continuous-time trajectory optimization for online UAV replanning. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5332–5339. IEEE (2016)
Quan, L., Han, L., Zhou, B., Shen, S., Gao, F.: Survey of UAV motion planning. IET Cyber-Systems Robot. 2(1), 14–21 (2020)
Quinlan, S., Khatib, O.: Elastic bands: connecting path planning and control. In: Proceedings IEEE International Conference on Robotics and Automation, pp. 802–807. IEEE (1993)
Richter, C., Bry, A., Roy, N.: Polynomial trajectory planning for aggressive quadrotor flight in dense indoor environments. In: Inaba, M., Corke, P. (eds.) Robotics Research. STAR, vol. 114, pp. 649–666. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-28872-7_37
Tordesillas, J., Lopez, B.T., How, J.P.: Faster: fast and safe trajectory planner for flights in unknown environments. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1934–1940. IEEE (2019)
Usenko, V., von Stumberg, L., Pangercic, A., Cremers, D.: Real-time trajectory replanning for MAVs using uniform B-splines and a 3D circular buffer. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 215–222. IEEE (2017)
Ye, H., Zhou, X., Wang, Z., Xu, C., Chu, J., Gao, F.: TGK-planner: an efficient topology guided kinodynamic planner for autonomous quadrotors. IEEE Robot. Autom. Lett. 6(2), 494–501 (2020)
Zhou, B., Gao, F., Pan, J., Shen, S.: Robust real-time UAV replanning using guided gradient-based optimization and topological paths. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 1208–1214. IEEE (2020)
Zhou, B., Gao, F., Wang, L., Liu, C., Shen, S.: Robust and efficient quadrotor trajectory generation for fast autonomous flight. IEEE Robot. Autom. Lett. 4(4), 3529–3536 (2019)
Acknowledgment
This work was supported by the National Key Research and Development Program of China under Grant No.2017YFB1001901.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Xu, J., Tan, J., Xue, C., Su, Y., He, X., Zhang, Y. (2021). A Safe Topological Waypoints Searching-Based Conservative Adaptive Motion Planner in Unknown Cluttered Environment. In: Gao, H., Wang, X. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-030-92638-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-92638-0_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92637-3
Online ISBN: 978-3-030-92638-0
eBook Packages: Computer ScienceComputer Science (R0)