Abstract
This paper introduces a new approach: the rapidly exploring random tree star-line (RRT*-line) algorithm for sub-optimal path planning based on distance, time, or energy with knowledge of the wind field. The proposed strategy is predicated on the concept that humans approach a target by aiming directly at it when it is visible. The RRT*-line is an extension of RRT*, which is an asymptotically optimal path planner. The proposed algorithm is tested in cluttered environments with obstacles, considering the optimal distance path, and optimal energy or time under wind load. The new algorithm is explicated in this paper, and a comparison between RRT, RRT*, Dijkstra, and RRT*-line shows that the proposed approach provides bett er results in various complex environments for wind-aware path planning based on distance, time, or energy.
This work was supported by Autonomous Robots Research Center of the Technology Innovation Institute, United Arab Emirates and the Robotics Group at the University of Patras, Greece.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kularatne, D., Bhattacharya, S., Hsieh, M.A.: Time and energy optimal path planning in general flows. In: Robotics: Science and Systems (2016)
Liu, L., Sukhatme, G.: A solution to time-varying markov decision processes. IEEE Rob. Autom. Lett. 3, 1631–1638 (2018)
Song, Z., Zhang, J., Wu, D., Tian, W.: A novel path planning algorithm for ships in dynamic current environment. Available at SSRN 4331220
Gu, S., Zhou, C., Wen, Y., Xiao, C., Knoll, A.: Motion planning for an unmanned surface vehicle with wind and current effects. J. Marine Sci. Eng. 10(3), 420 (2022)
Bucher, T., Stastny, T., Verling, S., Siegwart, R.: Robust wind-aware path optimization onboard small fixed-wing uavs. In: AIAA SCITECH 2023 Forum, p. 2640 (2023)
Thanellas, G., Moulianitis, V., Aspragathos, N.: A spatially wind aware quadcopter (UAV) path planning approach. IFAC-PapersOnLine 52, 283–288 (2019)
Dijkstra, E.: A note on two problems in connexion with graphs. Numerische Mathematik 1, 269–271 (1959)
Hwang, Y., Ahuja, N.: A potential field approach to path planning. IEEE Trans. Rob. Autom. 8, 23–32 (1992)
LaValle, S.: Rapidly-exploring random trees : a new tool for path planning. In: The Annual Research Report (1998)
Karaman, S., Frazzoli, E.: Incremental sampling-based algorithms for optimal motion planning. ArXiv, abs/1005.0416 (2010)
Gammell, J., Srinivasa, S., Barfoot, T.: Informed rrt*: optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2997–3004 (2014)
Nasir, J., et al.: Rrt*-smart: a rapid convergence implementation of rrt*. Int. J. Adv. Rob. Syst. 10 (2013)
Kim, D., Lee, J., Yoon, S.: Cloud rrt\(^*\): sampling cloud based rrt\(^*\). In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 2519–2526 (2014)
Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Rob. Res. 30, 846–894 (2011)
Strub, M.P., Gammell, J.D.: Advanced bit*(abit*): sampling-based planning with advanced graph-search techniques. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 130–136. IEEE (2020)
Gammell, J., Srinivasa, S., Barfoot, T.: Bit*: batch informed trees for optimal sampling-based planning via dynamic programming on implicit random geometric graphs. ArXiv, abs/1405.5848 (2014)
Acknowledgment
This work was supported by the Technology Innovation Institute (TII) of United Arab Emirates and the Robotics Group at the University of Patras, Greece.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Pediaditis, D., Galvis, J., Aspragathos, N.A. (2023). Wind Aware Planning Using the Introduced RRT*-line Algorithm. In: Petrič, T., Ude, A., Žlajpah, L. (eds) Advances in Service and Industrial Robotics. RAAD 2023. Mechanisms and Machine Science, vol 135. Springer, Cham. https://doi.org/10.1007/978-3-031-32606-6_41
Download citation
DOI: https://doi.org/10.1007/978-3-031-32606-6_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-32605-9
Online ISBN: 978-3-031-32606-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)