Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
125 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

RELAX: Reinforcement Learning Enabled 2D-LiDAR Autonomous System for Parsimonious UAVs (2309.08095v2)

Published 15 Sep 2023 in cs.RO

Abstract: Unmanned Aerial Vehicles (UAVs) have become increasingly prominence in recent years, finding applications in surveillance, package delivery, among many others. Despite considerable efforts in developing algorithms that enable UAVs to navigate through complex unknown environments autonomously, they often require expensive hardware and sensors, such as RGB-D cameras and 3D-LiDAR, leading to a persistent trade-off between performance and cost. To this end, we propose RELAX, a novel end-to-end autonomous framework that is exceptionally cost-efficient, requiring only a single 2D-LiDAR to enable UAVs operating in unknown environments. Specifically, RELAX comprises three components: a pre-processing map constructor; an offline mission planner; and a reinforcement learning (RL)-based online re-planner. Experiments demonstrate that RELAX offers more robust dynamic navigation compared to existing algorithms, while only costing a fraction of the others. The code will be made public upon acceptance.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (42)
  1. A. Rovira-Sugranes, A. Razi, F. Afghah, and J. Chakareski, “A review of ai-enabled routing protocols for uav networks: Trends, challenges, and future outlook,” Ad Hoc Networks, vol. 130, p. 102790, 2022.
  2. D. Patil, M. Ansari, D. Tendulkar, R. Bhatlekar, V. N. Pawar, and S. Aswale, “A survey on autonomous military service robot,” in 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE).   IEEE, 2020, pp. 1–7.
  3. B. Mishra, D. Garg, P. Narang, and V. Mishra, “Drone-surveillance for search and rescue in natural disaster,” Computer Communications, vol. 156, pp. 1–10, 2020.
  4. E. Alvarado, “237 ways drone applications revolutionize business,” Drone Industry Insights, 2021.
  5. M. Wieczorowski, N. Swojak, P. Pawlus, and A. Pereira, “The use of drones in modern length and angle metrology,” in Modern Technologies Enabling Safe and Secure UAV Operation in Urban Airspace.   IOS Press, 2021, pp. 125–140.
  6. S. Ahirwar, R. Swarnkar, S. Bhukya, and G. Namwade, “Application of drone in agriculture,” International Journal of Current Microbiology and Applied Sciences, vol. 8, no. 01, pp. 2500–2505, 2019.
  7. J. Shahmoradi, E. Talebi, P. Roghanchi, and M. Hassanalian, “A comprehensive review of applications of drone technology in the mining industry,” Drones, vol. 4, no. 3, p. 34, 2020.
  8. I. Noreen, A. Khan, and Z. Habib, “Optimal path planning using rrt* based approaches: a survey and future directions,” International Journal of Advanced Computer Science and Applications, vol. 7, no. 11, 2016.
  9. D. Kularatne, S. Bhattacharya, and M. A. Hsieh, “Time and energy optimal path planning in general flows.” in Robotics: Science and Systems.   Ann Arbor, MI, 2016, pp. 1–10.
  10. L. Xu, B. Song, and M. Cao, “A new approach to optimal smooth path planning of mobile robots with continuous-curvature constraint,” Systems Science & Control Engineering, vol. 9, no. 1, pp. 138–149, 2021.
  11. H. Shin and J. Chae, “A performance review of collision-free path planning algorithms,” Electronics, vol. 9, no. 2, p. 316, 2020.
  12. M. Jones, S. Djahel, and K. Welsh, “Path-planning for unmanned aerial vehicles with environment complexity considerations: A survey,” ACM Computing Surveys, vol. 55, no. 11, pp. 1–39, 2023.
  13. C. Cheng and Y. Chen, “A neural network based mobile robot navigation approach using reinforcement learning parameter tuning mechanism,” in 2021 China Automation Congress (CAC).   IEEE, 2021, pp. 2600–2605.
  14. Z. Xu, X. Zhan, B. Chen, Y. Xiu, C. Yang, and K. Shimada, “A real-time dynamic obstacle tracking and mapping system for uav navigation and collision avoidance with an rgb-d camera,” in 2023 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2023, pp. 10 645–10 651.
  15. S. Cui, Y. Chen, and X. Li, “A robust and efficient uav path planning approach for tracking agile targets in complex environments,” Machines, vol. 10, no. 10, 2022.
  16. H. Kim, H. Kim, S. Lee, and H. Lee, “Autonomous exploration in a cluttered environment for a mobile robot with 2d-map segmentation and object detection,” IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 6343–6350, 2022.
  17. H. Qin, Z. Meng, W. Meng, X. Chen, H. Sun, F. Lin, and M. H. Ang, “Autonomous exploration and mapping system using heterogeneous uavs and ugvs in gps-denied environments,” IEEE Transactions on Vehicular Technology, vol. 68, no. 2, pp. 1339–1350, 2019.
  18. L. Ulrich, E. Vezzetti, S. Moos, and F. Marcolin, “Analysis of rgb-d camera technologies for supporting different facial usage scenarios,” Multimedia Tools and Applications, vol. 79, no. 39-40, pp. 29 375–29 398, 2020.
  19. D. Van Nam and K. Gon-Woo, “Solid-state lidar based-slam: A concise review and application,” in 2021 IEEE International Conference on Big Data and Smart Computing (BigComp).   IEEE, 2021, pp. 302–305.
  20. S. Aggarwal and N. Kumar, “Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges,” Computer Communications, vol. 149, pp. 270–299, 2020.
  21. M. G. Ocando, N. Certad, S. Alvarado, and Á. Terrones, “Autonomous 2d slam and 3d mapping of an environment using a single 2d lidar and ros,” in 2017 Latin American robotics symposium (LARS) and 2017 Brazilian symposium on robotics (SBR).   IEEE, 2017, pp. 1–6.
  22. H. F. Murcia, M. F. Monroy, and L. F. Mora, “3d scene reconstruction based on a 2d moving lidar,” in Applied Informatics: First International Conference, ICAI 2018, Bogotá, Colombia, November 1-3, 2018, Proceedings 1.   Springer, 2018, pp. 295–308.
  23. Z. Ding and H. Dong, “Challenges of reinforcement learning,” Deep Reinforcement Learning: Fundamentals, Research and Applications, pp. 249–272, 2020.
  24. T. Elmokadem and A. V. Savkin, “Towards fully autonomous uavs: A survey,” Sensors, vol. 21, no. 18, p. 6223, 2021.
  25. Y. Lu, Z. Xue, G.-S. Xia, and L. Zhang, “A survey on vision-based uav navigation,” Geo-spatial information science, vol. 21, no. 1, pp. 21–32, 2018.
  26. J. Engel, J. Sturm, and D. Cremers, “Scale-aware navigation of a low-cost quadrocopter with a monocular camera,” Robotics and Autonomous Systems, vol. 62, no. 11, pp. 1646–1656, 2014.
  27. T. Mao, K. Huang, X. Zeng, L. Ren, C. Wang, S. Li, M. Zhang, and Y. Chen, “Development of power transmission line defects diagnosis system for uav inspection based on binocular depth imaging technology,” in 2019 2nd International Conference on Electrical Materials and Power Equipment (ICEMPE).   IEEE, 2019, pp. 478–481.
  28. W. Jingjing, G. De, and L. Fei, “Research on autonomous positioning method of uav based on binocular vision,” in 2019 Chinese automation congress (CAC).   IEEE, 2019, pp. 3588–3593.
  29. A. Bachrach, S. Prentice, R. He, P. Henry, A. S. Huang, M. Krainin, D. Maturana, D. Fox, and N. Roy, “Estimation, planning, and mapping for autonomous flight using an rgb-d camera in gps-denied environments,” The International Journal of Robotics Research, vol. 31, no. 11, pp. 1320–1343, 2012.
  30. N. Jeong, H. Hwang, and E. T. Matson, “Evaluation of low-cost lidar sensor for application in indoor uav navigation,” in 2018 IEEE Sensors Applications Symposium (SAS).   IEEE, 2018, pp. 1–5.
  31. E. Aldao, L. M. González-de Santos, and H. González-Jorge, “Lidar based detect and avoid system for uav navigation in uam corridors,” Drones, vol. 6, no. 8, p. 185, 2022.
  32. Q. Liang, Z. Wang, Y. Yin, W. Xiong, J. Zhang, and Z. Yang, “Autonomous aerial obstacle avoidance using lidar sensor fusion,” Plos one, vol. 18, no. 6, p. e0287177, 2023.
  33. G. Gabriel, M. Alvaro, A. Jose, and Milena, “Adaptive path planning for fusing rapidly exploring random trees and deep reinforcement learning in an agriculture dynamic environment uavs,” Agriculture, vol. 13, pp. 354–379, 2023.
  34. S. Kohlbrecher, O. von Stryk, J. Meyer, and U. Klingauf, “A flexible and scalable slam system with full 3d motion estimation,” in 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics, 2011, pp. 155–160.
  35. S. M. LaValle and J. James J. Kuffner, “Randomized kinodynamic planning,” The International Journal of Robotics Research, vol. 20, no. 5, pp. 378–400, 2001.
  36. H. Van Hasselt, A. Guez, and D. Silver, “Deep reinforcement learning with double q-learning,” in Proceedings of the AAAI conference on artificial intelligence, vol. 30, no. 1, 2016.
  37. Z. Wang, T. Schaul, M. Hessel, H. Hasselt, M. Lanctot, and N. Freitas, “Dueling network architectures for deep reinforcement learning,” in International conference on machine learning.   PMLR, 2016, pp. 1995–2003.
  38. V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing atari with deep reinforcement learning,” arXiv preprint arXiv:1312.5602, 2013.
  39. J. Tordesillas and J. P. How, “Deep-panther: Learning-based perception-aware trajectory planner in dynamic environments,” IEEE Robotics and Automation Letters, vol. 8, no. 3, pp. 1399–1406, 2023.
  40. F. Kong, W. Xu, Y. Cai, and F. Zhang, “Avoiding dynamic small obstacles with onboard sensing and computation on aerial robots,” IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7869–7876, 2021.
  41. J.-T. Tsai, J.-H. Chou, and T.-K. Liu, “Tuning the structure and parameters of a neural network by using hybrid taguchi-genetic algorithm,” IEEE Transactions on Neural Networks, vol. 17, no. 1, pp. 69–80, 2006.
  42. E. DIJKSTRA, “A note on two problems in connexion with graphs.” Numerische Mathematik, vol. 1, pp. 269–271, 1959. [Online]. Available: http://eudml.org/doc/131436

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now