Hostname: page-component-669899f699-ggqkh Total loading time: 0 Render date: 2025-04-25T01:27:15.268Z Has data issue: false hasContentIssue false
  • English
  • Français

Robust motion planning for mobile robots under attacks against obstacle localization

Published online by Cambridge University Press:  18 September 2024

Fenghua Wu ,
Wenbing Tang Open the ORCID record for Wenbing Tang [Opens in a new window] ,
Yuan Zhou Open the ORCID record for Yuan Zhou [Opens in a new window] ,
Shang-Wei Lin ,
Zuohua Ding  and
Yang Liu
Fenghua Wu
Affiliation:
School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
Wenbing Tang*
Affiliation:
Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, PR China
Yuan Zhou
Affiliation:
School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
Shang-Wei Lin
Affiliation:
School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
Zuohua Ding
Affiliation:
School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, PR China
Yang Liu
Affiliation:
School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
*
Corresponding author: Wenbing Tang; Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Thanks to its real-time computation efficiency, deep reinforcement learning (DRL) has been widely applied in motion planning for mobile robots. In DRL-based methods, a DRL model computes an action for a robot based on the states of its surrounding obstacles, including other robots that may communicate with it. These methods always assume that the environment is attack-free and the obtained obstacles’ states are reliable. However, in the real world, a robot may suffer from obstacle localization attacks (OLAs), such as sensor attacks, communication attacks, and remote-control attacks, which cause the robot to retrieve inaccurate positions of the surrounding obstacles. In this paper, we propose a robust motion planning method ObsGAN-DRL, integrating a generative adversarial network (GAN) into DRL models to mitigate OLAs in the environment. First, ObsGAN-DRL learns a generator based on the GAN model to compute the approximation of obstacles’ accurate positions in benign and attack scenarios. Therefore, no detectors are required for ObsGAN-DRL. Second, by using the approximation positions of the surrounding obstacles, ObsGAN-DRL can leverage the state-of-the-art DRL methods to compute collision-free motion commands (e.g., velocity) efficiently. Comprehensive experiments show that ObsGAN-DRL can mitigate OLAs effectively and guarantee safety. We also demonstrate the generalization of ObsGAN-DRL.

Keywords

motion planningdeep reinforcement learninggenerative adversarial networkobstacle localization attacks
Type
Research Article
Information
Robotica , Volume 42 , Issue 8 , August 2024 , pp. 2781 - 2800
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Hichri, B., Gallala, A., Giovannini, F. and Kedziora, S., “Mobile robots path planning and mobile multirobots control: A review,” Robotica 40(12), 4257–4270 (2022).CrossRefGoogle Scholar
Krnjak, A., Draganjac, I., Bogdan, S., Petrović, T., Miklić, D. and Kovačić, Z., “Decentralized Control of Free Ranging AGVs in Warehouse Environments,” In: IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA (2015) pp. 2034–2041.Google Scholar
Gonzalez, R., Kloetzer, M. and Mahulea, C., “Comparative Study of Trajectories Resulted from Cell Decomposition Path Planning Approaches,” In: International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania (2017) pp. 49–54.Google Scholar
Kemna, S., Rogers, J. G., Nieto-Granda, C., Young, S. and Sukhatme, G. S., “Multi-Robot Coordination Through Dynamic Voronoi Partitioning for Informative Adaptive Sampling in Communication-Constrained Environments,” In: IEEE International Conference on Robotics and Automation (ICRA), Singapore (2017) pp. 2124–2130.Google Scholar
LaValle, S. M., Rapidly-exploring random trees: A new tool for path planning, Iowa State University, IA, Ames, (Oct. 1998). Tech. Rep. TR 98-11.Google Scholar
Vlantis, P., Vrohidis, C., Bechlioulis, C. P. and Kyriakopoulos, K. J., “Robot Navigation in Complex Workspaces Using Harmonic Maps,” In: IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia (2018) pp. 1726–1731.Google Scholar
v. d. Berg, J., Guy, S. J., Lin, M. and Manocha, D., “Reciprocal n-Body Collision Avoidance,” In: Robotics Research, (Springer, 2011) pp. 3–19.CrossRefGoogle Scholar
Zhou, Y., Hu, H., Liu, Y., Lin, S.-W. and Ding, Z., “A real-time and fully distributed approach to motion planning for multirobot systems,” IEEE Trans Syst Man Cybern Syst 49(12), 2636–2650 (2017).CrossRefGoogle Scholar
Jiao, K., Chen, J., Xin, B., Li, L., Zheng, Y. and Zhao, Z., “Three-dimensional path planning with enhanced gravitational search algorithm for unmanned aerial vehicle,” Robotica, 1–35 (2024). doi: 10.1017/S0263574724000869.Google Scholar
Tang, W., Zhou, Y., Zhang, T., Liu, Y., Liu, J. and Ding, Z., “Cooperative collision avoidance in multirobot systems using fuzzy rules and velocity obstacles,” Robotica 41(2), 668–689 (2023).CrossRefGoogle Scholar
Tan, Y., Ouyang, J., Zhang, Z., Lao, Y. and Wen, P., “Path planning for spot welding robots based on improved ant colony algorithm,” Robotica 41(3), 926–938 (2023).CrossRefGoogle Scholar
Chen, Y. F., Liu, M., Everett, M. and How, J. P., “Decentralized Non-Communicating Multiagent Collision Avoidance with Deep Reinforcement Learning,” In: IEEE International Conference on Robotics and Automation (ICRA), Singapore (2017) pp. 285–292.Google Scholar
Chen, Y. F., Everett, M., Liu, M. and How, J. P., “Socially Aware Motion Planning with Deep Reinforcement Learning,” In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada (2017) pp. 1343–1350.Google Scholar
Everett, M., Chen, Y. F. and How, J. P., “Motion Planning among Dynamic, Decision-Making Agents with Deep Reinforcement Learning,” In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain (2018) pp. 3052–3059.Google Scholar
Chen, C., Liu, Y., Kreiss, S. and Alahi, A., “Crowd-Robot Interaction: Crowd-Aware Robot Navigation with Attention-Based Deep Reinforcement Learning,” In: International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada (2019) pp. 6015–6022.Google Scholar
Xu, L., Wu, F., Zhou, Y., Hu, H., Ding, Z. and Liu, Y., “Criticality-Guided Deep Reinforcement Learning for Motion Planning,” In: China Automation Congress (CAC), Kunming, China, (2021) pp. 3378–3383.Google Scholar
Semnani, S. H., Liu, H., Everett, M., De Ruiter, A. and How, J. P., “Multi-agent motion planning for dense and dynamic environments via deep reinforcement learning,” IEEE Robot Autom Lett 5(2), 3221–3226 (2020).CrossRefGoogle Scholar
Khlif, N., Nahla, K. and Safya, B., “Reinforcement learning with modified exploration strategy for mobile robot path planning,” Robotica 41(9), 2688–2702 (2023).CrossRefGoogle Scholar
Bao, J., Zhang, G., Peng, Y., Shao, Z. and Song, A., “Learn multi-step object sorting tasks through deep reinforcement learning,” Robotica 40(11), 3878–3894 (2022).CrossRefGoogle Scholar
Sun, Z., Balakrishnan, S., Su, L., Bhuyan, A., Wang, P. and Qiao, C., “Who is in control? practical physical layer attack and defense for mmwave-based sensing in autonomous vehicles,” IEEE Trans Inf Forensics Secur 16, 3199–3214 (2021).CrossRefGoogle Scholar
Hong, Z., Li, X., Wen, Z., Zhou, L., Chen, H. and Su, J., “Esp spoofing: Covert acoustic attack on mems gyroscopes in vehicles,” IEEE Trans Inf Forensics Secur 17, 3734–3747 (2022).CrossRefGoogle Scholar
Dasgupta, S., Rahman, M., Islam, M. and Chowdhury, M., “A sensor fusion-based gnss spoofing attack detection framework for autonomous vehicles,” IEEE Trans Intell Transp Syst 23(12), 23559–23572 (2022).CrossRefGoogle Scholar
Liu, J. and Park, J.-M., ““seeing is not always believing”: Detecting perception error attacks against autonomous vehicles,” IEEE Trans Dependable Secure Comput 18(5), 2209–2223 (2021).Google Scholar
Khazraei, A., Meng, H. and Pajic, M., “Stealthy Perception-Based Attacks on Unmanned Aerial Vehicles,” In: IEEE International Conference on Robotics and Automation (ICRA), London, UK (2023) pp. 3346–3352.Google Scholar
Fu, W., Qin, J., Shi, Y., Zheng, W. X. and Kang, Y., “Resilient consensus of discrete-time complex cyber-physical networks under deception attacks,” IEEE Trans Ind Electron 16(7), 4868–4877 (2020).Google Scholar
Deng, G., Zhou, Y., Xu, Y., Zhang, T. and Liu, Y., “An Investigation of Byzantine Threats in Multi-Robot Systems,” In: International Symposium on Research in Attacks, Intrusions and Defenses (RAID), San Sebastian, Spain (2021) pp. 17–32.Google Scholar
Wang, W., Han, Z., Liu, K. and Lü, J., “Distributed adaptive resilient formation control of uncertain nonholonomic mobile robots under deception attacks,” IEEE Trans Circuits Syst I Regul Pap 68(9), 3822–3835 (2021).CrossRefGoogle Scholar
Wang, Y., Zhao, C., Wang, H., Zhang, J. and Wang, D., “Secure Localization of Autonomous Articulated Vehicles: Attack Detection and Recovery,” In: International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Bizkaia, Spain (2023) pp. 2914–2919.Google Scholar
Castellano-Quero, M., Fernández-Madrigal, J.-A. and García-Cerezo, A., “Improving bayesian inference efficiency for sensory anomaly detection and recovery in mobile robots,” Expert Syst Appl 163, 113755 (2021).CrossRefGoogle Scholar
Akowuah, F., Prasad, R., Espinoza, C. O. and Kong, F., “Recovery-by-Learning: Restoring Autonomous Cyber-Physical Systems from Sensor Attacks,” In: International Conference on Embedded and Real-time Computing Systems and Applications (RTCSA), Houston, TX, USA (2021) pp. 61–66.Google Scholar
Wang, Y., Masoud, N. and Khojandi, A., “Real-time sensor anomaly detection and recovery in connected automated vehicle sensors,” IEEE Trans Intell Transp Syst 22(3), 1411–1421 (2020).CrossRefGoogle Scholar
Liu, Q., Mo, Y., Mo, X., Lv, C., Mihankhah, E. and Wang, D., “Secure Pose Estimation for Autonomous Vehicles Under Cyber Attacks,” In: IEEE Intelligent Vehicles Symposium (IV), Paris, France (2019) pp. 1583–1588.Google Scholar
Yousuf, S. and Kadri, M. B., “Information fusion of GPS, INS and odometer sensors for improving localization accuracy of mobile robots in indoor and outdoor applications,” Robotica 39(2), 250–276 (2021).CrossRefGoogle Scholar
Xu, Y., Han, X., Deng, G., Li, J., Liu, Y. and Zhang, T., “Sok: Rethinking Sensor Spoofing Attacks Against Robotic Vehicles from a Systematic View,” In: European Symposium on Security and Privacy (EuroS& P), Delft, Netherlands (2023) pp. 1082–1100.Google Scholar
Alkhawaja, F., Jaradat, M. A. and Romdhane, L., “Low-cost depth/IMU intelligent sensor fusion for indoor robot navigation,” Robotica 41(6), 1689–1717 (2023).CrossRefGoogle Scholar
Elsisi, M., Altius, M., Su, S.-F. and Su, C.-L., “Robust Kalman filter for position estimation of automated guided vehicles under cyberattacks,” IEEE Trans Instrum Meas 72, 1–12 (2023).CrossRefGoogle Scholar
Suliman, C., Cruceru, C. and Moldoveanu, F., “Mobile robot position estimation using the Kalman filter,” Acta Maris Seria Technol 6, 75 (2009).Google Scholar
Gosala, N., Bühler, A., Prajapat, M., Ehmke, C., Gupta, M., Sivanesan, R., Gawel, A., Pfeiffer, M., Bürki, M., Sa, I., Dubé, R. and Siegwart, R., “Redundant Perception and State Estimation for Reliable Autonomous Racing,” In: IEEE International Conference on Robotics and Automation (ICRA), Montreal, Canada (2019) pp. 6561–6567.Google Scholar
Zhang, Q.-B., Wang, P. and Chen, Z.-H., “An improved particle filter for mobile robot localization based on particle swarm optimization,” Expert Syst Appl 135, 181–193 (2019).CrossRefGoogle Scholar
Yaacoub, J.-P. A., Noura, H. N., Salman, O. and Chehab, A., “Robotics cyber security: Vulnerabilities, attacks, countermeasures, and recommendations,” Int J Inf Secur 21(1), 115–158 (2022).CrossRefGoogle ScholarPubMed
Zhou, L., Tzoumas, V., Pappas, G. J. and Tokekar, P., “Distributed attack-robust submodular maximization for multirobot planning,” IEEE Trans Robot 38(5), 3097–3112 (2022).CrossRefGoogle Scholar
Zhou, L. and Kumar, V., “Robust Multi-Robot Active Target Tracking Against Sensing and Communication Attacks,” In: American Control Conference (ACC), Atlanta, Georgia, USA (2022) pp. 4443–4450.Google Scholar
Zhao, Y., Sanán, D., Zhang, F. and Liu, Y., “Formal Specification and Analysis of Partitioning Operating Systems by Integrating Ontology and Refinement,” IEEE Trans Industr Inform 12(4), 1321–1331 (2016).CrossRefGoogle Scholar
Han, Y., Zhan, I. H., Zhao, W., Pan, J., Zhang, Z., Wang, Y. and Liu, Y.-J., “Deep reinforcement learning for robot collision avoidance with self-state-attention and sensor fusion,” IEEE Robot Autom Lett 7(3), 6886–6893 (2022).CrossRefGoogle Scholar
Van Den Berg, J., Guy, S. J., Lin, M. and Manocha, D., “Reciprocal n-Body Collision Avoidance,” In: Robotics Research: The 14th International Symposium ISRR (Berlin, Heidelberg, Springer, 2011) pp. 3–19.CrossRefGoogle Scholar