RSSI Map-Based Trajectory Design for UGV Against Malicious Radio Source: A Reinforcement Learning Approach

Zhaoyang Han, Yaoqi Yang, Weizheng Wang, Lu Zhou, Thippa Reddy Gadekallu, Mamoun Alazab, Prosanta Gope, Chunhua Su

    Research output: Contribution to journalArticlepeer-review

    14 Citations (Scopus)
    33 Downloads (Pure)

    Abstract

    Trajectory design is of great significance for the intelligent Unmanned Ground Vehicle (UGV) when performing various ground tasks. Though obstacle avoidance, speed control and other movement issues in the UGV navigation have been considered by the current research, the UGV path planning against malicious radio source is off the beaten path. To address such a research gap, we propose a reinforcement learning-based scheme to design UGV trajectory against malicious radio source as well as minimize the movement cost. Firstly, the malicious radio source detection and localization models are introduced after the Received Signal Strength Indicator (RSSI) map establishment. Then, the RSSI Map-based UGV trajectory design problem is formulated, where the movement cost and security risk are both concerned. To solve the formed problem, we propose a reinforcement learning-based trajectory design scheme, whose complexities are analyzed in detail. Finally, experiments are conducted under various parameter settings, where the simulation results evaluate the correctness and effectiveness of the proposed algorithm.

    Original languageEnglish
    Pages (from-to)4641-4650
    Number of pages10
    JournalIEEE Transactions on Intelligent Transportation Systems
    Volume24
    Issue number4
    DOIs
    Publication statusPublished - 1 Apr 2023

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