{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T18:38:53Z","timestamp":1781203133789,"version":"3.54.1"},"reference-count":0,"publisher":"Cambridge University Press (CUP)","issue":"8","license":[{"start":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T00:00:00Z","timestamp":1726617600000},"content-version":"unspecified","delay-in-days":48,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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\u2019 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 <jats:monospace>ObsGAN-DRL<\/jats:monospace>, integrating a generative adversarial network (GAN) into DRL models to mitigate OLAs in the environment. First, <jats:monospace>ObsGAN-DRL<\/jats:monospace> learns a generator based on the GAN model to compute the approximation of obstacles\u2019 accurate positions in benign and attack scenarios. Therefore, no detectors are required for <jats:monospace>ObsGAN-DRL<\/jats:monospace>. Second, by using the approximation positions of the surrounding obstacles, <jats:monospace>ObsGAN-DRL<\/jats:monospace> can leverage the state-of-the-art DRL methods to compute collision-free motion commands (e.g., velocity) efficiently. Comprehensive experiments show that <jats:monospace>ObsGAN-DRL<\/jats:monospace> can mitigate OLAs effectively and guarantee safety. We also demonstrate the generalization of <jats:monospace>ObsGAN-DRL<\/jats:monospace>.<\/jats:p>","DOI":"10.1017\/s0263574724001115","type":"journal-article","created":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T10:32:43Z","timestamp":1726655563000},"page":"2781-2800","source":"Crossref","is-referenced-by-count":2,"title":["Robust motion planning for mobile robots under attacks against obstacle localization"],"prefix":"10.1017","volume":"42","author":[{"given":"Fenghua","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0125-1939","authenticated-orcid":false,"given":"Wenbing","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1583-7570","authenticated-orcid":false,"given":"Yuan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shang-Wei","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuohua","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2024,9,18]]},"container-title":["Robotica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0263574724001115","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T06:20:53Z","timestamp":1731392453000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0263574724001115\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":0,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["S0263574724001115"],"URL":"https:\/\/doi.org\/10.1017\/s0263574724001115","relation":{},"ISSN":["0263-5747","1469-8668"],"issn-type":[{"value":"0263-5747","type":"print"},{"value":"1469-8668","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8]]}}}