{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T06:00:09Z","timestamp":1780466409093,"version":"3.54.1"},"reference-count":28,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:00:00Z","timestamp":1767312000000},"content-version":"unspecified","delay-in-days":1,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Path planning, as a critical component of mobile robotic systems, significantly impacts operational efficiency and energy consumption ratios. State-of-the-art algorithms often suffer from inadequate real-time adjustment capability, insufficient dynamic environment adaptation, and suboptimal computational efficiency. To resolve these limitations, we propose a bidirectionally optimized path planning algorithm named Bidirectional Q-learning LPA* (BQ-LPA*), which incorporates three key innovations. Specifically, to enhance the global search capability of the LPA* framework, we replace fixed heuristic functions with a Q-learning-driven adaptive heuristic mechanism, which improves path quality through dynamic heuristic weighting and update strategies. Additionally, to improve the convergence rate and sample efficiency of Q-learning in complex environments, we propose integrating the LPA* framework to provide prior knowledge guidance, which can effectively minimize redundant exploration attempts by informed pathfinding initialization. Moreover, the Q-learning method inherently faces dimensionality challenges in high-dimensional continuous spaces, which manifest as action space congestion, storage bottlenecks, and computational inefficiency. To mitigate these risks, we devise an LPA*-based space discretization strategy that can reduce action space dimensionality and preserve the path feasibility. Experimental results show that, compared with mainstream path planning algorithms, BQ-LPA* achieves higher accuracy and faster convergence in mobile robot path planning.<\/jats:p>","DOI":"10.1017\/s0263574725103093","type":"journal-article","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T10:43:41Z","timestamp":1767350621000},"page":"52-70","source":"Crossref","is-referenced-by-count":2,"title":["A novel path planning algorithm based on synergistic bidirectional optimization"],"prefix":"10.1017","volume":"44","author":[{"given":"Shixuan","family":"Qi","sequence":"first","affiliation":[{"name":"Nanjing University of Posts and Telecommunications"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haofei","family":"Lu","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyi","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Gao","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyao","family":"Chen","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanjian","family":"Liu","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3623-1858","authenticated-orcid":false,"given":"Weigang","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"S0263574725103093_ref1","doi-asserted-by":"publisher","DOI":"10.3390\/en17164106"},{"key":"S0263574725103093_ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3321196"},{"key":"S0263574725103093_ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-015-1825-z"},{"key":"S0263574725103093_ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2023.116510"},{"key":"S0263574725103093_ref3","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574723001480"},{"key":"S0263574725103093_ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3514320"},{"key":"S0263574725103093_ref6","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574723001625"},{"key":"S0263574725103093_ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122510"},{"key":"S0263574725103093_ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2024.3387018"},{"key":"S0263574725103093_ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3434621"},{"key":"S0263574725103093_ref27","doi-asserted-by":"publisher","DOI":"10.5772\/10528"},{"key":"S0263574725103093_ref24","first-page":"279","article-title":"Q-learning","volume":"8","author":"Watkins","year":"1992","journal-title":"Mach. 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