{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T03:42:00Z","timestamp":1770262920571,"version":"3.49.0"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T00:00:00Z","timestamp":1770163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Robot. AI"],"abstract":"<jats:p>Substation robots face significant challenges in path planning due to the complex electromagnetic environment, dense equipment layout, and safety-critical operational requirements. This paper proposes a path planning algorithm based on deep reinforcement learning enhanced by ant colony optimization, establishing a synergistic optimization framework that combines bio-inspired algorithms with deep learning. The proposed method addresses critical path planning issues in substation inspection and maintenance operations. The approach includes: 1) designing a pheromone-guided exploration strategy that transforms environmental prior knowledge into spatial bias to reduce ineffective exploration; 2) establishing a high-quality sample screening mechanism that enhances Q-network training through ant colony path experience to improve sample efficiency; 3) implementing dynamic decision weight adjustment that enables gradual transition from heuristic guidance to autonomous learning decisions. Experimental results in complex environments demonstrate the method\u2019s superiority. Compared to state-of-the-art baselines including PPO, DDQN, and A*, the proposed method achieves 24% higher sample efficiency, 18% reduction in average path length, and superior dynamic obstacle avoidance. Field validation in a 2,500-square-meter substation confirms a 14.8% improvement in task completion rate compared to standard DRL approaches.<\/jats:p>","DOI":"10.3389\/frobt.2025.1759501","type":"journal-article","created":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T13:43:49Z","timestamp":1770212629000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A substation robot path planning algorithm based on deep reinforcement learning enhanced by ant colony optimization"],"prefix":"10.3389","volume":"12","author":[{"given":"Hongwei","family":"Zhang","sequence":"first","affiliation":[{"name":"Guangzhou Power Supply Bureau, Guangdong Power Grid Co., LTD.","place":["Guangdong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijun","family":"Sun","sequence":"additional","affiliation":[{"name":"Guangzhou Power Supply Bureau, Guangdong Power Grid Co., LTD.","place":["Guangdong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weihong","family":"Tan","sequence":"additional","affiliation":[{"name":"Guangzhou Power Supply Bureau, Guangdong Power Grid Co., LTD.","place":["Guangdong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyu","family":"Bao","sequence":"additional","affiliation":[{"name":"Guangzhou Power Supply Bureau, Guangdong Power Grid Co., LTD.","place":["Guangdong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing","family":"He","sequence":"additional","affiliation":[{"name":"Guangzhou Power Supply Bureau, Guangdong Power Grid Co., LTD.","place":["Guangdong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinguo","family":"Chen","sequence":"additional","affiliation":[{"name":"Guangzhou Power Supply Bureau, Guangdong Power Grid Co., LTD.","place":["Guangdong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,4]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1109\/tnsm.2024.3485196","article-title":"O-DQR: a multi-agent deep reinforcement learning for multihop routing in overlay networks","volume":"22","author":"Alliche","year":"2024","journal-title":"IEEE Trans. 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