{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T02:14:14Z","timestamp":1783995254075,"version":"3.55.0"},"reference-count":43,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T00:00:00Z","timestamp":1771027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Entrepreneurship College of Chengdu University","award":["202511079004S"],"award-info":[{"award-number":["202511079004S"]}]},{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan Province","doi-asserted-by":"crossref","award":["2025ZNSFSC0642"],"award-info":[{"award-number":["2025ZNSFSC0642"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To address the issues of traditional grid-based Ant Colony Optimization path planning in discretized continuous space\u2014including limited direction freedom, lack of global topological guidance, and difficulty in balancing path smoothness and safety margin\u2014a topological progress potential-enhanced continuous-space ant colony path planning algorithm (TPP-CSACO) is proposed. TPP-CSACO discards grid-based expansion; instead, a perception circle centered on each ant is defined, movement is executed via a sector-based perception framework with probabilistic direction selection, and band-shaped decaying pheromones are deposited along the path. By coupling the global topological progress potential derived from the simplified probabilistic roadmap (PRM) with pheromones, a dual-field guidance mechanism is established to prevent local congestion. Combined with the explicit safety constraints of the signed distance field (SDF), an adaptive step size strategy that integrates elastic step size and frustration-induced temperature rise is introduced to enhance obstacle avoidance and search stability. Results from repeated experiments on multiscale constrained maps (conducted against six typical algorithms and the traditional ACO) show that compared with ACO, TPP-CSACO reduces the path length by up to 50.6% in the same environment, while achieving faster convergence and maintaining good search diversity. Although the path length increases slightly (by a maximum of 5.9%) compared with the shortest heuristic algorithms, the maximum turning angle is reduced by 75% to 93%, and a 100% success rate and zero safety violations are realized. This indicates that TPP-CSACO has achieved a relatively stable balance among safety, smoothness, and global search capability.<\/jats:p>","DOI":"10.3390\/s26041264","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T08:38:39Z","timestamp":1771231119000},"page":"1264","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Topological Progress Potential-Enhanced Continuous-Space Ant Colony Algorithm for Robot Path Planning"],"prefix":"10.3390","volume":"26","author":[{"given":"Guikun","family":"Dong","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feixiong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaxiong","family":"Zhuo","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiaoling","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2885-0117","authenticated-orcid":false,"given":"Xiangjun","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s10845-021-01867-z","article-title":"A Review of Motion Planning Algorithms for Intelligent Robots","volume":"33","author":"Zhou","year":"2022","journal-title":"J. Intell. Manuf."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Raj, R., and Kos, A. (2022). A Comprehensive Study of Mobile Robot: History, Developments, Applications, and Future Research Perspectives. Appl. Sci., 12.","DOI":"10.3390\/app12146951"},{"key":"ref_3","first-page":"215","article-title":"Autonomous Trajectory Tracking Control Method for an Agricultural Robotic Vehicle","volume":"17","author":"Yan","year":"2024","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100083","DOI":"10.1109\/ACCESS.2023.3311519","article-title":"Mobile Service Robot Path Planning Using Deep Reinforcement Learning","volume":"11","author":"Kumaar","year":"2023","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2079","DOI":"10.1017\/S0263574723000322","article-title":"A Comprehensive Review of the Latest Path Planning Developments for Multi-Robot Formation Systems","volume":"41","author":"Abujabal","year":"2023","journal-title":"Robotica"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102053","DOI":"10.1016\/j.rcim.2020.102053","article-title":"Path Planning of Cooperating Industrial Robots Using Evolutionary Algorithms","volume":"67","author":"Larsen","year":"2021","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.arcontrol.2020.10.001","article-title":"A Comparative Review on Mobile Robot Path Planning: Classical or Meta-Heuristic Methods?","volume":"50","author":"Wahab","year":"2020","journal-title":"Annu. Rev. Control"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gonzalez, R., Kloetzer, M., and Mahulea, C. (2017). Comparative Study of Trajectories Resulted from Cell Decomposition Path Planning Approaches. Proceedings of the 21st International Conference on System Theory, Control and Computing, Sinaia, Romania, 19\u201321 October 2017, IEEE.","DOI":"10.1109\/ICSTCC.2017.8107010"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1109\/JAS.2021.1004252","article-title":"An Adaptive Rapidly-Exploring Random Tree","volume":"9","author":"Li","year":"2022","journal-title":"IEEECAA J. Autom. Sin."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"147827","DOI":"10.1109\/ACCESS.2020.3015976","article-title":"Surface Optimal Path Planning Using an Extended Dijkstra Algorithm","volume":"8","author":"Luo","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.protcy.2016.03.010","article-title":"Time-Efficient A* Algorithm for Robot Path Planning","volume":"23","author":"Guruji","year":"2016","journal-title":"Procedia Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"108276","DOI":"10.1109\/ACCESS.2022.3212741","article-title":"Reliable Path Planning Algorithm Based on Improved Artificial Potential Field Method","volume":"10","author":"Luo","year":"2022","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"102196","DOI":"10.1016\/j.rcim.2021.102196","article-title":"Path Planning for Manipulators Based on an Improved Probabilistic Roadmap Method","volume":"72","author":"Chen","year":"2021","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1109\/70.508439","article-title":"Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces","volume":"12","author":"Kavraki","year":"1996","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Dai, S., Schaffert, S., Jasour, A., Hofmann, A., and Williams, B. (2019). Chance Constrained Motion Planning for High-Dimensional Robots. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 20\u201324 May 2019, IEEE.","DOI":"10.1109\/ICRA.2019.8793660"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/TAI.2025.3584023","article-title":"Dynamic Obstacle Avoidance Using Path Reshaping on Probabilistic Roadmaps for High-Degree-of-Freedom Robots","volume":"7","author":"Ojha","year":"2025","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"53296","DOI":"10.1109\/ACCESS.2018.2871222","article-title":"Path Planning of Industrial Robot Based on Improved RRT Algorithm in Complex Environments","volume":"6","author":"Zhang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106821","DOI":"10.1016\/j.jfranklin.2024.106821","article-title":"Hierarchical Path Planner Combining Probabilistic Roadmap and Deep Deterministic Policy Gradient for Unmanned Ground Vehicles with Non-Holonomic Constraints","volume":"361","author":"Fan","year":"2024","journal-title":"J. Frankl. Inst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2729","DOI":"10.1109\/LRA.2021.3062008","article-title":"Autonomous UAV Exploration of Dynamic Environments via Incremental Sampling and Probabilistic Roadmap","volume":"6","author":"Xu","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"104633","DOI":"10.1016\/j.robot.2024.104633","article-title":"A Collision-Free Path Planning Approach Based on Rule Guided Lazy-PRM with Repulsion Field for Gantry Welding Robots","volume":"174","author":"Zhou","year":"2024","journal-title":"Robot. Auton. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s00521-021-06569-4","article-title":"A Review of Artificial Intelligence Applied to Path Planning in UAV Swarms","volume":"34","author":"Rivero","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"74557","DOI":"10.1109\/ACCESS.2018.2882875","article-title":"RL and ANN Based Modular Path Planning Controller for Resource-Constrained Robots in the Indoor Complex Dynamic Environment","volume":"6","author":"Ullah","year":"2018","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3369","DOI":"10.1007\/s10462-022-10257-7","article-title":"A Review of the Literature on Fuzzy-Logic Approaches for Collision-Free Path Planning of Manipulator Robots","volume":"56","author":"Hentout","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.procs.2018.01.054","article-title":"Grid Path Planning with Deep Reinforcement Learning: Preliminary Results","volume":"123","author":"Panov","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1109\/TIV.2021.3082151","article-title":"Bio-Inspired Neural Network-Based Optimal Path Planning for UUVs under the Effect of Ocean Currents","volume":"7","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"110642","DOI":"10.1016\/j.engappai.2025.110642","article-title":"The Optimization-Based Fuzzy Logic Controllers for Autonomous Ground Vehicle Path Tracking","volume":"151","author":"Aliskan","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"8270","DOI":"10.1109\/ACCESS.2024.3521969","article-title":"Efficient Path Planning and Formation Control in Multi-Robot Systems: A Neural Fields and Auto-Switching Mechanism Approach","volume":"13","author":"Fareh","year":"2025","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Poudel, S., Arafat, M.Y., and Moh, S. (2023). Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey. Sensors, 23.","DOI":"10.3390\/s23063051"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.procs.2018.01.113","article-title":"Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning","volume":"127","author":"Lamini","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1109\/TII.2012.2198665","article-title":"Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning","volume":"9","author":"Roberge","year":"2013","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"121944","DOI":"10.1109\/ACCESS.2021.3108973","article-title":"An Improved Grey Wolf Optimization Algorithm and Its Application in Path Planning","volume":"9","author":"Liu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"107230","DOI":"10.1016\/j.cie.2021.107230","article-title":"Path Planning Optimization of Indoor Mobile Robot Based on Adaptive Ant Colony Algorithm","volume":"156","author":"Miao","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"ref_33","first-page":"436","article-title":"Ant Colony Optimization Algorithm for Robot Path Planning","volume":"Volume 3","author":"Brand","year":"2010","journal-title":"Proceedings of the 2010 International Conference on Computer Design and Applications, Qinhuangdao, China, 25\u201327 June 2010"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"119410","DOI":"10.1016\/j.eswa.2022.119410","article-title":"Modified Adaptive Ant Colony Optimization Algorithm and Its Application for Solving Path Planning of Mobile Robot","volume":"215","author":"Wu","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"110868","DOI":"10.1016\/j.asoc.2023.110868","article-title":"A Mixing Algorithm of ACO and ABC for Solving Path Planning of Mobile Robot","volume":"148","author":"Li","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"111459","DOI":"10.1016\/j.knosys.2024.111459","article-title":"Multi-Strategy Adaptable Ant Colony Optimization Algorithm and Its Application in Robot Path Planning","volume":"288","author":"Cui","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"109696","DOI":"10.1016\/j.compag.2024.109696","article-title":"Path Planning and Tracking Control of Orchard Wheel Mower Based on BL-ACO and GO-SMC","volume":"228","author":"Liu","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1016\/j.aej.2025.09.036","article-title":"GOA-ACO: A Goose Optimized Ant Colony Algorithm for the Automated Guided Vehicle Path Planning","volume":"130","author":"Sheng","year":"2025","journal-title":"Alex. Eng. J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"122407","DOI":"10.1016\/j.ins.2025.122407","article-title":"Research on Robot Path Planning Based on Multi-Strategy Genetic Ant Colony Optimization Algorithm","volume":"718","author":"Li","year":"2025","journal-title":"Inf. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"111890","DOI":"10.1016\/j.engappai.2025.111890","article-title":"Research on Mobile Robot Path Planning Based on Improved Q-Evaluation Ant Colony Optimization Algorithm","volume":"160","author":"Li","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1016\/j.ejor.2006.06.046","article-title":"Ant Colony Optimization for Continuous Domains","volume":"185","author":"Socha","year":"2008","journal-title":"Eur. J. Oper. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"110034","DOI":"10.1016\/j.compeleceng.2024.110034","article-title":"Path Planning for Unmanned Aerial Vehicles in Complex Environment Based on an Improved Continuous Ant Colony Optimisation","volume":"123","author":"Niu","year":"2025","journal-title":"Comput. Electr. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wang, Y., Liu, J., Qian, Y., and Yi, W. (2025). Path Planning for Multi-UAV in a Complex Environment Based on Reinforcement-Learning-Driven Continuous Ant Colony Optimization. Drones, 9.","DOI":"10.3390\/drones9090638"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/4\/1264\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T05:12:10Z","timestamp":1771305130000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/4\/1264"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,14]]},"references-count":43,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["s26041264"],"URL":"https:\/\/doi.org\/10.3390\/s26041264","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,14]]}}}