{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:07:48Z","timestamp":1787029668386,"version":"build-2736575974"},"reference-count":21,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:00:00Z","timestamp":1773014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"open project special fund of Intelligent Transportation Laboratory in Shanxi Province","award":["2024-ITLOP-KD-05"],"award-info":[{"award-number":["2024-ITLOP-KD-05"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Tunnel lighting cleaning is of significant practical importance for improving driving safety. To address the low operational efficiency of tunnel lighting cleaning tasks, a trajectory planning method based on the chaotic adaptive whale\u2013particle swarm optimization (CAW-PSO) algorithm is proposed. Taking the SIASUN GCR16-2000 robotic arm as the research object, the trajectory is constructed using a 3-5-3 polynomial interpolation, with the objective of achieving time-optimal trajectory planning. In the CAW-PSO algorithm, a tent chaotic map is introduced to improve the quality of the population; a linearly decreasing inertia weight is designed to strike a balance between local and global search; dynamic learning factors are defined to strengthen the individual learning ability and global cognitive capability of particles; finally, the exploitation mechanism of the whale optimization algorithm is employed to avoid getting trapped in local optima and improve convergence accuracy. The simulation time is 3.661 s, a reduction of 69.94%. The experimental results yielded a mean relative error of 1.16%, indicating good agreement with the simulation results. The results of the simulation and experiment indicate that the CAW-PSO effectively reduces the motion time of the robotic arm, exhibiting superior applicability in trajectory planning for tunnel lighting cleaning robotic arms.<\/jats:p>","DOI":"10.3390\/s26051722","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T10:16:12Z","timestamp":1773051372000},"page":"1722","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Robotic Arm Trajectory Planning for Tunnel Lighting Cleaning Based on the CAW-PSO Algorithm"],"prefix":"10.3390","volume":"26","author":[{"given":"Zhibin","family":"Yao","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4433-4498","authenticated-orcid":false,"given":"Taibo","family":"Song","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanxi Provincial Intelligent Transportation Laboratory Co., Ltd., Taiyuan 030032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanlong","family":"Li","sequence":"additional","affiliation":[{"name":"Shanxi Provincial Intelligent Transportation Laboratory Co., Ltd., Taiyuan 030032, China"},{"name":"School of Vehicle and Transportation Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Vega-Heredia, M., Muhammad, I., Ghanta, S., Ayyalusami, V., Aisyah, S., and Elara, M.R. (2020). Multi-sensor orientation tracking for a fa\u00e7ade-cleaning robot. Sensors, 20.","DOI":"10.3390\/s20051483"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sun, H., Xu, R., Luo, J., and Cheng, H. (2025). Review of the Application of UAV Edge Computing in Fire Rescue. Sensors, 25.","DOI":"10.3390\/s25113304"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Valero, S., Martinez, J.C., Montes, A.M., Mar\u00edn, C., Bola\u00f1os, R., and \u00c1lvarez, D. (2025). Machine vision-assisted design of end effector pose in robotic mixed depalletizing of heterogeneous cargo. Sensors, 25.","DOI":"10.3390\/s25041137"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ji, Y., and Yu, J. (2025). Time-Impact Optimal Trajectory Planning for Wafer-Handling Robotic Arms Based on the Improved Snake Optimization Algorithm. Sensors, 25.","DOI":"10.20944\/preprints202502.0880.v1"},{"key":"ref_5","unstructured":"Eberhart, R., and Kennedy, J. (1995, January 4\u20136). A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.future.2024.04.008","article-title":"Quantum particle swarm optimization algorithm based on diversity migration strategy","volume":"157","author":"Gong","year":"2024","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"122780","DOI":"10.1016\/j.eswa.2023.122780","article-title":"An improved levy chaotic particle swarm optimization algorithm for energy-efficient cluster routing scheme in industrial wireless sensor networks","volume":"241","author":"Luo","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"101890","DOI":"10.1016\/j.swevo.2025.101890","article-title":"Adaptive distance-based multi-objective particle swarm optimization algorithm with simple position update","volume":"94","author":"Wang","year":"2025","journal-title":"Swarm Evol. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"126398","DOI":"10.1016\/j.eswa.2025.126398","article-title":"t-SNE-PSO: Optimizing t-SNE using particle swarm optimization","volume":"269","author":"Allaoui","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3076","DOI":"10.1007\/s42235-024-00578-4","article-title":"An opposition-based learning adaptive chaotic particle swarm optimization algorithm","volume":"21","author":"Jiao","year":"2024","journal-title":"J. Bionic Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3658","DOI":"10.1016\/j.asoc.2011.01.037","article-title":"A novel particle swarm optimization algorithm with adaptive inertia weight","volume":"11","author":"Nickabadi","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1715","DOI":"10.1016\/j.chaos.2007.09.063","article-title":"Chaos embedded particle swarm optimization algorithms","volume":"40","author":"Alatas","year":"2009","journal-title":"Chaos Solitons Fractals"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"109766","DOI":"10.1016\/j.ijepes.2023.109766","article-title":"Optimal scheduling of electric vehicle ordered charging and discharging based on improved gravitational search and particle swarm optimization algorithm","volume":"157","author":"Pan","year":"2024","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"110851","DOI":"10.1016\/j.cie.2024.110851","article-title":"Two-dimensional bin-packing problem with conflicts and load balancing: A hybrid chaotic and evolutionary particle swarm optimization algorithm","volume":"200","author":"Sun","year":"2025","journal-title":"Comput. Ind. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"117791","DOI":"10.1016\/j.cma.2025.117791","article-title":"MSFPSO: Multi-algorithm integrated particle swarm optimization with novel strategies for solving complex engineering design problems","volume":"437","author":"Shu","year":"2025","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"120704","DOI":"10.1016\/j.oceaneng.2025.120704","article-title":"An enhanced L\u00e9vy flight quantum particle swarm optimization for stress monitoring in jacket wind turbines","volume":"324","author":"Yan","year":"2025","journal-title":"Ocean Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"102115","DOI":"10.1016\/j.swevo.2025.102115","article-title":"Collaborative knowledge transfer-based multiobjective multitask particle swarm optimization","volume":"98","author":"Wang","year":"2025","journal-title":"Swarm Evol. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"40470","DOI":"10.1109\/JIOT.2025.3589644","article-title":"Multi-Strategy Improved Particle Swarm Optimization Algorithm for Path Planning of UAV in 3-D Low Altitude Urban Environment","volume":"12","author":"Tao","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"110882","DOI":"10.1016\/j.engappai.2025.110882","article-title":"Hybrid Gaussian quantum particle swarm optimization and adaptive genetic algorithm for flexible job-shop scheduling problem","volume":"154","author":"Xu","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"127423","DOI":"10.1016\/j.eswa.2025.127423","article-title":"Particle swarm optimization based on particle perturbation and elite preservation strategies for dynamic optimization problems","volume":"279","author":"Li","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_21","first-page":"69","article-title":"A modified particle swarm optimizer","volume":"890","author":"Shi","year":"1998","journal-title":"Evol. Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/5\/1722\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T10:24:51Z","timestamp":1773051891000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/5\/1722"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,9]]},"references-count":21,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["s26051722"],"URL":"https:\/\/doi.org\/10.3390\/s26051722","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,9]]}}}