{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T04:44:41Z","timestamp":1777092281594,"version":"3.51.4"},"reference-count":26,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T00:00:00Z","timestamp":1776643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Multi-Robot Systems (MRS) demand optimal spatial resource configuration to ensure systemic efficiency in mission-critical applications. Conventional paradigms rely on rigid coverage-first principles, prioritizing exhaustive spatial scanning over rapid target discovery, thereby compromising systemic responsiveness. To bridge this gap, this study proposes the Attraction of Unknown area Centroid for Exploration (AUCE) architecture, a centralized framework designed to simultaneously optimize global exploration efficiency and early-stage target discovery rates. The control framework incorporates a dynamic region planning strategy that adaptively modulates the systemic search focus based on the specific field of view of autonomous agents, alongside an optimized S-shaped trajectory pattern to establish a rigorous balance between localized path simplicity and global coverage. A versatile profit function synthesizing constant and time-varying coefficient strategies explicitly regulates the systemic trade-off between accelerated early-stage target discovery and global path cost minimization. Quantitative simulations demonstrate that AUCE significantly outperforms established methods by mitigating redundant path costs and generating a distinct front-loading effect to accelerate target localization. Subsequent evaluations confirm the framework\u2019s computational scalability in expanded swarms and its systemic adaptability when navigating static obstacles.<\/jats:p>","DOI":"10.3390\/systems14040450","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T14:53:23Z","timestamp":1776696803000},"page":"450","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Region Planning and Profit-Adaptive Collaborative Search Strategies for Multi-Robot Systems"],"prefix":"10.3390","volume":"14","author":[{"given":"Zeyu","family":"Xu","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Xue","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Decheng","family":"Kong","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cai, C., Chen, J., Ayub, M.S., and Liu, F. (2023). A Task Allocation Method for Multi-AUV Search and Rescue with Possible Target Area. J. Mar. Sci. Eng., 11.","DOI":"10.3390\/jmse11040804"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"14246","DOI":"10.1109\/ACCESS.2019.2894524","article-title":"Hybrid Stochastic Exploration Using Grey Wolf Optimizer and Coordinated Multi-Robot Exploration Algorithms","volume":"7","author":"Albina","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1007\/s10514-017-9631-3","article-title":"Simultaneous Area Partitioning and Allocation for Complete Coverage by Multiple Autonomous Industrial Robots","volume":"41","author":"Hassan","year":"2017","journal-title":"Auton. Robot."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lin, H.-Y., and Huang, Y.-C. (2021). Collaborative Complete Coverage and Path Planning for Multi-Robot Exploration. Sensors, 21.","DOI":"10.3390\/s21113709"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Shamsoshoara, A., Afghah, F., Razi, A., Mousavi, S., Ashdown, J., and Turk, K. (2019). An Autonomous Spectrum Management Scheme for Unmanned Aerial Vehicle Networks in Disaster Relief Operations. arXiv.","DOI":"10.1109\/ACCESS.2020.2982932"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"El Romeh, A., and Mirjalili, S. (2023). Multi-Robot Exploration of Unknown Space Using Combined Meta-Heuristic Salp Swarm Algorithm and Deterministic Coordinated Multi-Robot Exploration. Sensors, 23.","DOI":"10.3390\/s23042156"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6225","DOI":"10.1109\/TVT.2023.3341878","article-title":"Multi-UAV Collaborative Dynamic Task Allocation Method Based on ISOM and Attention Mechanism","volume":"73","author":"Wu","year":"2024","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"30989","DOI":"10.1109\/ACCESS.2024.3368851","article-title":"Multi-UAV Reconnaissance Task Allocation in 3D Urban Environments","volume":"12","author":"Tian","year":"2024","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1007\/s40815-017-0395-x","article-title":"An Improved Spinal Neural System-Based Approach for Heterogeneous AUVs Cooperative Hunting","volume":"20","author":"Ni","year":"2018","journal-title":"Int. J. Fuzzy Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"119310","DOI":"10.1109\/ACCESS.2021.3108177","article-title":"A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms","volume":"9","author":"Tan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Muhsen, D.K., Sadiq, A.T., and Raheem, F.A. (2024). A Survey on Swarm Robotics for Area Coverage Problem. Algorithms, 17.","DOI":"10.3390\/a17010003"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7511","DOI":"10.1109\/TIE.2023.3301541","article-title":"Multirobot Target Searches in Unknown Environments Via Waypoint Planning System","volume":"71","author":"Yu","year":"2024","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"112025","DOI":"10.1016\/j.asoc.2024.112025","article-title":"Minimum Time Search Using Ant Colony Optimization for Multiple Fixed-Wing UAVs in Dynamic Environments","volume":"165","author":"Motamedi","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3705","DOI":"10.1109\/TRO.2023.3281483","article-title":"Impedance Learning for Human-Guided Robots in Contact with Unknown Environments","volume":"39","author":"Xing","year":"2023","journal-title":"IEEE Trans. Robot."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lyu, M., Zhao, Y., Huang, C., and Huang, H. (2023). Unmanned Aerial Vehicles for Search and Rescue: A Survey. Remote Sens., 15.","DOI":"10.3390\/rs15133266"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yan, X., Zeng, Z., He, K., and Hong, H. (2023). Multi-Robot Cooperative Autonomous Exploration via Task Allocation in Terrestrial Environments. Front. Neurorobot., 17.","DOI":"10.3389\/fnbot.2023.1179033"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, C., Guo, J., Guo, S., and Fu, Q. (2022, January 7\u201310). Study on Collaborative Task Assignment of Sphere Multi-Robot Based on Group Intelligence Algorithm. Proceedings of the 2022 IEEE International Conference on Mechatronics and Automation (ICMA), Guilin, China.","DOI":"10.1109\/ICMA54519.2022.9856105"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Andreychuk, A., Yakovlev, K., Panov, A., and Skrynnik, A. (2025, January 19\u201325). Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-Tuning 2025. Proceedings of the 2025 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hangzhou, China.","DOI":"10.1109\/IROS60139.2025.11247645"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Poddar, P., Esfahani, E.T., Dantu, K., and Chowdhury, S. (2025, January 17\u201321). Automated Generation of Diverse Courses of Actions for Multi-Agent Operations Using Binary Optimization and Graph Learning. Proceedings of the 2025 IEEE 21st International Conference on Automation Science and Engineering (CASE), Los Angeles, CA, USA.","DOI":"10.1109\/CASE58245.2025.11163864"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cani, J., Koletsis, P., Foteinos, K., Kefaloukos, I., Argyriou, L., Falelakis, M., Pino, I.D., Santamaria-Navarro, A., \u010cech, M., and Severa, O. (2025, January 4\u20136). TRIFFID: Autonomous Robotic Aid for Increasing First Responders Efficiency. Proceedings of the 2025 6th International Conference in Electronic Engineering & Information Technology (EEITE), Chania, Greece.","DOI":"10.1109\/EEITE65381.2025.11166443"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mahato, P., Saha, S., Sarkar, C., and Shaghil, M. (2022). Consensus-Based Fast and Energy-Efficient Multi-Robot Task Allocation. arXiv.","DOI":"10.1016\/j.robot.2022.104270"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"128008","DOI":"10.1016\/j.neucom.2024.128008","article-title":"A Survey on Collaborative Hunting with Robotic Swarm: Key Technologies and Application Scenarios","volume":"598","author":"Cai","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"012037","DOI":"10.1088\/1742-6596\/2798\/1\/012037","article-title":"A Review of Coordinated Multi-Robot Exploration","volume":"2798","author":"Hu","year":"2024","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ghauri, S.A., Sarfraz, M., Qamar, R.A., Sohail, M.F., and Khan, S.A. (2024). A Review of Multi-UAV Task Allocation Algorithms for a Search and Rescue Scenario. J. Sens. Actuator Netw., 13.","DOI":"10.3390\/jsan13050047"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ghods, R., Durkin, W.J., and Schneider, J. (2021). Multi-Agent Active Search Using Realistic Depth-Aware Noise Model. arXiv.","DOI":"10.1109\/ICRA48506.2021.9561598"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"9199","DOI":"10.1109\/LRA.2024.3432351","article-title":"APF-CPP: An Artificial Potential Field Based Multi-Robot Online Coverage Path Planning Approach","volume":"9","author":"Wang","year":"2024","journal-title":"IEEE Robot. Autom. Lett."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/4\/450\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T04:16:19Z","timestamp":1777090579000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/4\/450"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,20]]},"references-count":26,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["systems14040450"],"URL":"https:\/\/doi.org\/10.3390\/systems14040450","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,20]]}}}