{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T05:06:19Z","timestamp":1789707979428,"version":"4.0.1"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"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:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Dynamic multi-robot coordination demands real-time resilience against stochastic disruptions, yet existing planning methodologies often falter under the computational burden of high-dimensional state transitions. To address this challenge, we present a generative real-time mission planning framework that integrates deep prior learning with adaptive evolutionary optimization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>By employing a subtraction-average-based optimizer (SABO) to tune long short-term memory (LSTM) networks, we extract deep spatiotemporal priors that map discrete contingency events to continuous cost intervals, effectively compressing the feasible search space. Within this refined subspace, a guided whale optimization algorithm (GWOA), which is augmented by target-gap adaptive control and dimension-wise opposition learning, executes precise plan regeneration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Validation across six distinct contingency scenarios, ranging from risk-induced termination to agent paralysis, demonstrates that the proposed approach outperforms traditional reallocation baselines, achieving rapid responsiveness and superior solution stability. The framework also demonstrates scalability in large-scale scenarios involving up to 1000 agents.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These results demonstrate that integrating deep spatiotemporal prior learning with adaptive evolutionary optimization can effectively reduce the computational burden of mission replanning while maintaining responsiveness, stability, and scalability, providing a robust approach to resilient multi-robot management in unpredictable environments.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frobt.2026.1955600","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T04:34:09Z","timestamp":1789706049000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Generative real-time planning for multi-robot contingencies using deep prior-guided whale optimization"],"prefix":"10.3389","volume":"13","author":[{"given":"Xinyi","family":"Zhang","sequence":"first","affiliation":[{"name":"Xi\u2019an High-Tech Research Institute","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinqi","family":"Li","sequence":"additional","affiliation":[{"name":"Xi\u2019an High-Tech Research Institute","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbo","family":"Li","sequence":"additional","affiliation":[{"name":"Xi\u2019an High-Tech Research Institute","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"33","DOI":"10.3390\/axioms13010033","article-title":"An interval Type-2 fuzzy logic approach for dynamic parameter adaptation in a whale optimization algorithm applied to mathematical functions","volume":"13","author":"Amador-Angulo","year":"2023","journal-title":"Axioms"},{"key":"B2","doi-asserted-by":"publisher","first-page":"104723","DOI":"10.1016\/j.robot.2024.104723","article-title":"Reinforcement learning particle swarm optimization based trajectory planning of autonomous ground vehicle using 2D LiDAR point cloud","volume":"178","author":"Ambuj","year":"2024","journal-title":"Robot. Auton. Syst."},{"key":"B3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3564696","article-title":"Multiple Mobile robot task and motion planning: a survey","volume":"55","author":"Antonyshyn","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"B4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3700591","article-title":"A systematic literature review on multi-robot task allocation","volume":"57","author":"Athira","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"B5","doi-asserted-by":"publisher","first-page":"10921","DOI":"10.1007\/s10586-024-04522-3","article-title":"A multiobjective optimization of task workflow scheduling using hybridization of PSO and WOA algorithms in cloud-fog computing","volume":"27","author":"Bansal","year":"2024","journal-title":"Clust. Comput."},{"key":"B6","doi-asserted-by":"publisher","first-page":"129","DOI":"10.3390\/math11010129","article-title":"Continuous metaheuristics for binary optimization problems: an updated systematic literature review","volume":"11","author":"Becerra-Rozas","year":"2022","journal-title":"Mathematics"},{"key":"B7","doi-asserted-by":"publisher","first-page":"101081","DOI":"10.1016\/j.iot.2024.101081","article-title":"Blockchain-enabled trust management for secure content caching in Mobile edge computing using deep reinforcement learning","volume":"25","author":"Bounaira","year":"2024","journal-title":"Internet Things"},{"key":"B8","doi-asserted-by":"publisher","first-page":"104492","DOI":"10.1016\/j.robot.2023.104492","article-title":"Optimization techniques for multi-robot task allocation problems: review on the state-of-the-art","volume":"168","author":"Chakraa","year":"2023","journal-title":"Robot. Auton. Syst."},{"key":"B9","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/s10514-021-10022-9","article-title":"Dynamic multi-robot task allocation under uncertainty and temporal constraints","volume":"46","author":"Choudhury","year":"2021","journal-title":"Auton. Robots"},{"key":"B10","first-page":"1","article-title":"A learning approach to multi-robot task allocation with priority constraints and uncertainty","volume-title":"Proc. IEEE Int. Conf. Ind. Technol. (ICIT)","author":"Deng","year":"2022"},{"key":"B11","doi-asserted-by":"publisher","first-page":"16574","DOI":"10.1109\/ICRA57147.2024.10611370","article-title":"Multi-robot task allocation under uncertainty via hindsight optimization","author":"Dhanaraj","year":"2024","journal-title":"Proc. 2024 IEEE Int. Conf. Robot. Autom. (ICRA)"},{"key":"B12","doi-asserted-by":"publisher","first-page":"13046","DOI":"10.1109\/TASE.2025.3546281","article-title":"Proactive contingency-aware task allocation and scheduling in multi-robot multi-human cells via hindsight optimization","volume":"22","author":"Dhanaraj","year":"2025","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"B13","doi-asserted-by":"publisher","first-page":"102146","DOI":"10.1016\/j.jocs.2023.102146","article-title":"A fast and differentiable optimization model for finding the minimum dominating set in large-scale graphs","volume":"73","author":"Ding","year":"2023","journal-title":"J. Comput. Sci."},{"key":"B14","doi-asserted-by":"publisher","first-page":"4239","DOI":"10.3390\/math11204239","article-title":"Theoretical framework and practical considerations for achieving superior multi-robot exploration: hybrid cheetah optimization with intelligent initial configurations","volume":"11","author":"El Romeh","year":"2023","journal-title":"Mathematics"},{"key":"B15","doi-asserted-by":"publisher","first-page":"4924","DOI":"10.1109\/TIA.2025.3540788","article-title":"Enhancing EV charging station integration: a hybrid ARIMA-LSTM forecasting and optimization framework","volume":"61","author":"El-Afifi","year":"2025","journal-title":"IEEE Trans. Ind. Appl."},{"key":"B16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3665499","article-title":"A framework for simultaneous task allocation and planning under uncertainty","volume":"19","author":"Faruq","year":"2024","journal-title":"ACM Trans. Auton. Adapt. Syst."},{"key":"B17","doi-asserted-by":"publisher","first-page":"103905","DOI":"10.1016\/j.robot.2021.103905","article-title":"Multi-robot task allocation in disaster response: addressing dynamic tasks with deadlines and robots with range and payload constraints","volume":"147","author":"Ghassemi","year":"2022","journal-title":"Robot. Auton. Syst."},{"key":"B18","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1016\/j.procs.2024.04.048","article-title":"Efficient and robust multirobot navigation and task allocation using soft actor critic","volume":"235","author":"Hersi","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"B19","doi-asserted-by":"publisher","first-page":"4485","DOI":"10.1007\/s11831-024-10168-6","article-title":"Metaheuristics for solving global and engineering optimization problems: review, applications, open issues and challenges","volume":"31","author":"Houssein","year":"2024","journal-title":"Arch. Comput. Methods Eng."},{"key":"B20","doi-asserted-by":"publisher","first-page":"16415","DOI":"10.1109\/TASE.2025.3575762","article-title":"Reward-based collision-free algorithm for trajectory planning of autonomous robots","volume":"22","author":"Hoyos","year":"2025","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"B21","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3390\/lubricants12010010","article-title":"Time-frequency fusion features-based GSWOA-KELM model for gear fault diagnosis","volume":"12","author":"Hu","year":"2024","journal-title":"Lubricants"},{"key":"B22","doi-asserted-by":"publisher","first-page":"771520","DOI":"10.3389\/frobt.2021.771520","article-title":"Balancing collective exploration and exploitation in multi-agent and multi-robot systems: a review","volume":"8","author":"Kwa","year":"2022","journal-title":"Front. Robot. AI"},{"key":"B23","doi-asserted-by":"publisher","first-page":"433","DOI":"10.3233\/aic-220118","article-title":"Decision-making under uncertainty for multi-robot systems","volume":"35","author":"Lacerda","year":"2022","journal-title":"AI Commun."},{"key":"B24","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1016\/j.aej.2024.08.102","article-title":"Deep reinforcement learning path planning and task allocation for multi-robot collaboration","volume":"109","author":"Li","year":"2024","journal-title":"Alex. Eng. J."},{"key":"B25","first-page":"20","article-title":"Multi-robot collaboration for dynamic environments: a study based on the improved PPO algorithm","volume-title":"Proc. 8th Int. Conf. Adv. Algorithms Control Eng. (ICAACE)","author":"Li","year":"2025"},{"key":"B26","doi-asserted-by":"publisher","first-page":"773","DOI":"10.3390\/machines10090773","article-title":"A review of path-planning approaches for multiple Mobile robots","volume":"10","author":"Lin","year":"2022","journal-title":"Machines"},{"key":"B27","doi-asserted-by":"publisher","first-page":"3569","DOI":"10.3390\/app14093569","article-title":"Apparent resistivity variation imaging method based on magnetic field gradient by NGO-LSSVM for the ground-airborne frequency-domain electromagnetic method","volume":"14","author":"Liu","year":"2024","journal-title":"Appl. Sci."},{"key":"B28","doi-asserted-by":"publisher","first-page":"114498","DOI":"10.1016\/j.asoc.2025.114498","article-title":"A comprehensive review on multi robot task assignment and path planning: operational constraints, challenges, and potentials of deep reinforcement learning","volume":"188","author":"Mahmud","year":"2026","journal-title":"Appl. Soft Comput."},{"key":"B29","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1109\/LRA.2025.3632086","article-title":"Collaborative task allocation for heterogeneous multi-robot systems through iterative clustering","volume":"11","author":"Martin","year":"2026","journal-title":"IEEE Robot. Autom. Lett."},{"key":"B30","doi-asserted-by":"publisher","first-page":"553","DOI":"10.3390\/fractalfract7070553","article-title":"Metaheuristic-based algorithms for optimizing fractional-order controllers\u2014A recent, systematic, and comprehensive review","volume":"7","author":"Nassef","year":"2023","journal-title":"Fractal Fract."},{"key":"B31","doi-asserted-by":"publisher","first-page":"109337","DOI":"10.1016\/j.cnsns.2025.109337","article-title":"Wave optics optimizer: a novel meta-heuristic algorithm for engineering optimization","volume":"152","author":"Peng","year":"2026","journal-title":"Commun. Nonlinear Sci. Numer. Simul."},{"key":"B32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TSTE.2023.3262135","article-title":"Chance-constrained generic energy storage operations under decision-dependent uncertainty","volume":"14","author":"Qi","year":"2023","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"B33","doi-asserted-by":"publisher","first-page":"601243","DOI":"10.3389\/frobt.2020.601243","article-title":"Genetic fuzzy based scalable system of distributed robots for a collaborative task","volume":"7","author":"Sathyan","year":"2020","journal-title":"Front. Robot. AI"},{"key":"B34","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1007\/s42235-023-00469-0","article-title":"An improved golden jackal optimization algorithm based on multi-strategy mixing for solving engineering optimization problems","volume":"21","author":"Wang","year":"2024","journal-title":"J. Bionic Eng."},{"key":"B35","doi-asserted-by":"publisher","first-page":"1996","DOI":"10.3390\/buildings15121996","article-title":"Learning-enhanced differential evolution for multi-mode resource-constrained multi-project scheduling problem in industrial prefabrication","volume":"15","author":"Xing","year":"2025","journal-title":"Buildings"},{"key":"B36","doi-asserted-by":"publisher","first-page":"1528754","DOI":"10.3389\/frobt.2025.1528754","article-title":"A multi-user multi-robot multi-goal multi-device human-robot interaction manipulation benchmark","volume":"12","author":"Yoshida","year":"2025","journal-title":"Front. Robot. AI"},{"key":"B37","doi-asserted-by":"publisher","first-page":"19106","DOI":"10.1109\/TASE.2025.3593125","article-title":"Multi-UAV dynamic task assignment based on event-triggered graph reinforcement learning under weak communication","volume":"22","author":"Yu","year":"2025","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"B38","doi-asserted-by":"publisher","first-page":"960","DOI":"10.3390\/en17040960","article-title":"Short-term photovoltaic power prediction based on extreme learning machine with improved dung beetle optimization algorithm","volume":"17","author":"Zhang","year":"","journal-title":"Energies"},{"key":"B39","doi-asserted-by":"publisher","first-page":"118387","DOI":"10.1016\/j.enconman.2024.118387","article-title":"Self-adaptive enhanced learning differential evolution with surprisingly efficient decomposition approach for parameter identification of photovoltaic models","volume":"308","author":"Zhang","year":"","journal-title":"Energy Convers. Manag."},{"key":"B40","doi-asserted-by":"publisher","first-page":"100611","DOI":"10.1016\/j.patter.2022.100611","article-title":"Nature-inspired self-organizing collision avoidance for drone swarm based on reward-modulated spiking neural network","volume":"3","author":"Zhao","year":"2022","journal-title":"Patterns"},{"key":"B41","doi-asserted-by":"publisher","first-page":"4934","DOI":"10.3390\/app14114934","article-title":"A CNN-LSTM-Attention model for near-crash event identification on mountainous roads","volume":"14","author":"Zhao","year":"2024","journal-title":"Appl. Sci."},{"key":"B42","first-page":"1","article-title":"MamAM: a novel deep reinforcement learning architecture for multi-robot task allocation","volume-title":"Proc. Int. Conf. Neuromorphic Comput. (ICNC)","author":"Zhong","year":"2024"}],"container-title":["Frontiers in Robotics and AI"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frobt.2026.1955600\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T04:34:13Z","timestamp":1789706053000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frobt.2026.1955600\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,18]]},"references-count":42,"alternative-id":["10.3389\/frobt.2026.1955600"],"URL":"https:\/\/doi.org\/10.3389\/frobt.2026.1955600","relation":{},"ISSN":["2296-9144"],"issn-type":[{"value":"2296-9144","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,18]]},"article-number":"1955600"}}