Resource‑ and Energy‑Aware Multi‑Robot Operations: Scheduling, Recharging, and Long‑Horizon Persistence

Battery capacity, recharge logistics, and computational budgets are now the primary bottlenecks for scaling multi robot systems from short demos to dependable, 24/7 operations. Long horizon missions—warehousing, inspection, agriculture, environmental monitoring, and public safety—require fleets to coordinate tasks, routes, and recharge events while accounting for battery aging, stochastic demand, uncertain travel times, and heterogeneous platforms. Classical planning or perception advances alone are insufficient: persistence hinges on resource models, predictive energy usage, and infrastructure aware scheduling that closes the loop between algorithms, chargers, and the physical environment. This Research Topic focuses on the algorithmic and systems foundations that make multi robot autonomy sustainable and economically viable over days to months, emphasizing measurable mission availability, throughput, safety under energy constraints, and total cost of ownership.The goal is to catalyze a coherent research agenda for resource and energy aware multi robot operations. We seek contributions that: Formulate principled models for energy consumption, battery state of charge/state of health, thermal limits, and degradation aware planning; Develop fleet level task allocation, routing, and recharge scheduling with provable guarantees (e.g., feasibility, competitive ratios, or MPC stability) and practical performance at scale; Integrate infrastructure decisions—charger placement, capacities, mobile energy replenishment, battery swapping, and opportunity charging—into operational planning; Jointly optimize multi objective criteria (energy, latency, throughput, service levels, safety) under uncertainty via learning augmented optimization, robust/online methods, or safe RL; Demonstrate real deployments, high fidelity simulations, benchmarks, or open datasets that quantify persistence (e.g., time to depletion, mission availability, station utilization, energy per task). A key aim is to bridge theory and practice: unifying resource modeling, decision making, and runtime supervision so fleets remain safe and productive over long horizons. Submissions should position their novelty in operations and resource orchestration, rather than in sensing, perception, mapping, or generic swarm intelligence.We welcome Original Research, Methods, Brief Research Reports, Data/Benchmark Reports, Perspective, (Systematic/Mini) Reviews, and Technology & Code papers. Topics include (but are not limited to): energy aware task and motion planning; fleet scheduling and dispatch; recharge/charging station orchestration; mobile charging robots; battery health aware control; opportunity charging; energy harvesting; degradation robust routing; learning augmented or online optimization; MPC and mixed integer models at scale; grid aware operations and tariff sensitive planning; runtime monitoring and safety under resource constraints; evaluation protocols and simulators for persistent autonomy. Scope note: The primary contribution should be resource/energy modeling, optimization, or infrastructure aware operations. Works whose novelty is mainly in sensing/perception/exploration/mapping are out of scope unless tightly coupled to operational persistence. Authors are encouraged to report reproducible metrics (e.g., mission availability, energy per completed task, time between charges) and release code/data where possible. Topic editors: Ziyang Wang, Jie (Jay) Wang, Zhuangzhuang Dai, Pian Yu, Qingbiao LI Source: https://www.frontiersin.org/research-topics/74372/resource--and-energy-aware-multi-robot-operations-scheduling-recharging-and-long-horizon-persistence