Herein, a room‐temperature liquid metal battery (LMB) with a solid lithium anode electrode and gallium–tin (Ga–Sn) alloy cathode electrode is reported. With the improved wettability of the electric collector and grain‐refined liquid metal droplets cathode, the aforementioned LMB exhibits good cyclic reversibility and negligible self‐discharge. The result shows that the assembled Li||Ga–Sn battery has a satisfactory specific capacity (409 mAh g−1) and high energy efficiency (up to 92%). The low melting point Ga–Sn alloy can construct fast kinetics for the redox reaction and improve the electrode reaction kinetics, imparting the Li||Ga–Sn system with high discharge voltage (0.77 V at 1C) and smaller polarization voltage at different current densities, which ensure the high Coulomb efficiency and good rate performance of the battery. Moreover, the battery stably cycles at 35 °C for 60 cycles at the 1C rate, with no significant capacity degradation. With good electrochemical performance, simple structure, easy maintenance, and high safety, this room‐temperature Li||Ga–Sn battery may be a promising choice for power grid energy storage applications.

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For massive large-scale tasks, a multi-robot system (MRS) can effectively improve efficiency by utilizing each robot's different capabilities, mobility, and functionality. In this paper, we focus on the multi-robot coverage path planning (mCPP) problem in large-scale planar areas with random dynamic interferers in the environment, where the robots have limited resources. We introduce a worker-station MRS consisting of multiple workers with limited resources for actual work, and one station with enough resources for resource replenishment. We aim to solve the mCPP problem for the worker-station MRS by formulating it as a fully cooperative multi-agent reinforcement learning problem. Then we propose an end-to-end decentralized online planning method, which simultaneously solves coverage planning for workers and rendezvous planning for station. Our method manages to reduce the influence of random dynamic interferers on planning, while the robots can avoid collisions with them. We conduct simulation and real robot experiments, and the comparison results show that our method has competitive performance in solving the mCPP problem for worker-station MRS in metric of task finish time.

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