Soft robotics with new designs, fabrication technologies and control strategies inspired by nature have been totally changing our view on robotics. To fully exploit their potential in practical applications, untethered designs are preferred in implementation. However, hindered by the limited thermal/mechanical performance of soft materials, it has been always challenging for researchers to implement untethered solutions, which generally involve rigid forms of high energy-density power sources or high energy-density processes. A number of insects in nature, such as rove beetles, can gain a burst of kinetic energy from the induced surface-energy gradient on water to return to their familiar habitats, which is generally known as Marangoni propulsion. Inspired by such a behavior, we report the agile untethered mobility of a fully soft robot in liquid based on induced energy gradients and also develop corresponding fabrication and maneuvering strategies. The robot can reach a speed of 5.5 body lengths per second, which is 7-fold more than the best reported, 0.69 (body length per second), in the previous work on untethered soft robots in liquid by far. Further controlling the robots, we demonstrate a soft-robot swarm that can approach a target simultaneously to assure a hit with high accuracy. Without employing any high energy-density power sources or processes, our robot exhibits many attractive merits, such as quietness, no mechanical wear, no thermal fatigue, invisibility and ease of robot fabrication, which may potentially impact many fields in the future.

中文翻译：

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由感应能量梯度驱动的液体中仿生无束缚全软机器人


受大自然启发的软机器人具有新的设计、制造技术和控制策略，彻底改变了我们对机器人的看法。为了充分发挥其在实际应用中的潜力，在实施中优选采用无束缚设计。然而，由于软材料有限的热/机械性能的阻碍，研究人员实施不受限制的解决方案一直是一个挑战，这些解决方案通常涉及刚性形式的高能量密度电源或高能量密度工艺。自然界中的许多昆虫，例如罗夫甲虫，可以从水面诱导的表面能梯度中获得一阵动能，以返回它们熟悉的栖息地，这通常被称为马兰戈尼推进力。受这种行为的启发，我们报告了基于诱导能量梯度的全软机器人在液体中的敏捷无束缚移动性，并开发了相应的制造和操纵策略。该机器人的速度可以达到每秒 5.5 个身体长度，这比迄今为止在液体中不受束缚的软体机器人的最佳报告 0.69（身体长度每秒）快了 7 倍。进一步控制机器人，我们演示了一个软机器人群，它可以同时接近目标，以确保高精度命中。在不使用任何高能量密度电源或工艺的情况下，我们的机器人表现出许多吸引人的优点，例如安静、无机械磨损、无热疲劳、隐形和易于机器人制造，这可能会影响未来的许多领域。