Acoustic-driven millimetric helical robot: ultrasonic synergistic manipulation in confined fluidic environment

Acoustic microrobots have emerged as a promising frontier for targeted drug delivery and minimally invasive medicine due to their high-power density and biocompatibility. Despite wide-ranging designs, conventional acoustic microrobots mostly rely on air microbubbles trapped within confined microcavities within the robot body, which suffer from limited operational longevity due to rapid gas dissolution and resultant shifts in resonance frequency. In this paper, we propose a robust, membrane-based acoustic microrobot that overcomes these limitations by employing a thin flexible Polydimethylsiloxane (PDMS) membrane bonded over confined microcavities for microstreaming. The introduced design physically prevents gas diffusion, ensuring stable performance over extended periods at high actuation voltages. We systematically characterized the membrane-based acoustic actuator longevity, demonstrating consistent streaming and propulsion for over 24 hours of continuous operation. In addition, by embedding magnetic microparticles into the structural body, these actuators were successfully employed as microswimmers with directional control using low-intensity (2 mT) external magnetic fields. Finally, we demonstrate the scalability of the proposed design architecture down to ~100 um. This membrane-based approach establishes a reliable framework for the development of high-endurance acoustic microactuators and microrobots capable of performing long-term tasks.

Natural language interfaces can simplify interaction with multi-robot systems, especially when non-expert users need to issue high-level commands. Acoustic manipulation using ultrasonic phased arrays also enables contactless object handling for applications such as healthcare, laboratory automation, and precision transport. However, combining large language models (LLMs) with distributed acoustic mobile robots remains underexplored. This paper presents a decentralized framework for natural language-driven coordination of acoustic robots for contactless object manipulation. The system converts spoken instructions into executable multi-robot task plans using Whisper-based speech recognition, LLM-based semantic parsing, structured JSON task representation, and distributed scheduling. The JSON schema encodes robot assignments, temporal dependencies, spatial constraints, and synchronization requirements for sequential, parallel, and synchronized execution. The system is implemented on two TurtleBot3-based acoustic robots, each equipped with an ultrasonic phased array for contactless object transport. Experiments were conducted in three scenarios: sequential execution, parallel multi-robot transport, and synchronized cooperative manipulation. The system achieved task success rates of 96 percent for sequential tasks, 86 percent for parallel execution, and 70 percent for synchronized collaborative transport. These results show that natural language commands can be transformed into distributed robot actions for contactless manipulation, highlighting the potential of LLM-driven automation for human-robot interaction in distributed robotic systems.

Aquatic robots have expanded human access to underwater environments, yet many underwater spaces contain obstacles that can disrupt open-water locomotion. In "hydro-cluttered" environments, water is interspersed with rigid and flexible clutter, making body-obstacle contact unavoidable. Operating in these spaces requires robots that can regulate and exploit contact, but this regime remains difficult to model or simulate. Building on recent advances in mechanical intelligence in terradynamically capable limbless robotics, we develop principles for 3D aquatic locomotion using AquaMILR, an elongate limbless robot that combines bilateral cable-driven actuation, programmable body compliance, distributed depth regulation, corrosion-resistant enclosures, and onboard power and electronics for untethered field operation. Systematic robophysical experiments reveal that programmable body compliance regulates body deformation and converts body-environment interactions into fast, robust, forward progression across increasing hydro-clutter constraint strength. Depth regulation provides three-dimensional access, allowing the robot to bypass clutter, recover from obstruction, and continue through otherwise inaccessible routes. In potential jamming scenarios, emergent inertia-induced rolling acts as a spontaneous recovery mechanism, freeing the robot from clutter that would otherwise lead to failure and allowing locomotion to continue without additional control. Tests of the robot in an aquatic mangrove field demonstrate that these principles transfer to practical operation, enabling navigation and onboard visual inspection of inaccessible root zones. These results establish principles for hydro-cluttered locomotion and a design paradigm in which aquatic robots exploit environmental complexity as a locomotor resource.