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Electroaerodynamic propulsion, where force is produced via the momentum-transferring collisions between accelerated ions and neutral air molecules, is a promising alternative mechanism for flight at the micro air vehicle scale due to its silent and solid-state nature. Its relatively low efficiency, however, has thus far precluded its use in a power-autonomous vehicle; leveraging the efficiency benefits of operation close to a fixed surface is a potential solution. While proximity effects like the ground and ceiling effects have been well-investigated for rotorcraft and flapping wing micro air vehicles, they have not been for electroaerodynamically-propelled fliers. In this work, we investigate the change in performance when centimeter-scale thrusters are operated close to a "ceiling" plane about the inlet. We show a surprising and, until now, unreported effect; a major electrostatic attractive component, analogous to electroadhesive pressure but instead mediated by a stable atmospheric plasma. The isolated electrostatic and fluid dynamic components of the ceiling effect are shown for different distances from the plane and for different materials. We further show that a flange attached to the inlet can vastly increase both components of force. A peak efficiency improvement of 600% is shown close to the ceiling. This work points the way towards effective use of the ceiling effect for power autonomous vehicles, extending flight duration, or as a perching mechanism.

Electroaerodynamic (EAD) propulsion, where thrust is produced by collisions between electrostatically-accelerated ions and neutral air, is a potentially transformative method for indoor flight owing to its silent and solid-state nature. Like rotors, EAD thrusters exhibit changes in performance based on proximity to surfaces. Unlike rotors, they have no fragile and quickly spinning parts that have to avoid those surfaces; taking advantage of the efficiency benefits from proximity effects may be a route towards longer-duration indoor operation of ion-propelled fliers. This work presents the first empirical study of ground proximity effects for EAD propulsors, both individually and as quad-thruster arrays. It focuses on multi-stage ducted centimeter-scale actuators suitable for use on small robots envisioned for deployment in human-proximal and indoor environments. Three specific effects (ground, suckdown, and fountain lift), each occurring with a different magnitude at a different spacing from the ground plane, are investigated and shown to have strong dependencies on geometric parameters including thruster-to-thruster spacing, thruster protrusion from the fuselage, and inclusion of flanges or strakes. Peak thrust enhancement ranging from 300 to 600% is found for certain configurations operated in close proximity (0.2 mm) to the ground plane and as much as a 20% increase is measured even when operated centimeters away.

Electroaerodynamic propulsion, where force is produced through collisions between electrostatically accelerated ions and neutral air molecules, is an attractive alternative to propeller- and flapping wing-based methods for micro air vehicle (MAV) flight due to its silent and solid-state nature. One major barrier to adoption is its limited thrust efficiency at useful disk loading levels. Ducted actuators comprising multiple serially-integrated acceleration stages are a potential solution, allowing individual stages to operate at higher efficiency while maintaining a useful total thrust, and potentially improving efficiency through various aerodynamic and fluid dynamic mechanisms. In this work, we investigate the effects of duct and emitter electrode geometries on actuator performance, then show how a combination of increasing cross-sectional aspect ratio and serial integration of multiple stages can be used to produce overall thrust densities comparable to commercial propulsors. An optimized five-stage device attains a thrust density of about 18 N/m$^2$ at a thrust efficiency of about 2 mN/W, among the highest values ever measured at this scale. We further show how this type of thruster can be integrated under the wings of a MAV-scale fixed wing platform, pointing towards future use as a distributed propulsion system.

Microsurgery is a particularly impactful yet challenging form of surgery. Robot assisted microsurgery has the potential to improve surgical dexterity and enable precise operation on such small scales in ways not previously possible. Intraocular microsurgery is a particularly challenging domain in part due to the lack of dexterity that is achievable with rigid instruments inserted through the eye. In this work, we present a new design for a millimeter-scale, dexterous wrist intended for microsurgery applications. The wrist is created via a state-of-the-art two-photon-polymerization (2PP) microfabrication technique, enabling the wrist to be constructed of flexible material with complex internal geometries and critical features at the micron-scale. The wrist features a square cross section with side length of 1.25 mm and total length of 3.75 mm. The wrist has three tendons routed down its length which, when actuated by small-scale linear actuators, enable bending in any plane. We present an integrated gripper actuated by a fourth tendon routed down the center of the robot. We evaluate the wrist and gripper by characterizing its bend-angle. We achieve more than 90 degrees bending in both axes. We demonstrate out of plane bending as well as the robot's ability to grip while actuated. Our integrated gripper/tendon-driven continuum robot design and meso-scale assembly techniques have the potential to enable small-scale wrists with more dexterity than has been previously demonstrated. Such a wrist could improve surgeon capabilities during teleoperation with the potential to improve patient outcomes in a variety of surgical applications, including intraocular surgery., Comment: 2 pages, 3 figures, published in the proceedings of the 2023 Hamlyn Symposium on Medical Robotics (HSMR) conference

Decentralized control in multi-robot systems is dependent on accurate and reliable communication between agents. Important communication factors, such as latency and packet delivery ratio, are strong functions of the number of agents in the network. Findings from studies of mobile and high node-count radio-frequency (RF) mesh networks have only been transferred to the domain of multi-robot systems to a limited extent, and typical multi-agent robotic simulators often depend on simple propagation models that do not reflect the behavior of realistic RF networks. In this paper, we present a new open source swarm robotics simulator, BotNet, with an embedded standards-compliant time-synchronized channel hopping (6TiSCH) RF mesh network simulator. Using this simulator we show how more accurate communications models can limit even simple multi-robot control tasks such as flocking and formation control, with agent counts ranging from 10 up to 2500 agents. The experimental results are used to motivate changes to the inter-robot communication propagation models and other networking components currently used in practice in order to bridge the sim-to-real gap., Comment: 9 pages, 8 figures

The electrohydrodynamic (EHD) force produced by ions ejected from a corona plasma is a solid state, silent mechanism for accelerating air, useful for applications ranging from electronics cooling to flying microrobots. This paper presents the theoretical motivation and the first implementation of a multi-stage, highly miniaturized EHD device, which can provide both improved absolute power output and power density as compared to single-stage devices. A laser microfabricated, folded electrode design reduces component count and assembly time. Data from one, two, and three-stage devices demonstrates a near linear scaling of output force with stage count, indicating inter-stage ducting successfully reduces losses. Device lifetime is assessed to validate the use of stainless-steel emission electrodes. Areal thrust, force density, and volumetric power density for the three-stage device are among the highest ever measured from an EHD actuator., Comment: Accepted and presented at Transducers 2021

Nonholonomic control is a candidate to control nonlinear systems with path-dependant states. We investigate an underactuated flying micro-aerial-vehicle, the ionocraft, that requires nonholonomic control in the yaw-direction for complete attitude control. Deploying an analytical control law involves substantial engineering design and is sensitive to inaccuracy in the system model. With specific assumptions on assembly and system dynamics, we derive a Lie bracket for yaw control of the ionocraft. As a comparison to the significant engineering effort required for an analytic control law, we implement a data-driven model-based reinforcement learning yaw controller in a simulated flight task. We demonstrate that a simple model-based reinforcement learning framework can match the derived Lie bracket control (in yaw rate and chosen actions) in a few minutes of flight data, without a pre-defined dynamics function. This paper shows that learning-based approaches are useful as a tool for synthesis of nonlinear control laws previously only addressable through expert-based design., Comment: 7 pages, 1 page appendix

Designing effective low-level robot controllers often entail platform-specific implementations that require manual heuristic parameter tuning, significant system knowledge, or long design times. With the rising number of robotic and mechatronic systems deployed across areas ranging from industrial automation to intelligent toys, the need for a general approach to generating low-level controllers is increasing. To address the challenge of rapidly generating low-level controllers, we argue for using model-based reinforcement learning (MBRL) trained on relatively small amounts of automatically generated (i.e., without system simulation) data. In this paper, we explore the capabilities of MBRL on a Crazyflie centimeter-scale quadrotor with rapid dynamics to predict and control at <50Hz. To our knowledge, this is the first use of MBRL for controlled hover of a quadrotor using only on-board sensors, direct motor input signals, and no initial dynamics knowledge. Our controller leverages rapid simulation of a neural network forward dynamics model on a GPU-enabled base station, which then transmits the best current action to the quadrotor firmware via radio. In our experiments, the quadrotor achieved hovering capability of up to 6 seconds with 3 minutes of experimental training data., Comment: Accepted to IROS and RA-L, 2019. For more information, see the website: https://sites.google.com/berkeley.edu/mbrl-quadrotor/. 9 pages, 12 figures

We present a regime-switching vector-autoregressive method for very-short-term wind speed forecasting at multiple locations with regimes based on large-scale meteorological phenomena. Statistical methods short-term wind forecasting out-perform numerical weather prediction for forecast horizons up to a few hours, and the spatio-temporal interdependency between geographically dispersed locations may be exploited to improve forecast skill. Here we show that conditioning spatio-temporal interdependency on `atmospheric modes' can further improve forecast performance. The modes are defined from the atmospheric classification of wind and pressure fields at the surface level, and the geopotential height field at the 500hPa level. The data fields are extracted from the MERRA-2 reanalysis dataset with an hourly temporal resolution over the UK, atmospheric patterns are classified using self-organising maps and then clustered to optimise forecast performance. In a case study based on 6 years of measurements from 23 weather stations in the UK, a set of three atmospheric modes are found to be optimal for forecast performance. The skill in the one- to six-hour-ahead forecasts is improved at all sites compared to persistence and competitive benchmarks. Across the 23 test sites, one-hour-ahead root mean squared error is reduced by between 0.3% and 4.1% compared to the best performing benchmark, and by and average of 1.6% over all sites; the six-hour-ahead accuracy is improved by an average of 3.1%.