In this paper, we introduce advances in the sensor suite of an autonomous flying insect robot (FIR) weighing less than a gram. FIRs, because of their small weight and size, offer unparalleled advantages in terms of material cost and scalability. However, their size introduces considerable control challenges, notably high-speed dynamics, restricted power, and limited payload capacity. While there have been advancements in developing lightweight sensors, often drawing inspiration from biological systems, no sub-gram aircraft has been able to attain sustained hover without relying on feedback from external sensing such as a motion capture system. The lightest vehicle capable of sustained hovering-the first level of “sensor autonomy”-is the much larger 28 g Crazyflie. Previous work reported a reduction in size of that vehicle's avionics suite to 187 mg and 21 mW. Here, we report a further reduction in mass and power to only 78.4 mg and 15 mW. We replaced the laser rangefinder with a lighter and more efficient pressure sensor, and built a smaller optic flow sensor around a global-shutter imaging chip. A Kalman Filter (KF) fuses these measurements to estimate the state variables that are needed to control hover: pitch angle, translational velocity, and altitude. Our system achieved performance comparable to that of the Crazyflie's estimator while in flight, with root mean squared errors of 1.573$\text{deg}, 0.186 \mathrm{m} / \mathrm{s}$, and 0.136 m, respectively, relative to motion capture.

Sub-gram flying robots have transformative potential in applications from search and rescue to precision agriculture to environmental monitoring. However, a key gap in achieving autonomous flight for these applications is the low lift to weight ratio of flapping wing and quadrotor designs around 1 g or less. To close this gap, we propose a helictoper-style design that minimizes size and weight by leveraging the high lift, reliability, and low-voltage of sub-gram motors. We take an important step to enable this goal by designing a light-weight, micfrofabricated flybar mechanism to passively stabilize such a robot. Our 48 mg flybar is folded from a flat carbon fiber laminate into a 3D mechanism that couples tilting of the flybar to a change in the angle of attack of the rotors. Our design uses flexure joints instead of ball-in-socket joints common in larger flybars. To expedite the design exploration and optimization of a microfabricated flat-folded flybar, we develop a novel user-in-the-loop bi-level optimization workflow that combines Bayesian optimization design tools and expert feedback. We develop four template designs and use this method to achieve a peak damping ratio of 0.528, an 18.9x improvement from our initial design. Compared to a flybar-less rotor with a near 0 damping ratio, our flybar-rotor mechanism maintains a stable roll and pitch with relative deviations < 1°. Our results show that, if combined with a counter-torque mechanism such as a tail rotor, our miniaturized flybar could mechanically provide attitude stability for a sub-gram helicopter.

Flying insect robots weighing less than a gram (FIRs) have advantages over their larger counterparts due to their low materials cost, small size, and low weight, allowing for deployment in large numbers. Control autonomy in such aircraft introduces challenges arising from their small size such as high-speed dynamics, limited power and payload capacity. Previous work has produced and characterized sensors with compatible mass and power specifications, many of which are biologically-inspired. And controlled flight has been demon-strated using feedback from external motion capture cameras. But to date, no avionics system has been reported that is light enough and capable of providing the feedback necessary to perform controlled hovering flight using only components carried on-board. Here we present such a system. It consists a sensor package consisting of an inertial measurement unit, a laser rangefinder and an optical flow sensor, and an associated estimator based on the nonlinear Extended Kalman Filter (EKF). The sensor suite weighs 187 mg and consumes 21 mW. We implemented a low-latency wireless link to transmit this data at 1 kHz without cumbersome wires. The EKF estimates attitude, altitude and lateral velocities. We estimate that computation power usage is <400 µW using floating-point operations on a standard microcontroller. Our system's RMSE attitude and position error are less than 4° and 1 cm relative to motion capture estimates.

Visual navigation for insect-scale robots is very challenging because in such a small scale, the size, weight, and power (SWaP) constraints do not appear to permit visual navigation techniques such as SLAM (Simultaneous Localization and Mapping) because they are likely to be too power-hungry. We propose to use a biology-inspired approach, which we term the bilinear optic flow approximation, that is more computationally efficient. We build on previous work that has shown that the bilinear approximation can be used for visual servoing. Here, we show that a bilinear approximator can be learned that is able to stabilize the heading of a robot while performing continuous forward motion in a corridor-shaped environment. This is a necessary capability for confined-space navigation that insect-sized robots are likely to perform. In this work, we describe the underlining methodology of the method and built a 2D visual simulation environment and omnidirectional camera model to validate our results.

We propose a novel compute-in-memory (CIM)-based ultralow-power framework for probabilistic localization of insect-scale drones. Localization is a critical subroutine for path planning and rotor control in drones, where a drone is required to continuously estimate its pose (position and orientation) in flying space. The conventional probabilistic localization approaches rely on the 3-D Gaussian mixture model (GMM)-based representation of a 3-D map. A GMM model with hundreds of mixture functions is typically needed to adequately learn and represent the intricacies of the map. Meanwhile, localization using complex GMM map models is computationally intensive. Since insect-scale drones operate under extremely limited area/power budget, continuous localization using GMM models entails much higher operating energy, thereby limiting flying duration and/or size of the drone due to a larger battery. Addressing the computational challenges of localization in an insect-scale drone using a CIM approach, we propose a novel framework of 3-D map representation using a harmonic mean of the “Gaussian-like” mixture (HMGM) model. We show thatshort-circuit currentof a multiinput floating-gate CMOS-based inverter follows the harmonic mean of a Gaussian-like function. Therefore, the likelihood function useful for drone localization can be efficiently implemented by connecting many multiinput inverters in parallel, each programmed with the parameters of the 3-D map model represented as HMGM. When the depth measurements are projected to the input of the implementation, the summed current of the inverters emulates the likelihood of the measurement. We have characterized our approach on an RGB-D scenes dataset. The proposed localization framework is$\sim 25\times $energy-efficient than the traditional, 8-bit digital GMM-based processor paving the way for tiny autonomous drones.

Flapping-wing insect-scale robots (ppwithpp. The thrust modulation reported here is necessary to realize controlled flight using on-board power systems, instead of externally provided signals via a wire tether.

Constrained battery life on current Unmanned Aerial Vehicles (drones) limits the time they can operate and distance they can travel. We address this challenge by harvesting solar power to enable duty-cycled operation on a palm-sized drone. We present a scaling analysis that suggests that more solar power can be collected per unit mass of the drone as scale reduces, favoring small drones. By charging from the sun, the drone can operate for more than a single charging cycle, enabling extended mission time, and long-distance travel. To realize this, we design a high efficiency charging circuit and introduce two innovations. The first is a photovoltaic array that passively folds down while in flight to reduce air drag and automatically opens during landing due to the ground effect. The second is a sensor system and controller that autonomously finds suitable charging sites that are flat and well-lit. The drone can be fully charged in 3 hrs using the solar array and charging circuit with an average efficiency of 90.84%. Each charge enables a 4.7 min flight, allowing the drone to travel up to 1.2 km in a day. We also discuss how this platform could be used to take periodic measurements for smart agriculture or wildlife tracking, rapidly deploy wireless networks, or deploy microrobots in the future.

Insect-sized ($\sim$100 mg) aerial robots have advantages over larger robots because of their small size, low weight, and low materials cost. Previous iterations have demonstrated controlled flight but were difficult to fabricate because they consisted of many separate parts assembled together and were also unable to perform locomotion modes besides flight. This article presents a new design of a 74-mg flapping-wing robot that dramatically reduces the number of parts and simplifies fabrication. The robot also has a lower center of mass, which allows the robot to additionally land without the need for long legs, even in case of unstable flight. We also show that the new design allows for wing-driven ground and air–water interfacial locomotion, improving the versatility of the robot. During surface ambulation, forward thrust is generated by increasing the speed of the upstroke relative to the downstroke of the flapping wings. Adjusting relative wing stroke amplitudes also allows for steering. The ability to land and subsequently move along the ground first presented here allows the robot to negotiate extremely confined spaces and underneath obstacles. We present results demonstrating these capabilities, as well as hovering flight and controlled landing.

We present the design, fabrication, and feedforward control of a insect-sized (142 mg) aerial robot that is equipped with a bio-inspired inertial tail. A tail allows the robot to perform rapid inertial reorientation as well as to shift weight to modulate aerodynamic torques on its body. Here we present the first analysis of inertial reorientation using a piezo actuator, departing from previous work to date that has focused exclusively on actuation by DC electric motor. The primary difference is that unlike a geared motor system, the piezo-tail system operates as a resonant system, exhibiting slowly-decaying oscillations. We present a dynamic model of piezo-driven inertial reorientation, along with an open-loop feedforward controller that reduces excitation of the resonant mode. We validate our approach on a tethered testbed as well as a flight-capable prototype. Our results indicate that incorporating a tail can allow for more rapid dynamic maneuvers and could stabilize the robot during flight.

Robotic airborne chemical source localization has critical applications ranging from search and rescue to hazard detection to pollution assessment. Previous demonstrations on flying robots have required search times in excess of ten minutes, or required computation-intensive signal processing, largely because of the slow response of semiconductor gas sensors. To mitigate these limitations, we developed a hybrid biological/synthetic chemical sensing platform consisting of a moth antenna on an aerial robot. We demonstrate that our robot, a 9 centimeter nano drone, can repeatedly detect and reach the source of a volatile organic chemical plume in less than a minute. We also introduce wind vanes to passively aim the robot upwind, greatly simplifying control. To our knowledge this is the first odor-finding robot to use this approach, and it allows for localization using feedback only from sensors carried on-board rather than GPS, allowing indoor operation. The chemical sensor consists of a hybrid biological/synthetic integrated chemical sensor (electroantennogram) using an excised antenna of the hawkmoth Manduca sexta and associated miniaturized electrophysiology conditioning circuitry. Our robot performs an insect-inspired cast-and-surge search algorithm inspired by the odor-tracking behavior observed in Manduca sexta. These results represent a significant step toward robots that have the speed and sensitivity of biological systems.

Sensor networks with devices capable of moving could enable applications ranging from precision irrigation to environmental sensing. Using mechanical drones to move sensors, however, severely limits operation time since flight time is limited by the energy density of current battery technology. We explore an alternative, biology-based solution: integrate sensing, computing and communication functionalities onto live flying insects to create a mobile IoT platform. Such an approach takes advantage of these tiny, highly efficient biological insects which are ubiquitous in many outdoor ecosystems, to essentially provide mobility for free. Doing so however requires addressing key technical challenges of power, size, weight and self-localization in order for the insects to perform location-dependent sensing operations as they carry our IoT payload through the environment. We develop and deploy our platform on bumblebees which includes backscatter communication, low-power self-localization hardware, sensors, and a power source. We show that our platform is capable of sensing, backscattering data at 1 kbps when the insects are back at the hive, and localizing itself up to distances of 80 m from the access points, all within a total weight budget of 102 mg.

To date, insect scale aerial robots have required wire tethers for providing power due to the challenges of integrating the required high-voltage power electronics within their severely constrained weight budgets. In this paper we present a significant milestone in the achievement of flight autonomy: the first wireless liftoff of a 190 mg aerial vehicle. Our robot is remotely powered using a 976 nm laser and integrates a complete power electronics package weighing a total of 104 mg, using commercially available components and fabricated using a fast-turnaround laser based circuit fabrication technique. The onboard electronics include a lightweight boost converter capable of producing high voltage bias and drive signals of over 200 V at up to 170 Hz and regulated by a microcontroller performing feedback control. We present our system design and analysis, detailed description of our fabrication method, and results from flight experiments.

Efforts to engineer insect-sized (~100 mg) robots are motivated by their potential advantages relative to larger robots, such as greater deployment numbers at the same cost. Previous iterations have demonstrated controlled flight, but were limited in terms of locomotion capabilities outside of flight. They also consisted of many parts, making them difficult to fabricate. Here we present a re-design that lowers the center of mass, allowing the robot to additionally land without the need for long legs. Furthermore, we show that the new design allows for wing-driven ground locomotion. This is achieved by varying the speed of downstroke relative to the upstroke of the flapping wings, which also allows for steering. By landing and subsequently moving along the ground, the robot can negotiate extremely confined spaces and underneath obstacles, as well as navigate to precise locations for sensing operations. The new design also drastically reduces the number of parts, simplifying fabrication. We describe the new design in detail and present results demonstrating these capabilities, as well as feedback-stabilized flights.

The RoboBee is a novel insect-scale flapping wing Micro Aerial Vehicle that is envisioned to enable exciting applications. While recent results have demonstrated full control as well as biomimetic behaviors such as perching, more complex challenges such as perception and navigation still exist. Typically, challenges in perception-based control can only be solved by experimentation. However, such fly-size MAVs are not widely available to researchers at large due to its intricate manufacturing process and limited mechanical lifetime. To facilitate the development of perception and control algorithms of insect-scale MAVs, we explore an approach of simulating flapping wing aerial vehicle dynamics on a quad-rotor. This work performs detailed analysis of the transformation of control inputs, and demonstrates feasibility by numerically simulating basic flight patterns of models of a RoboBee as well as that of a scaled quad-rotor.

The Harvard Robobee is a fly-sized aerial vehicle that can perform controlled flight maneuvers. But this robot is unable to control its yaw or heading angle to a desired value. Motivated by this deficiency, we propose a new method to produce yaw-axis rotations. Termed wriggle-steering, it consists of driving body oscillations around its two other rotational axes. Because no torque is applied directly around the controlled axis, it therefore constitutes an alternative control method for under-actuated designs. Oscillations are driven around pitch and roll axes at the same frequency but 90 degrees out of phase, resulting in a small change in yaw angle after each cycle because of nonlinearity in attitude dynamics. We propose two wing kinematics perturbations that produce the necessary actuation. The predictions are validated with a quasi-steady aerodynamics model, free-body simulations, and flight tests on a fly-sized hovering aerial robot. The results suggest that wriggle-steering can save mass and reduce complexity by eliminating the need for additional actuators in flapping-wing robots or other aircraft.

The Harvard RoboBee was the first fly-sized vehicle to lift its own weight. This vehicle has previously demonstrated controlled flight maneuvers, but this required an array of external cameras to precisely track its trajectory. Developing flight-worthy sensors to eliminate the need for external motion capture is an area of active study. In this paper, we consider an onboard analog magnetometer. We show that the sensor meets the size, weight, and power requirements for the RoboBee and can provide feedback on angular position for pitch and yaw angle control. We show that this sensor can provide an accurate angle reading despite proximity to the piezoelectric actuators of this vehicle. This is likely because the actuators are driven by electrostatic forces rather than the electromagnetic forces that drive the electric motors of larger aircraft. This sensor provided sufficient bandwidth to enable rapid pitch angle maneuvers within 200ms on a RoboBee constrained to rotate only about its pitch axis. We also show it operating in a feedback loop to control heading angle, the first demonstration of controlling yaw orientation at this scale.

This paper discusses recent developments in sensors for the Harvard RoboBee. The RoboBee is a sub-100 mg flapping-wing micro-aerial vehicle that is able to lift its own weight under external power, but, like flying insects, is unstable in flight without active feedback. We discuss design and characterization of two low-latency insect-inspired sensors for flight control: an antenna to sense airspeed and light-sensing ocelli to estimate attitude angle relative to a luminous sky. We demonstrate accurate wind velocity estimation in a wind tunnel despite the effect of nearby flapping wings.We also demonstrate pitch angle control using the ocelli on a wire-mounted RoboBee that is free to rotate about its pitch axis. These flight-weight sensors are essential first steps toward autonomous upright stability and controlled forward motions.

The Harvard RoboBee is the first insect-scale cflapping-wing robot weighing less than 100 mg that is able to lift its own weight. However, when flown without guide wires, this vehicle quickly tumbles after takeoff because of instability in its dynamics. Here, we show that by adding aerodynamic dampers, we can can alter the vehicle's dynamics to stabilize its upright orientation. We provide an analysis using wind tunnel experiments and a dynamic model. We demonstrate stable vertical takeoff, and using a marker-based external camera tracking system, hovering altitude control in an active feedback loop. These results provide a stable platform for both system dynamics characterization and unconstrained active maneuvers of the vehicle and represent the first known hovering demonstration of an insect-scale flapping-wing robot.