New Wing Stroke and Wing Pitch Approaches for Milligram-scale Aerial Devices

Here we report the construction of the simplest transmission mechanism ever designed capable of converting linear motions of any actuator to $\pm$60$^\circ$ rotary wing stroke motion. It is planar, compliant, can be fabricated in a single step and requires no assembly. Further, its design is universal in nature, that is, it can be used with any linear actuator capable of delivering sufficient power, irrespective of the magnitude of actuator displacements. We also report a novel passive wing pitch mechanism whose motion has little dependence on the aerodynamic loading on the wing. This exponentially simplifies the job of the designer by decoupling the as of yet highly coupled wing morphology, wing kinematics and flexure stiffness parameters. Like the contemporary flexure-based methods it is an add-on to a given wing stroke mechanism. Moreover, the intended wing pitch amplitude could easily be changed post-fabrication by tuning the resonance mass in the mechanism.

Design of the first sub-milligram flapping wing aerial vehicle

Here we report the first sub-milligram flapping wing vehicle which is able to mimic insect wing kinematics. Wing stroke amplitude of 90$^\circ$ and wing pitch amplitude of 80$^\circ$ is demonstrated. This is also the smallest wing-span (single wing length of 3.5mm) device reported yet and is at the same mass-scale as a fruit fly. Assembly has been made simple and requires gluing together 5 components in contrast to higher part count and intensive assembly of other milligram-scale microrobots. This increases the fabrication speed and success-rate of the fully fabricated device. Low operational voltages (70mV) makes testing further easy and will enable eventual deployment of autonomous sub-milligram aerial vehicles.

An Insect-scale Self-sufficient Rolling Microrobot

We design an insect-sized rolling microrobot driven by continuously rotating wheels. It measures 18mm$\times$8mm$\times$8mm. There are 2 versions of the robot - a 96mg laser-powered one and a 130mg supercapacitor powered one. The robot can move at 27mm/s (1.5 body lengths per second) with wheels rotating at 300$^\circ$/s, while consuming an average power of 2.5mW. Neither version has any electrical wires coming out of it, with the supercapacitor powered robot also being self-sufficient and is able to roll freely for 8 seconds after a single charge. Low-voltage electromagnetic actuators (1V-3V) along with a novel double-ratcheting mechanism enable the operation of this device. It is, to the best of our knowledge, the lightest and fastest self-sufficient rolling microrobot reported yet.

An Insect-scale Untethered Laser-powered Jumping Microrobot

We present the design of an insect-sized jumping microrobot measuring 17mm$\times$6mm$\times$14mm and weighing 75 milligrams. The microrobot consumes 6.4mW of power to jump up by 8mm in height. The tethered version of the robot can jump 6 times per minute each time landing perfectly on its feet. The untethered version of the robot is powered using onboard photovoltaic cells illuminated by an external infrared laser source. It is, to the best of our knowledge, the lightest untethered jumping microrobot with onboard power source that has been reported yet.

Fast Neural Network Verification via Shadow Prices

To use neural networks in safety-critical settings it is paramount to provide assurances on their runtime operation. Recent work on ReLU networks has sought to verify whether inputs belonging to a bounded box can ever yield some undesirable output. Input-splitting procedures, a particular type of verification mechanism, do so by recursively partitioning the input set into smaller sets. The efficiency of these methods is largely determined by the number of splits the box must undergo before the property can be verified. In this work, we propose a new technique based on shadow prices that fully exploits the information of the problem yielding a more efficient generation of splits than the state-of-the-art. Results on the Airborne Collision Avoidance System (ACAS) benchmark verification tasks show a considerable reduction in the partitions generated which substantially reduces computation times. These results open the door to improved verification methods for a wide variety of machine learning applications including vision and control.

Combining Optimal Control and Learning for Visual Navigation in Novel Environments

Model-based control is a popular paradigm for robot navigation because it can leverage a known dynamics model to efficiently plan robust robot trajectories. However, it is challenging to use model-based methods in settings where the environment is a priori unknown and can only be observed partially through on-board sensors on the robot. In this work, we address this short-coming by coupling model-based control with learning-based perception. The learning-based perception module produces a series of waypoints that guide the robot to the goal via a collision-free path. These waypoints are used by a model-based planner to generate a smooth and dynamically feasible trajectory that is executed on the physical system using feedback control. Our experiments in simulated real-world cluttered environments and on an actual ground vehicle demonstrate that the proposed approach can reach goal locations more reliably and efficiently in novel, previously-unknown environments as compared to a purely end-to-end learning-based alternative. Our approach is successfully able to exhibit goal-driven behavior without relying on detailed explicit 3D maps of the environment, works well with low frame rates, and generalizes well from simulation to the real world. Videos describing our approach and experiments are available on the project website.

Regression-based Inverter Control for Decentralized Optimal Power Flow and Voltage Regulation

Electronic power inverters are capable of quickly delivering reactive power to maintain customer voltages within operating tolerances and to reduce system losses in distribution grids. This paper proposes a systematic and data-driven approach to determine reactive power inverter output as a function of local measurements in a manner that obtains near optimal results. First, we use a network model and historic load and generation data and do optimal power flow to compute globally optimal reactive power injections for all controllable inverters in the network. Subsequently, we use regression to find a function for each inverter that maps its local historical data to an approximation of its optimal reactive power injection. The resulting functions then serve as decentralized controllers in the participating inverters to predict the optimal injection based on a new local measurements. The method achieves near-optimal results when performing voltage- and capacity-constrained loss minimization and voltage flattening, and allows for an efficient volt-VAR optimization (VVO) scheme in which legacy control equipment collaborates with existing inverters to facilitate safe operation of distribution networks with higher levels of distributed generation.

A Successive-Elimination Approach to Adaptive Robotic Sensing

We study the adaptive sensing problem for the multiple source seeking problem, where a mobile robot must identify the strongest emitters in an environment with background emissions. Background signals may be highly heterogeneous, and can mislead algorithms which are based on receding horizon control, greedy heuristics, or smooth background priors. We propose AdaSearch, a general algorithm for adaptive sensing. AdaSearch combines global trajectory planning with principled confidence intervals in order to concentrate measurements in promising regions while still guaranteeing sufficient coverage of the entire area. Theoretical analysis shows that AdaSearch significantly outperforms a uniform sampling strategy when the distribution of background signals is highly variable. Simulation studies demonstrate that when applied to the problem of radioactive source-seeking, AdaSearch outperforms both uniform sampling and a receding time horizon information-maximization approach based on the current literature. We corroborate these findings with a hardware demonstration, using a small quadrotor helicopter in a motion-capture arena.

The Parallelization of Riccati Recursion

A method is presented for parallelizing the computation of solutions to discrete-time, linear-quadratic, finite-horizon optimal control problems, which we will refer to as LQR problems. This class of problem arises frequently in robotic trajectory optimization. For very complicated robots, the size of these resulting problems can be large enough that computing the solution is prohibitively slow when using a single processor. Fortunately, approaches to solving these type of problems based on numerical solutions to the KKT conditions of optimality offer a parallel solution method and can leverage multiple processors to compute solutions faster. However, these methods do not produce the useful feedback control policies that are generated as a by-product of the dynamic-programming solution method known as Riccati recursion. In this paper we derive a method which is able to parallelize the computation of Riccati recursion, allowing for super-fast solutions to the LQR problem while still generating feedback control policies. We demonstrate empirically that our method is faster than existing parallel methods.