Sawyer B. Fuller

Aaron Weber, Daksh Dhingra, Sawyer B. Fuller

Sub-gram flapping-wing flying insect robots (FIRs) are challenging to model because of mechanical complexity in their wings, unsteady aerodynamic flow, and the difficulty of making precise measurements at a small scale. Coupling effects between roll and pitch torque actuation have not previously been measured because a two-axis sensor that is sensitive enough has not been realized. To address this shortcoming, we introduce a microfabricated gimbal design capable of precisely and simultaneously measuring roll and pitch torques as well as thrust. We then used it to measure the extent to which a pitch torque command affects roll torque and vice versa on a 180 mg piezo-actuated flapping-wing flying platform. Our results show a high coefficient of determination in the linear regression for both pitch (0.95) and roll (0.98) and low cross-correlation coefficients (-0.001 and -0.085, respectively) across the full range of simultaneous torque commands, indicating negligible cross-axis coupling. Similarly, thrust force deviates by a maximum of only 5.8% from the mean thrust value. These results validate the assumption that pitch and toll can be considered independently in control and will inform future models of how inputs affect the aerodynamics of resonant flapping-wing systems.

Priyesh Shukla, Ankith Muralidhar, Nick Iliev et al.

We propose a novel compute-in-memory (CIM)-based ultra-low-power framework for probabilistic localization of insect-scale drones. The conventional probabilistic localization approaches rely on the three-dimensional (3D) Gaussian Mixture Model (GMM)-based representation of a 3D 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 3D map representation using a harmonic mean of "Gaussian-like" mixture (HMGM) model. The likelihood function useful for drone localization can be efficiently implemented by connecting many multi-input inverters in parallel, each programmed with the parameters of the 3D 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 indoor localization dataset. The average localization error in our approach is $\sim$0.1125 m which is only slightly degraded than software-based evaluation ($\sim$0.08 m). Meanwhile, our localization framework is ultra-low-power, consuming as little as $\sim$17 $μ$W power while processing a depth frame in 1.33 ms over hundred pose hypotheses in the particle-filtering (PF) algorithm used to localize the drone.