Distributed IMU Sensors for In-Field Dynamic Measurements on an Alpine Ski.

Modern ski design is an inherently time-consuming process that involves an iterative feedback loop comprised of design, manufacturing and in-field qualitative evaluations. Additionally consumers can only rely on qualitative evaluation for selecting the ideal ski, and due to the variation in skier styles and ability levels, consumers can find it to be an inconsistent and expensive experience. We propose supplementing the design and evaluation process with data from in-field prototype testing, using a modular sensor array that can be ported to nearly any ski. This paper discusses a new distributed Inertial Measurement Unit (IMU) suite, including details regarding the design and operation, sensor validation experiments, and outdoor in-field testing results. Data are collected from a set of spatially distributed IMUs located on the upper surface of the ski. We demonstrate that this system and associated post-processing algorithms provide accurate data at a high rate (>700 Hz), enabling the measurement of both structural and rigid ski characteristics, and are robust to repetitive testing in outdoor winter conditions.

Lidar-Based Navigation of Subterranean Environments Using Bio-Inspired Wide-Field Integration of Nearness.

Navigating unknown environments is an ongoing challenge in robotics. Processing large amounts of sensor data to maintain localization, maps of the environment, and sensible paths can result in high compute loads and lower maximum vehicle speeds. This paper presents a bio-inspired algorithm for efficiently processing depth measurements to achieve fast navigation of unknown subterranean environments. Animals developed efficient sensorimotor convergence approaches, allowing for rapid processing of large numbers of spatially distributed measurements into signals relevant for different behavioral responses necessary to their survival. Using a spatial inner-product to model this sensorimotor convergence principle, environmentally relative states critical to navigation are extracted from spatially distributed depth measurements using derived weighting functions. These states are then applied as feedback to control a simulated quadrotor platform, enabling autonomous navigation in subterranean environments. The resulting outer-loop velocity controller is demonstrated in both a generalized subterranean environment, represented by an infinite cylinder, and nongeneralized environments like tunnels and caves.

Identification of optimal feedback control rules from micro-quadrotor and insect flight trajectories.

This paper presents "optimal identification," a framework for using experimental data to identify the optimality conditions associated with the feedback control law implemented in the measurements. The technique compares closed loop trajectory measurements against a reduced order model of the open loop dynamics, and uses linear matrix inequalities to solve an inverse optimal control problem as a convex optimization that estimates the controller optimality conditions. In this study, the optimal identification technique is applied to two examples, that of a millimeter-scale micro-quadrotor with an engineered controller on board, and the example of a population of freely flying Drosophila hydei maneuvering about forward flight. The micro-quadrotor results show that the performance indices used to design an optimal flight control law for a micro-quadrotor may be recovered from the closed loop simulated flight trajectories, and the Drosophila results indicate that the combined effect of the insect longitudinal flight control sensing and feedback acts principally to regulate pitch rate.

Wing motion transformation to evaluate aerodynamic coupling in flapping wing flight.

Whether the remarkable flight performance of insects is because the animals leverage inherent physics at this scale or because they employ specialized neural feedback mechanisms is an active research question. In this study, an empirically derived aerodynamics model is used with a transformation involving a delay and a rotation to identify a class of kinematics that provide favorable roll-yaw coupling. The transformation is also used to transform both synthetic and experimentally measured wing motions onto the manifold representing proverse yaw and to quantify the degree to which freely flying insects make use of passive aerodynamic mechanisms to provide proverse roll-yaw turn coordination. The transformation indicates that recorded insect kinematics do act to provide proverse yaw for a variety of maneuvers. This finding suggests that passive aerodynamic mechanisms can act to reduce the neural feedback demands of an insect׳s flight control strategy.

Bio-inspired modeling and implementation of the ocelli visual system of flying insects.

Two visual sensing modalities in insects, the ocelli and compound eyes, provide signals used for flight stabilization and navigation. In this article, a generalized model of the ocellar visual system is developed for a 3-D visual simulation environment based on behavioral, anatomical, and electrophysiological data from several species. A linear measurement model is estimated from Monte Carlo simulation in a cluttered urban environment relating state changes of the vehicle to the outputs of the ocellar model. A fully analog-printed circuit board sensor based on this model is designed and fabricated. Open-loop characterization of the sensor to visual stimuli induced by self motion is performed. Closed-loop stabilizing feedback of the sensor in combination with optic flow sensors is implemented onboard a quadrotor micro-air vehicle and its impulse response is characterized.

Neural dynamics of robust legged robots.

Legged robot control has improved in recent years with the rise of deep reinforcement learning, however, much of the underlying neural mechanisms remain difficult to interpret. Our aim is to leverage bio-inspired methods from computational neuroscience to better understand the neural activity of robust robot locomotion controllers. Similar to past work, we observe that terrain-based curriculum learning improves agent stability. We study the biomechanical responses and neural activity within our neural network controller by simultaneously pairing physical disturbances with targeted neural ablations. We identify an agile hip reflex that enables the robot to regain its balance and recover from lateral perturbations. Model gradients are employed to quantify the relative degree that various sensory feedback channels drive this reflexive behavior. We also find recurrent dynamics are implicated in robust behavior, and utilize sampling-based ablation methods to identify these key neurons. Our framework combines model-based and sampling-based methods for drawing causal relationships between neural network activity and robust embodied robot behavior.

Robust control of electrohydraulic soft robots.

This article introduces a model-based robust control framework for electrohydraulic soft robots. The methods presented herein exploit linear system control theory as it applies to a nonlinear soft robotic system. We employ dynamic mode decomposition with control (DMDc) to create appropriate linear models from real-world measurements. We build on the theory by developing linear models in various operational regions of the system to result in a collection of linear plants used in uncertainty analysis. To complement the uncertainty analyses, we utilize H ∞ ("H Infinity") synthesis techniques to determine an optimal controller to meet performance requirements for the nominal plant. Following this methodology, we demonstrate robust control over a multi-input multi-output (MIMO) hydraulically amplified self-healing electrostatic (HASEL)-actuated system. The simplifications in the proposed framework help address the inherent uncertainties and complexities of compliant robots, providing a flexible approach for real-time control of soft robotic systems in real-world applications.

Optic flow estimation on trajectories generated by bio-inspired closed-loop flight.

We generated panoramic imagery by simulating a fly-like robot carrying an imaging sensor, moving in free flight through a virtual arena bounded by walls, and containing obstructions. Flight was conducted under closed-loop control by a bio-inspired algorithm for visual guidance with feedback signals corresponding to the true optic flow that would be induced on an imager (computed by known kinematics and position of the robot relative to the environment). The robot had dynamics representative of a housefly-sized organism, although simplified to two-degree-of-freedom flight to generate uniaxial (azimuthal) optic flow on the retina in the plane of travel. Surfaces in the environment contained images of natural and man-made scenes that were captured by the moving sensor. Two bio-inspired motion detection algorithms and two computational optic flow estimation algorithms were applied to sequences of image data, and their performance as optic flow estimators was evaluated by estimating the mutual information between outputs and true optic flow in an equatorial section of the visual field. Mutual information for individual estimators at particular locations within the visual field was surprisingly low (less than 1 bit in all cases) and considerably poorer for the bio-inspired algorithms that the man-made computational algorithms. However, mutual information between weighted sums of these signals and comparable sums of the true optic flow showed significant increases for the bio-inspired algorithms, whereas such improvement did not occur for the computational algorithms. Such summation is representative of the spatial integration performed by wide-field motion-sensitive neurons in the third optic ganglia of flies.

Miniaturized Circuitry for Capacitive Self-Sensing and Closed-Loop Control of Soft Electrostatic Transducers.

Soft robotics is a field of robotic system design characterized by materials and structures that exhibit large-scale deformation, high compliance, and rich multifunctionality. The incorporation of soft and deformable structures endows soft robotic systems with the compliance and resiliency that makes them well adapted for unstructured and dynamic environments. Although actuation mechanisms for soft robots vary widely, soft electrostatic transducers such as dielectric elastomer actuators (DEAs) and hydraulically amplified self-healing electrostatic (HASEL) actuators have demonstrated promise due to their muscle-like performance and capacitive self-sensing capabilities. Despite previous efforts to implement self-sensing in electrostatic transducers by overlaying sinusoidal low-voltage signals, these designs still require sensing high-voltage signals, requiring bulky components that prevent integration with miniature untethered soft robots. We present a circuit design that eliminates the need for any high-voltage sensing components, thereby facilitating the design of simple low cost circuits using off-the-shelf components. Using this circuit, we perform simultaneous sensing and actuation for a range of electrostatic transducers including circular DEAs and HASEL actuators and demonstrate accurate estimated displacements with errors <4%. We further develop this circuit into a compact and portable system that couples high voltage actuation, sensing, and computation as a prototype toward untethered multifunctional soft robotic systems. Finally, we demonstrate the capabilities of our self-sensing design through feedback control of a robotic arm powered by Peano-HASEL actuators.

Control theoretic interpretation of directional motion preferences in optic flow processing interneurons.

In this article, we formalize the processing of optic flow in identified fly lobula plate tangential cells and develop a control theoretic framework that suggests how the signals of these cells may be combined and used to achieve reflex-like navigation behavior. We show that this feedback gain synthesis task can be cast as a combined static state estimation and linear feedback control problem. Our framework allows us to analyze and determine the relationship between optic flow measurements and actuator commands, which greatly simplifies the implementation of biologically inspired control architectures on terrestrial and aerial robotic platforms.

Robust analysis of an underwater navigational strategy in electrically heterogeneous corridors.

Obstacles and other global stimuli provide relevant navigational cues to a weakly electric fish. In this work, robust analysis of a control strategy based on electrolocation for performing obstacle avoidance in electrically heterogeneous corridors is presented and validated. Static output feedback control is shown to achieve the desired goal of reflexive obstacle avoidance in such environments in simulation and experimentation. The proposed approach is computationally inexpensive and readily implementable on a small scale underwater vehicle, making underwater autonomous navigation feasible in real-time.

Electrolocation-based underwater obstacle avoidance using wide-field integration methods.

Weakly electric fish are capable of efficiently performing obstacle avoidance in dark and navigationally challenging aquatic environments using electrosensory information. This sensory modality enables extraction of relevant proximity information about surrounding obstacles by interpretation of perturbations induced to the fish's self-generated electric field. In this paper, reflexive obstacle avoidance is demonstrated by extracting relative proximity information using spatial decompositions of the perturbation signal, also called an electric image. Electrostatics equations were formulated for mathematically expressing electric images due to a straight tunnel to the electric field generated with a planar electro-sensor model. These equations were further used to design a wide-field integration based static output feedback controller. The controller was implemented in quasi-static simulations for environments with complicated geometries modelled using finite element methods to demonstrate sense and avoid behaviours. The simulation results were confirmed by performing experiments using a computer operated gantry system in environments lined with either conductive or non-conductive objects acting as global stimuli to the field of the electro-sensor. The proposed approach is computationally inexpensive and readily implementable, making underwater autonomous navigation in real-time feasible.

From falling to flying: the path to powered flight of a robotic samara nano air vehicle.

This paper details the development of a nano-scale (>15 cm) robotic samara, or winged seed. The design of prototypes inspired by naturally occurring geometries is presented along with a detailed experimental process which elucidates similarities between mechanical and robotic samara flight dynamics. The helical trajectories of a samara in flight were observed to differ in-flight path and descent velocity. The body roll and pitch angular rates for the differing trajectories were observed to be coupled to variations in wing pitch, and thus provide a means of control. Inspired by the flight modalities of the bio-inspired samaras, a robotic device has been created that mimics the autorotative capability of the samara, whilst providing the ability to hover, climb and translate. A high-speed camera-based motion capture system is used to observe the flight dynamics of the mechanical and robotic samara. Similarities in the flight dynamics are compared and discussed as it relates to the design of the robotic samara.