Small-object detection and avoidance in unknown environments is a significant challenge to overcome for small autonomous vehicles that are generally highly agile and restricted in payload and computational processing power. Typical machine-vision and range measurement-based solutions suffer either from restricted fields-of-view or significant computational complexity and are, hence, not easily portable to small platforms. In order to overcome these drawbacks, in this paper, two novel bioinspired approaches are proposed to extract information about small-field objects contained in planar optic flow. The first approach, which is analogous to the small-field extraction process hypothesized to occur in the lobula plate of the fly visual system, is based on the Fourier residual analysis of instantaneous optic flow. Alternatively, the flow-of-flow method is the engineering analogue of the small-field extraction process thought to occur in the fruit-fly's medulla, and extracts high-frequency content of optic flow by means of an elementary motion detector array. Both approaches extract instantaneous relative range and bearing of small-field obstacles from planar optic flow in a local environment characterized by small and wide-field obstacles, which is then combined with an artificial potential function-based low-order steering control law. The proposed sensing and control scheme is experimentally validated with a quadrotor vehicle that is able to effectively navigate an unknown environment laden with small-field clutter. This bioinspired approach is computationally efficient, which renders extraction of vehicle velocity and local environment structure superfluous, and thus, serves as a robust, reflexive solution to the problem of small-object detection, and avoidance for small autonomous robots.

Small-object detection and avoidance in unknown environments is a significant challenge to overcome for small autonomous vehicles that are generally highly agile and restricted in payload and computational processing power. Typical machine-vision and range measurement based solutions suffer either from restricted fields-of-view or significant computational complexity and are not easily portable to small platforms. In this paper, a novel bio-inspired navigation technique is introduced that is modeled using analogues of the small-field motion-sensitive interneurons of the insect visuomotor system. The proposed technique achieves small-field object detection based on Fourier residual analysis of instantaneous optic flow. The small field signal is used to extract relative range and bearing of the nearest obstacle, which is then combined with an artificial potential function-based low-order steering control law. The proposed sensing and control scheme is experimentally validated with a quadrotor vehicle that is able to effectively navigate an unknown environment laden with small-field clutter. This bio-inspired approach is computationally efficient and serves as a robust, reflexive solution to the problem of small-object detection and avoidance for autonomous robots.

Weakly electric fish use a navigational technique called electrolocation to investigate their surroundings for predator, prey and obstacles. Obstacles and other global stimuli are perceived as perturbations to the fish's self-generated electric field, which provide relevant navigational cues to the fish. In this work a control strategy based on electrolocation for performing obstacle avoidance in electrically heterogeneous environments is presented and validated. The control strategy developed for a straight tunnel is shown to be robust to small variations in the tunnel width. This justifies its application to other heterogeneous corridor-like environments. 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.

Kapton strain-based hair sensors were designed and fabricated to measure flow on a micro air vehicle. The fabrication process is carried out using a single mask and a combination of cleanroom and regular lab room processing. The processing resulted in successful realization of 6.4 mm long sensors with high yield. Associated signal conditioning circuitry was developed to extract small variations in resistance due to flow while suppressing noise. Three sensors were deployed on a fuselage shaped structure and characterized in a wind tunnel. Static linear and nonlinear estimation schemes were obtained based on the characterization data to determine the angle of attack, sideslip angle and airspeed values. For small angles of perturbation, the estimation scheme demonstrated good accuracy in determining the variables of interest. The sensors along with the signal conditioning circuitry are sufficiently lightweight that they can be used for estimating velocity states on micro air vehicles.

Potential flow theory is used to derive velocity magnitude information for a) inclined flow past a stationary cylinder, b) moving cylinder inside a tunnel and c) moving cylinder inside a circular arena. Wide-Field Integration methods which are bioinspired techniques are used to extract relative states from the velocity magnitude signals for the above three situations. A linear controller using these relative states has been simulated to produce rheotaxis, wide-field obstacle avoidance, centering and wall following behavior. The approach is extended to a viscous inclined flow over an airfoil using an off-the-shelf CFD package where rheotaxis is again demonstrated using the same controller.