The need to create more viable soft sensors is increasing in tandem with the growing interest in soft robots. Several sensing methods, like capacitive stretch sensing and intrinsic capacitive self-sensing, have proven to be useful when controlling soft electro-hydraulic actuators, but are still problematic. This is due to challenges around high-voltage electronic interference or the inability to accurately sense the actuator at higher actuation frequencies. These issues are compounded when trying to sense and control the movement of a multiactuator system. To address these shortcomings, we describe a two-part magnetic sensing mechanism to measure the changes in displacement of an electro-hydraulic (HASEL) actuator. Our magnetic sensing mechanism can achieve high accuracy and precision for the HASEL actuator displacement range, and accurately tracks motion at actuation frequencies up to 30 Hz, while being robust to changes in ambient temperature and relative humidity. The high accuracy of the magnetic sensing mechanism is also further emphasized in the gripper demonstration. Using this sensing mechanism, we can detect submillimeter difference in the diameters of three tomatoes. Finally, we successfully perform closed-loop control of one folded HASEL actuator using the sensor, which is then scaled into a deformable tilting platform of six units (one HASEL actuator and one sensor) that control a desired end effector position in 3D space. This work demonstrates the first instance of sensing electro-hydraulic deformation using a magnetic sensing mechanism. The ability to more accurately and precisely sense and control HASEL actuators and similar soft actuators is necessary to improve the abilities of soft, robotic platforms.

The paper proposes a path planning solution for autonomous robotic exploration of complex subterranean environments. The work contributes to the family of graph-based planners by bringing the following improvements. Firstly, an occupancy grid-based sample-and-project solution to terrain-assessment is proposed instead of building an explicit elevation map of the environment. Secondly, the solution-search method is formulated as a constraint-satisfaction problem to obtain a good-enough solution instead of optimizing for a single objective function parametrized by penalty gains. This method is shown to significantly improve the computational efficiency of the planner. Thirdly, a coordination solution is proposed that relies on the position histories of the robots instead of merged-maps or merged-graphs, therefore, making the planning solution resilient to inter-robot map misalignments, while also reducing the communication bandwidth required to carry out coordination. Finally, the planner also takes into account changes in the environment such as blocked passages that are initially open. The proposed planning solution is demonstrated at the DARPA Subterranean Challenge final event by team MARBLE, the third place finisher of the challenge.

The paper proposes a reliable and robust planning solution to the long range robotic navigation problem in extremely cluttered environments. A two-layer planning architecture is proposed that leverages both the environment map and the direct depth sensor information to ensure maximal information gain out of the onboard sensors. A frontier-based pose sampling technique is used with a fast marching cost-to-go calculation to select a goal pose and plan a path to maximize robot exploration rate. An artificial potential function approach, relying on direct depth measurements, enables the robot to follow the path while simultaneously avoiding small scene obstacles that are not captured in the map due to mapping and localization uncertainties. We demonstrate the feasibility and robustness of the proposed approach through field deployments in a structurally complex warehouse using a micro-aerial vehicle (MAV) with all the sensing and computations performed onboard.

Autonomous navigation in unknown environments with the intent of exploring all traversable areas is a significant challenge for robotic platforms. In this paper, a simple yet reliable method for exploring unknown environments is presented based on bio-inspired reactive control and metric-topological planning. The reactive control algorithm is modeled after the spatial decomposition of wide and small-field patterns of optic flow in the insect visuomotor system. Centering behaviour and small obstacle detection and avoidance are achieved through wide-field integration and Fourier residual analysis of instantaneous measured nearness respectively. A topological graph is estimated using image processing techniques on a continuous occupancy grid. Node paths are rapidly generated to navigate to the nearest unexplored edge in the graph. It is shown through rigorous field-testing that the proposed control and planning method is robust, reliable, and computationally efficient.

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.

This paper describes a system identification method and the development of a closed-loop controller for a Hydraulically Amplified Self-healing Electrostatic (HASEL) actuator. Our efforts focus on developing a reliable and consistent way to identify system models for these soft robotic actuators using high-speed videography based motion tracking. Utilizing a mass-spring-damper model we are able to accurately capture the behavior of a HASEL actuator. We use the resulting plant model to design a Proportional-Integral controller that demonstrates improved closed-loop tracking and steady-state error performance.

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.

The goal of this research is to develop a lightweight, high bandwidth control actuator that can be integrated on a flapping wing nano air vehicle (NAV). Traditional control actuators for air vehicles including DC servomotors and shape memory alloy are either too heavy or too slow to control a fast moving NAV. This paper develops a new bio-inspired active tail mechanism to stabilize an inverted pendulum with the same mass and inertia as the NAV. An analysis of the dynamic model shows a critical angle at which the control actuator can no longer stabilize the pendulum varies significantly with link lengths and mass ratios. Based on this dynamic model, an LQR controller is developed and implemented as a state space controller on a microcontroller based test setup. Using a gyroscope to measure the pendulum's angular velocity and estimate the angle, the active tail mechanism was able to stabilize the pendulum for over five minutes before falling due to drift in the gyroscope sensor.

This paper describes the physics-based modeling of the second visual system, the ocelli, common to many species of flying insects. We perform high fidelity visual simulation to study its role in estimating angular velocities during flight. Development of a fully analog, bio-mimetic ocellar sensor is detailed.

We present a fly-inspired integrated motion image sensor for autonomous navigation with on-chip optic flow computation, mismatch compensation, and programmable spatial filters. The sensor computes spatial motion patterns from local motion signals extracted by elementary motion detectors (EMDs). Floating gate transistors are used to cancel fabrication mismatch in the EMDs and also to program spatial filtering coefficients. The sensor is the first reported motion image sensor that extracts multiple motion parameters from the spatial motion pattern, enabled by the mismatch compensation and programmable filters. An array of 19 EMDs provide inputs to 4 weighted spatial filters on a 3 × 3 mm2chip in 0.5 μm CMOS technology. Results from both benchtop tests and closed loop navigation of a ground vehicle are reported.

In this paper, wide-field integration methods, which are inspired by the spatial decompositions of wide-field patterns of optic flow in the insect visuomotor system, are explored as an efficient means to extract visual cues for guidance and navigation. A control-theoretic framework is developed and used to quantitatively link weighting functions to behaviorally-relevant interpretations such as relative orientation, position, and speed in a corridor environment. It is shown through analysis and demonstration on a ground vehicle that the proposed sensorimotor architecture gives rise to navigational heuristics, namely, centering and speed regulation, which are observed in natural systems.

Wide-field integration (WFI) methods, which are based on the spatial decompositions of optic flow performed by motion sensitive tangential neurons within the insect visuomotor system, have been shown to be an efficient and robust way to extract visual cues for guidance and navigation. In this paper, previous analytical results on WFI-based autonomous local guidance are validated on a wheeled robotic platform. Successful corridor navigation is achieved through closed loop feedback of the first two spatial harmonics of the optic flow, which provide the lateral and rotational stiffness with respect to local obstacles required to stabilize a collision-free trajectory through the environment.