This paper presents a multirotor system with an integrated net mechanism for autonomous object detection and collection from water surfaces utilizing only onboard sensors. The task of object collection on water surfaces is challenging due to the following reasons: i) propeller outwash alters the object’s dynamics, ii) unpredictable current flows, iii) extreme reflection and glare off water surfaces affects the object detection and iv) noisy height measurements over water surface. A linearized polarizing filter is used with an onboard camera to eliminate excessive reflection and glare. A contour-based detection algorithm is implemented for detecting the targeted objects on water surface. Subsequently, a boundary layer sliding mode control is implemented to ensure the system is robust to modeling uncertainties. A dynamic sliding surface is designed based on constrained linear model predictive control. The efficacy of the proposed collection system is validated by multiple outdoor tests. Three objects of different shapes and sizes are collected with an overall success rate of 91.6%.

This paper presents a nonlinear disturbance observer (NDOB) for active disturbance rejection in the attitude control loop for quadrotors. An optimization framework is developed for tuning the parameter in the NDOB structure, which includes the infinity-norm minimization of the weighted sum of noise-to-output transfer function and load disturbance sensitivity function. Subsequently, the minimization generates an optimal value of the parameter based on the tradeoff between disturbance rejection and noise propagation in the system. The proposed structure is implemented on PIXHAWK, a real-time embedded flight control unit. Simulation tests are carried out on a custom built, high-fidelity simulator providing physically accurate simulations. Furthermore, experimental flight tests are conducted to demonstrate the performance of the proposed approach. The system is injected with step, sinusoidal, and square wave disturbances, and the corresponding system tracking performance is recorded. Experimental results show that the proposed algorithm attenuates the disturbances better compared to just a baseline controller implementation. The proposed algorithm is computationally cheap, an active disturbance rejection technique and robust to exogenous disturbances.

Autonomous missions of small unmanned aerial vehicles (UAVs) are prone to collisions owing to environmental disturbances and localization errors. Consequently, a UAV that can endure collisions and perform recovery control in critical aerial missions is desirable to prevent loss of the vehicle and/or payload. We address this problem by proposing a novel foldable quadrotor system which can sustain collisions and recover safely. The quadrotor is designed with integrated mechanical compliance using a torsional spring such that the impact time is increased and the net impact force on the main body is decreased. The post-collision dynamics is analysed and a recovery controller is proposed which stabilizes the system to a hovering location without additional collisions. Flight test results on the proposed and a conventional quadrotor demonstrate that for the former, integrated spring-damper characteristics reduce the rebound velocity and lead to simple recovery control algorithms in the event of unintended collisions as compared to a rigid quadrotor of the same dimension.

This paper presents the design and control of a novel quadrotor with a variable geometry to physically interact with cluttered environments and fly through narrow gaps and passageways. This compliant quadrotor with passive morphing capabilities is designed using torsional springs at every arm hinge to allow for rotation driven by external forces. We derive the dynamic model of this variable geometry quadrotor (SQUEEZE), and develop an adaptive controller for trajectory tracking. The corresponding Lyapunov stability proof of attitude tracking is also presented. Further, an admittance controller is designed to account for changes in yaw due to physical interactions with the environment. Finally, the proposed design is validated in flight tests with two setups: a small gap and a passageway. The experimental results demonstrate the unique capability of the SQUEEZE in navigating through constrained narrow spaces.

This paper presents the development of a novel hybrid unmanned aerial-ground vehicle (UAGV) to improve the success rate in grasping tasks. With the proposed method, the vehicle lands near the object and attempts grasping in its ground modality. A passive propulsion mechanism using motorized deflectors is employed for the ground locomotion. A nonlinear thrust vector model for ground locomotion is derived and validated in SolidWorks simulations. A nonlinear model predictive controller (NMPC) is employed for precise ground navigation. Multiple experiments are conducted with different initial conditions to demonstrate the performance of the proposed UAGV and NMPC for autonomous grasping. It is shown that the system is able to reliably reach and grasp the object when initially aligned with the object at distances between 1 and 1.5 meters.

In this paper, a novel foldable quadrotor (FQR) inspired by an origami mechanism is designed. The FQR can fold its arms during flight to enable aggressive turning maneuvers and operations in cluttered environments. A dynamic model of folding is built for this system with the collected data, and a feedback controller is designed to control the position and orientation of the FQR. Lyapunov stability analysis is conducted to show that the system is stable during arm folding and extension, and motion planning of the FQR is achieved based on a modified minimum-snap trajectory generation method. Simulation results are provided to demonstrate the advantage of this design over the conventional quad-rotor in obstacle avoidance during flight.

In this paper, an image based visual servo (IBVS) scheme is developed for a hexacopter, equipped with a robotic soft grasper to perform autonomous object detection and grasping. The structural design of the hexacopter-soft grasper system is analyzed to study the soft grasper’s influence on the multirotor’s aerodynamics. The object detection, tracking and trajectory planning are implemented on a high-level computer which sends position and velocity setpoints to the flight controller. A soft robotic grasper is mounted on the UAV to enable the collection of various contaminants. The use of soft robotics removes excess weight associated with traditional rigid graspers, as well as simplifies the controls of the grasping mechanics. Collected experimental results demonstrate autonomous object detection, tracking and grasping. This pipeline would enable the system to autonomously collect solid and liquid contaminants in water canal based on GPS and multi-camera system. It can also be used for more complex aerial manipulation including in-flight grasping.

In this paper, a disturbance observer (DOB) approach is developed to reject disturbance and achieve precision position control of a quadcopter. The Q-filter is tuned to deal with both drift and noise from the onboard sensors. The position estimates are obtained from a hybrid low-pass and de-trending (HLPD) filter. Simulation and experimental flight tests are conducted to demonstrate the performance of the proposed control technique. Experimental flight tests, which include hover test and waypoint following test, prove that the performance of the proposed algorithm is better than the widely used cascaded PID-PID structure. The algorithm doesn’t rely on a motion capture system for position estimates which makes it suitable for outdoor flight missions.

In this paper, a hybrid low-pass and de-trending (HLPD) filtering technique is proposed to achieve robust position estimates using an optical flow based sensor which calculates velocity information at a rate of 400 Hz. In order to filter out the high-frequency oscillation in the velocity information, a standard low-pass filter is implemented. The low-pass filter successfully eliminates sudden jumps and missing data-points, which prevents unprecedented maneuvers and mid-air crashes. The integrated position estimate has the accumulated drift which occurs due to electrical signal and temperature fluctuations together with other environmental factors which affect the data acquisition from the optical flow sensor. A recursive linear least squares fit is performed for the drift model and de-trending is applied to the integrated position signal. The performance of the proposed estimator is validated by comparing with model-identification based weighted average (MI-WA) position estimator, which is commonly used in quadcopters for position estimation. Simulation and experimental flight tests are conducted and the results show that the flight performance of HLPD filter is better than the extensively used MI-WA position filter in hover and square pattern flight tests.

Various embodiments for a foldable quad-rotor (FQR) inspired by an origami mechanism are disclosed herein. The FQR can fold its arms during flight to enable aggressive turning maneuvers and operations in cluttered environments. A dynamic model of folding is built for this system with the collected data, and a feedback controller is designed to control the position and orientation of the FQR. Lyapunov stability analysis is conducted to show that the system is stable during arm folding and extension, and motion planning of the FQR is achieved based on a modified minimum-snap trajectory generation method.