In this paper, a soft rotary wing robot capable of flight and control is presented. The Collapsible Airfoil Single Actuator ROtor-craft (CASARO) is a single actuator monocopter that derives its geometric properties from the Samara seed. CASARO achieves better flight efficiency, lift, and handling ergonomics by reducing its overall volume by 91.7% when collapsed and stowed. Unlike conventional rotorcraft, CASARO uses a non-rigid fabric wing to produce lift in flight. It utilizes the robot's rotational velocity to maintain tension within its fabric and airframe, providing adequate lift during its hover state. The conception, design, construction, and control of the soft monowing are demonstrated, including its capability to reduce its footprint with its soft fabric construction. To analyze the flight dynamics of CASARO, the craft is flown indoors autonomously, tracking its wing surface, craft body attitude, and position with various step inputs to observe different wing dynamics. CASARO is also capable of being deployed outdoors for real-life human-operated flight.

In this paper, the dynamics of an emerging class of rotating nature-inspired micro aerial vehicles known as the Monocopter is proven and shown to be differentially flat. By exploiting this phenomenon, trajectory tracking can now be implemented on Monocopters via feed-forward terms that are computed per the trajectory. To demonstrate this, a Monocopter in the form of a Stable Invertible Coaxial Actuated ROtorcraft (SICARO) is chosen to harness this approach fully. The SICARO is capable of flying with either side of the wing facing up and this feature determines the craft’s direction of rotation about its body Z axis as well. In addition, it has the unique feature of a coaxial motor configuration that allows for a pitching-up moment regardless of the wing side facing up. The feed-forward terms computed are fused into a cascaded nonlinear controller on the craft to ensure its effectiveness in tracking trajectories. Lastly, the flight experiments extend to both sides of the wing to validate this method as being applicable for trajectory tracking for Monocopters such as the SICARO which has an extended range of flying capabilities.

The information provided to a chasing quadrotor (Chaser) about a moving target's linear velocity during pursuit offers utility beyond simply knowing how fast the target is moving linearly. During such pursuits for the Chaser, external tracking of its linear velocity is necessary to establish stable close-loop control. The alternative, an open-loop system, relies solely on the linear velocity outputs from its dynamics which usually results in unstable flight performances due to imperfections in the model's fidelity. Thus, this paper presents a Cascading Velocity Model Predictive Control Framework (CVMPC) for the Chaser to leverage on the real-time linear velocity feedback of the target for stable open-loop linear velocity control. By using this information with Gaussian Processes (GPs), the Chaser's linear velocity outputs from its dynamics are augmented in real-time and cascaded from one control step into the next within a Model Predictive Controller. Simulations in Gazebo further verify the performance of CVMPC against a MPC with external linear velocity tracking (close-loop system) with RMSE in euclidean linear velocity and distance being less than 1m/s and 1m respectively.

Pose estimation is of paramount importance for flight control as well as localization and navigation of Unmanned Aerial Vehicles (UAVs) to enable autonomous operations. In environments without GPS, such estimation can only be determined using onboard sensors; optical flow using a monocular camera is a popular approach. Monocopters are a class of nature inspired UAVs known as free rotors where their design and flight dynamics are inspired by the falling samara seed. With a constantly rotating body frame, free rotors introduces some unique challenges for visual perception required during optical flow sensing. This paper addresses these problems with the introduction of SHIFT (Selective Heading Image for Translation) that selects optimal images for determining translation with optical flow. It achieves this by decoupling rotation vectors about the optical axis from translation vectors in a flow field through the separate tracking of orientation and position using an Unscented Kalman Filter with phase correlation in the log-polar and spatial domain. The experiments show that SHIFT's estimation in orientation is stable even under sinusoidal excitation with a median absolute percentage errors of less than 1%. It is able to track position and orientation of a UAV accurately.

In this paper, the high angular rate estimation for simultaneous localisation and mapping (SLAM) of a Flying Li-DAR (Flydar) is presented. The proposed EKF-based algorithm exploits the sinusoidal magnetometer measurement generated by the continuously rotating airframe for estimation of the robot hovering angular velocity. Significantly, the proposed method does not rely on additional sensors other than existing IMU sensors already being used for flight stabilization. The gyro measurement and the gyro bias are incorporated as a control input and a filter state respectively to enable estimation even under gyro saturation condition. Additionally, this work proposes leveraging on the inherently rotating locomotion to generate a planar lidar scan using only a single-point laser for possible lightweight autonomy. The proposed estimation method was experimentally evaluated on a ground rotating rig up to twice the gyro saturation limit with an effective rms error of 0.0045Hz; and on the proposed aerial platform - Flydar - hovering beyond the saturation limit with a rms error of 0.0056Hz. Lastly, the proposed method for SLAM using the rotating dynamics of Flydar was demonstrated with a localisation accuracy of 0.11m.