Towards Non-Robocentric Dynamic Landing of Quadrotor UAVs

In this work, we propose a dynamic landing solution without the need for onboard exteroceptive sensors and an expensive computation unit, where all localization and control modules are carried out on the ground in a non-inertial frame. Our system starts with a relative state estimator of the aerial robot from the perspective of the landing platform, where the state tracking of the UAV is done through a set of onboard LED markers and an on-ground camera; the state is expressed geometrically on manifold, and is returned by Iterated Extended Kalman filter (IEKF) algorithm. Subsequently, a motion planning module is developed to guide the landing process, formulating it as a minimum jerk trajectory by applying the differential flatness property. Considering visibility and dynamic constraints, the problem is solved using quadratic programming, and the final motion primitive is expressed through piecewise polynomials. Through a series of experiments, the applicability of this approach is validated by successfully landing 18 cm x 18 cm quadrotor on a 43 cm x 43 cm platform, exhibiting performance comparable to conventional methods. Finally, we provide comprehensive hardware and software details to the research community for future reference.

Hybrid Aerodynamics-Based Model Predictive Control for a Tail-Sitter UAV

It is challenging to model and control a tail-sitter unmanned aerial vehicle (UAV) because its blended wing body generates complicated nonlinear aerodynamic effects, such as wing lift, fuselage drag, and propeller-wing interactions. We therefore devised a hybrid aerodynamic modeling method and model predictive control (MPC) design for a quadrotor tail-sitter UAV. The hybrid model consists of the Newton-Euler equation, which describes quadrotor dynamics, and a feedforward neural network, which learns residual aerodynamic effects. This hybrid model exhibits high predictive accuracy at a low computational cost and was used to implement hybrid MPC, which optimizes the throttle, pitch angle, and roll angle for position tracking. The controller performance was validated in real-world experiments, which obtained a 57% tracking error reduction compared with conventional nonlinear MPC. External wind disturbance was also introduced and the experimental results confirmed the robustness of the controller to these conditions.

A Modular Pneumatic Soft Gripper Design for Aerial Grasping and Landing

Aerial robots have garnered significant attention due to their potential applications in various industries, such as inspection, search and rescue, and drone delivery. However, the ability of these robots to effectively grasp and land on objects or surfaces is often crucial for the successful completion of missions. This paper presents a novel modular soft gripper design tailored explicitly for aerial grasping and landing operations. The proposed modular pneumatic soft gripper incorporates a feed-forward proportional controller to regulate pressure, enabling compliant gripping capabilities. The modular connectors of the soft fingers offer two configurations of the 4-finger soft gripper, H-base and X-base, allowing adaptability to different target objects. Furthermore, when deflated, the gripper can function as a soft landing gear, reducing the weight and complexity of aerial manipulation control and enhancing flight efficiency. We demonstrate the efficacy of indoor aerial grasping and achieve a maximum payload of 217 g for the proposed soft aerial vehicle (SAV), with the weight of the soft drone being 808 g.

A Fast Planning Approach for 3D Short Trajectory with a Parallel Framework

For real applications of unmanned aerial vehicles, the capability of navigating with full autonomy in unknown environments is a crucial requirement. However, planning a shorter path with less computing time is contradictory. To address this problem, we present a framework with the map planner and point cloud planner running in parallel in this paper. The map planner determines the initial path using the improved jump point search method on the 2D map, and then it tries to optimize the path by considering a possible shorter 3D path. The point cloud planner is executed at a high frequency to generate the motion primitives. It makes the drone follow the solved path and avoid the suddenly appearing obstacles nearby. Thus, vehicles can achieve a short trajectory while reacting quickly to the intruding obstacles. We demonstrate fully autonomous quadrotor flight tests in unknown and complex environments with static and dynamic obstacles to validate the proposed method. In simulation and hardware experiments, the proposed framework shows satisfactorily comprehensive performance.

Stereo Visual Inertial Pose Estimation Based on Feedforward-Feedback Loops

In this paper, we present a novel stereo visual inertial pose estimation method. Compared to the widely used filter-based or optimization-based approaches, the pose estimation process is modeled as a control system. Designed feedback or feedforward loops are introduced to achieve the stable control of the system, which include a gradient decreased feedback loop, a roll-pitch feed forward loop and a bias estimation feedback loop. This system, named FLVIS (Feedforward-feedback Loop-based Visual Inertial System), is evaluated on the popular EuRoc MAV dataset. FLVIS achieves high accuracy and robustness with respect to other state-of-the-art visual SLAM approaches. The system has also been implemented and tested on a UAV platform. The source code of this research is public to the research community.