An aerial manipulator is a new kind of flying robot system composed of a rotorcraft unmanned aerial vehicle (UAV) and a multi-link robotic arm. It gives the flying robot the capacity to complete manipulation tasks. Steady flight is essential for an aerial manipulator to complete manipulation tasks. This paper focuses on the steady flight control performance of the aerial manipulator. A separate control strategy is used in the aerial manipulator system, in which the UAV and the manipulator are controlled separately. In order to complete tasks in environments with strong wind disturbance, an acceleration feedback enhanced robust H∞ controller was designed for the UAV in the aerial manipulator. The controller is based on the hierarchical inner-outer loop control structure of the UAV and composed of a robust H∞ controller and acceleration feedback enhanced term, which is used to compensate for the wind disturbance. Experimental results of aerial grasping of a target object show that the controller can suppress the wind disturbance effectively, and make the aerial manipulator hover steadily with sufficient accuracy to complete aerial manipulation tasks in strong wind.

In this paper, a simple active-model-based control scheme is developed for the quadrotor slung load (QSL) system. The scheme works to improve the rejection of the influences caused by the abruptly changed load as a complementary enhancement while maintaining the structure and parameters of the original controller. A linearized model is first constructed with respect to the hovering state of a quadrotor. Modeling error is then introduced to describe the uncertainties caused by the load change and the simplified model. The modeling error is actively estimated by a Kalman filter (KF), while the estimation is further integrated into a normal controller, to enhance its performance of disturbance rejection. Experiments are conducted on a quadrotor controlled by the Pixhawk, which is one of the most popular controllers commercially available on the market. The improvements of the proposed scheme are shown by the comparisons between the controls with and without the active-model-based enhancement. The experiments also indicate that, with its simple structure and less computational algorithm, this active-model-based enhancement would be a feasible approach to enhance the commercial UAV controller to handle more uncertainties.

Multirotor UAVs are becoming increasingly important in both civilian and military fields. In the plateau environment, the endurance time of multirotor UAVs is still too short for mission requirements. Thus, the optimisation of the efficiency of the power system is considered to be an effective way to overcome this problem. This paper presents a practical method of power system design to maximise endurance time in a plateau environment. First, the optimal proportion of battery quality is analysed from the perspective of energy consumption of multirotor UAVs. Second, the modelling method of each component of the power system is studied separately. Then, the endurance solution method is given, the multirotor UAV power system is optimised and analysed according to the given design requirements, and the effectiveness and practicability of the method are verified through experiments.

Given the payload limitation of unmanned aerial vehicles (UAVs), lightweight sensors such as camera, inertial measurement unit (IMU), and GPS, are ideal onboard measurement devices. By fusing multiple sensors, accurate state estimations can be achieved. Robustness against sensor faults is also possible because of redundancy. However, scale estimation of visual systems (visual odometry or visual inertial odometry, VO/VIO) suffers from sensor noise and special-case movements such as uniform linear motion. Thus, in this paper, a scale insensitive multi-sensor fusion (SIMSF) framework based on graph optimization is proposed. This framework combines the local estimation of the VO/VIO and global sensors to infer the accurate global state estimation of UAVs in real time. A similarity transformation between the local frame of the VO/VIO and the global frame is estimated by optimizing the poses of the most recent UAV states. In particular, for VO, an initial scale is estimated by aligning the VO with the IMU and GPS measurements. Moreover, a fault detection method for VO/VIO is also proposed to enhance the robustness of the fusion framework. The proposed methods are tested on a UAV platform and evaluated in several challenging environments. A comparison between our results and the results from other state-of-the-art algorithms demonstrate the superior accuracy, robustness, and real-time performance of our system. Our work is also a general fusion framework, which can be extended to other platforms as well.