Abstract
How to save the energy of unmanned aerial vehicles (UAVs) and then enable long-distance transport is a very real and difficult task. However, for UAVs, the classic object detection algorithm, such as the deep convolutional neural network–based object detection algorithm and the classic flight control algorithm, such as the PID-based position control algorithm, require significant energy, which limits the application scenarios of the UAV system. In view of this problem, this paper proposes a lightweight object detection network and a linear active disturbance rejection controller (LADRC) for the quadrotor with the cable-suspended payload (QCSP) system to improve energy efficiency. The system uses a YOLOV3 network and embeds it into the Jesson NX mobile platform to accurately detect the target position. Furthermore, a nonlinear velocity controller with a cable-suspended structure to control the velocity of the payload, a LADRC algorithm is adopted to achieve fast and accurate control of the payload position. Simulations and real flight experiments show that the proposed object detection algorithm and the LADRC control strategy can save the energy of drone effectively.
Highlights
With the development of unmanned aerial vehicle (UAV) technology (Wu et al, 2018), drone transport has become an important branch of unmanned aerial vehicles (UAVs) applications
In the transportation process, in addition to the energy required for drone flight, the object detection algorithm and the quadrotor with the cablesuspended payload (QCSP) flight control strategy consume great energy
To improve the energy efficiency of the QCSP systems, we propose a lightweight object detection algorithm and an linear active disturbance rejection controller (LADRC) payload position control strategy for the QCSP
Summary
With the development of unmanned aerial vehicle (UAV) technology (Wu et al, 2018), drone transport has become an important branch of UAV applications. To enable a multirotor UAV to achieve static hovering, Mochida, et al (Mochida et al, 2021) propose a geometric method that reveals the relationship between the position of the center of mass (CoM) and the rotor placement of a multirotor UAV with upward-oriented rotors These methods can effectively help UAVs accomplish their tasks, but they do not take into account the energy limitation of UAVs; the algorithm is complex and not applicable to the QCSP. The contributions of this paper to the energy saving of QCSP mainly include 1) a new QCSP experimental platform with embedded vision detection is constructed, and a lightweight object detection network is used to obtain position information; 2) an LADRC algorithm is used to control the payload position quickly and efficiently.
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