Design of Fuzzy-PID Controller for Quadcopter Trajectory-Tracking

Rabah, Mohammed; Rohan, Ali; Han, Yun-Jong; Kim, Sung Hoon

doi:10.5391/ijfis.2018.18.3.204

Cited by 45 publications

(42 citation statements)

References 15 publications

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“…Using proportional-derivative (PD) function [25] and proportional-integral-derivative (PID) function [26] controllers is one of the common techniques for trajectory tracking for following a GMT, but without adequate position and velocity information it would not support both obstacle avoidance and following the target. Introducing a fuzzy logic controller to the PD and PID controllers is another technique used for controlling a UAV to follow a GMT [27], but not without several drawbacks due to their high computational requirements, big overshoot and oscillations (especially with sudden change in the speed and direction of the target), and inability to deal with dynamic obstacles. In [12], a fuzzy-PI based path planning technique for following the movements of mobile vehicles has been presented, along with the simulation and experiment results.…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Artificial Potential Field (D-APF) UAV Path Planning Technique for Following Ground Moving Targets

Jayaweera

Hanoun

2020

IEEE Access

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Path planning is a vital and challenging component in the support of Unmanned Aerial Vehicles (UAVs) and their deployment in autonomous missions, such as following ground moving target. Few attempts are reported in the literature on multirotor UAV path planning techniques for following ground moving targets despite the great improvement in their control dynamics, flying behaviors and hardware specifications. These attempts suffer several drawbacks including their hardware dependency, high computational requirements, inability to handle obstacles and dynamic environments in addition to their low performance regarding the moving target speed variations. In this paper, a novel dynamic Artificial Potential Field (D-APF) path planning technique is developed for multirotor UAVs for following ground moving targets. The UAV produced path is a smooth and flyable path suitable to dynamic environments with obstacles and can handle different motion profiles for the ground moving target including change in speed and direction. Additionally, the proposed path planning technique effectively supports UAVs following ground moving targets while maneuvering ahead and at a standoff distance from the target. It is hardware-independent where it can be used on most types of multirotor UAVs with an autopilot flight controller and basic sensors for distance measurements. The developed path planning technique is tested and validated against existing general potential field techniques for different simulation scenarios in ROS and gazebo-supported PX4-SITL. Simulation results show that the proposed D-APF is better suited for UAV path planning for following moving ground targets compared to existing general APFs. In addition, it outperforms the general APFs as it is more suitable for UAVs flying in environments with dynamic and unknown obstacles. INDEX TERMS Unmanned Aerial Vehicles, path planning, Artificial Potential Field, ground moving targets.

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Section: Related Workmentioning

confidence: 99%

A Dynamic Artificial Potential Field (D-APF) UAV Path Planning Technique for Following Ground Moving Targets

Jayaweera

Hanoun

2020

IEEE Access

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“…Many control systems for tracking and following object have been developed. A controller system for quadcopter to follow different trajectories is presented in [19]. Authors in [20] developed a non-linear control algorithm for object tracking.…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous Parallelization for Object Detection and Tracking in UAVs

et al. 2020

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Recent technical advancements in both fields of unmanned aerial vehicles (UAV) control and artificial intelligence (AI) have made a certain realm of applications possible. However, one of the main problems in integration of these two areas is the bottleneck of computing AI applications on UAV's resource limited platform. One of the main solution for this problem is that AI and control software from one side and computing hardware mounted on UAV from the other side be adopted together based on the main constraints of the resource limited computing platform on UAV. Basically, the target constraints of such adaptation are performance, energy efficiency, and accuracy. In this paper, we propose a strategy to integrate and adopt the commonly used object detection and tracking algorithm and UAV control software to be executed on a heterogeneous resource limited computing units on a UAV. For object detection, a convolutional neural network (CNN) algorithm is used. For object tracking, a novel algorithm is proposed that can execute along with object tracking via sequential stream data. For UAV control, a Gain-Scheduled PID controller is designed that steers the UAV by continuously manipulation of the actuators based on the stream data from the tracking unit and dynamics of the UAV. All the algorithms are adopted to be executed on a heterogeneous platform including NVIDIA Jetson TX2 embedded computer and an ARM Cortex M4. The observation from real-time operation of the platform shows that using the proposed platform reduces the power consumption by 53.69% in contrast with other existing methods while having marginal penalty for object detection and tracking parts.

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“…In [15], the drone velocities were estimated using frame registration. In [16] and [17], the fuzzy controllers were studied for quadcopter trajectory tracking.…”

Section: Introductionmentioning

confidence: 99%

Moving Vehicle Detection and Drone Velocity Estimation with a Moving Drone

Nam¹,

Yeom²

2020

IJFIS

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Small unmanned aerial vehicles can be effectively used for aerial video surveillance. Although the field of view of the camera mounted on the drone is limited, flying drones can expand their surveillance coverage. In this paper, we address the detection of moving targets in urban environments with a moving drone. The drone moves at a constant velocity and captures video clips of moving vehicles such as cars, buses, and bicycles. Moving vehicle detection consists of frame registration and subtraction followed by thresholding, morphological operations and false blob reduction. First, two consecutive frames are registered; the coordinates of the next frame are compensated by a displacement vector that minimizes the sum of absolute difference between the two frames. Second, the next compensated frame is subtracted from the current frame, and the binary image is generated by thresholding. Finally, morphological operations and false alarm removal extract the target blobs. In the experiments, the drone flies at a constant speed of 5.1 m/s at an altitude of 150 m while capturing video clips of nine moving targets. The detection and false alarm rates as well as the receiver operating characteristic curves are obtained, and the drone velocities in the x and y directions are estimated by the displacement vector. The average detection rate ranges from 90% to 97% while the false alarm rate ranges from 0.06 to 0.5. The root mean square error of the speed is 0.07 m/s when the reference frame is fixed, showing the robustness of the proposed method.

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Design of Fuzzy-PID Controller for Quadcopter Trajectory-Tracking

Cited by 45 publications

References 15 publications

A Dynamic Artificial Potential Field (D-APF) UAV Path Planning Technique for Following Ground Moving Targets

A Dynamic Artificial Potential Field (D-APF) UAV Path Planning Technique for Following Ground Moving Targets

Heterogeneous Parallelization for Object Detection and Tracking in UAVs

Moving Vehicle Detection and Drone Velocity Estimation with a Moving Drone

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