Estimation and Tracking of a Moving Target by Unmanned Aerial Vehicles

Li, Junming; Chen, Ching Wen; Cheng, Teng-Hu

doi:10.23919/acc.2019.8815101

Cited by 14 publications

(10 citation statements)

References 30 publications

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“…Although non-cooperative vision-based localization shows promising results in indoor and outdoor environments, it fails to determine the position information when a direct line of sight is absent. Also, the target is required to continuously remain in the field of view of the UAV on-board sensors during the entire mission for the UAV to be able to determine its positional information [19]. To avoid the drawbacks of GPS-based localization and vision-based non-cooperative localization, hybrid systems are proposed; [20] and [21] present a localization and tracking system using GPS and visual information where one system can be used if the other system fails.…”

Section: Related Workmentioning

confidence: 99%

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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%

“…k p4 and k p5 are the gain factors that control the maximum force generated from (19) and (20) respectively. The positive constants c 5 and c 6 are less than 1.0; therefore, the UAV will receive the vertical directional force before the repulsive force in the XY plane.…”

Section: B D-apf Repulsive Forcementioning

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 types of research on the UKF method are in progress, such as comparing different filtering methods and sensor fusion [4]. The UKF is presently used in a wide range of applications, from target tracking [31]. Some other types of data fusion studies aimed at improving the performance of mobile robots [32].…”

Section: Introductionmentioning

confidence: 99%

An Indoor Mobile Robot Localization in perspective of Analysis and Performance using Unscented Kalman Filter

Rashid

et al. 2022

Trends Sci

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This paper describes a method in an indoor environment for the estimation and position, using an Unscented Kalman Filter (UKF). The UKF algorithm applied for the position estimation proposing a new measurement uncertainty model that fixes the error covariance according to the distance measurement. In addition, this approach sets the non-diagonal component of the error covariance matrix for the uncertainty of the speed information and the measurement uncertainty to a value other than zero. This method is evaluated through an experiment using a wheel-type mobile robot with an LRF sensor in an indoor environment. In this experiment, we differentiate the estimation execution of the proposed approach with a conventional method that does not employ an adaptive uncertainty model. Moreover, the results improved the estimation performance by setting the non-diagonal component of the error covariance to a value other than zero. The main emphasis of this paper is to implement a practical UKF method for location estimation of a mobile robot and analyze it with better performance.

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“…To further improve the accuracy of the motion information of robots, many filtering approaches exist and continue to be developed in the literature. Many applications are using the unscented Kalman filter (UKF) in various domains nowadays, ranging from target tracking [15] to multi-sensor fusion [16,17]. Another form of sensor fusion research to improve the performance of existing mobile robots is found in [18], where two methods (Dempster-Shafer theory and Kalman filtering) are used to integrate a global positioning system (GPS) and an inertial measurement unit (IMU), and the obtained results allowed for selecting the most accurate method for robot localization at an appropriate cost.…”

Section: Introductionmentioning

confidence: 99%

State Estimation and Localization Based on Sensor Fusion for Autonomous Robots in Indoor Environment

Doumbia

2020

Computers

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Currently, almost all robot state estimation and localization systems are based on the Kalman filter (KF) and its derived methods, in particular the unscented Kalman filter (UKF). When applying the UKF alone, the estimate of the state is not sufficiently precise. In this paper, a new hierarchical infrared navigational algorithm hybridization (HIRNAH) system is developed to provide better state estimation and localization for mobile robots. Two navigation subsystems (inertial navigation system (INS) and, using a novel infrared navigation algorithm (NIRNA), Odom-NIRNA) and an RPLIDAR-A3 scanner cooperation to build HIRNAH. The robot pose (position and orientation) errors are estimated by a system filtering module (SFM) and used to smooth the robot’s final poses. A prototype (two rotary encoders, one smartphone-based robot sensing model and one RPLIDAR-A3 scanner) has been built and mounted on a four-wheeled mobile robot (4-WMR). Simulation results have motivated real-life experiments, and obtained results are compared to some existent research (hardware and control technology navigation (HCTNav), rapid exploring random tree (RRT) and in stand-alone mode (INS)) for performance measurements. The experimental results confirm that HIRNAH presents a more accurate estimation and a lower mean square error (MSE) of the robot’s state than those calculated by the previously cited HCTNav, RRT and INS.

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Estimation and Tracking of a Moving Target by Unmanned Aerial Vehicles

Cited by 14 publications

References 30 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

An Indoor Mobile Robot Localization in perspective of Analysis and Performance using Unscented Kalman Filter

State Estimation and Localization Based on Sensor Fusion for Autonomous Robots in Indoor Environment

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