ORCID Profile
0000-0002-8697-1515
Current Organisation
Deakin University
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: MDPI AG
Date: 05-07-2021
DOI: 10.3390/S21134595
Abstract: Path planning of unmanned aerial vehicles (UAVs) for reconnaissance and look-ahead coverage support for mobile ground vehicles (MGVs) is a challenging task due to many unknowns being imposed by the MGVs’ variable velocity profiles, change in heading, and structural differences between the ground and air environments. Few path planning techniques have been reported in the literature for multirotor UAVs that autonomously follow and support MGVs in reconnaissance missions. These techniques formulate the path planning problem as a tracking problem utilizing gimbal sensors to overcome the coverage and reconnaissance complexities. Despite their lack of considering additional objectives such as reconnaissance coverage and dynamic environments, they retain several drawbacks, including high computational requirements, hardware dependency, and low performance when the MGV has varying velocities. In this study, a novel 3D path planning technique for multirotor UAVs is presented, the enhanced dynamic artificial potential field (ED-APF), where path planning is formulated as both a follow and cover problem with nongimbal sensors. The proposed technique adopts a vertical sinusoidal path for the UAV that adapts relative to the MGV’s position and velocity, guided by the MGV’s heading for reconnaissance and exploration of areas and routes ahead beyond the MGV sensors’ range, thus extending the MGV’s reconnaissance capabilities. The litude and frequency of the sinusoidal path are determined to maximize the required look-ahead visual coverage quality in terms of pixel density and quantity pertaining to the area covered. The ED-APF was tested and validated against the general artificial potential field techniques for various simulation scenarios using Robot Operating System (ROS) and Gazebo-supported PX4-SITL. It demonstrated superior performance and showed its suitability for reconnaissance and look-ahead support to MGVs in dynamic and obstacle-populated environments.
Publisher: SAGE Publications
Date: 05-08-2022
DOI: 10.1177/00187208221116953
Abstract: This research aimed to investigate the relationship between gaze behaviour dynamics and operator performance. In iduals differ in their approach when learning a new task often resulting in performance disparity. During training some in iduals learn the structure and dynamics of the task and develop a systematic approach, whereas others may achieve the same result albeit with increased perceived workload, or indeed some may fail to achieve superior performance levels. Previous research has shown that comparing gaze of experts with novices can provide unique insights into cognitive functioning of superior performers. Twenty-five in iduals participated in a computer-based simulation task. The concept of coefficient of variation (CoV) of task scores was used to compute the participants’ consistency of performance. Based on CoV, the cohort was split into two performance categories. The temporal patterns in participants gaze data were transformed using autocorrelation, and recurrence quantification analysis (RQA) was employed to analyse and quantify the patterns. A Mann–Whitney U analysis demonstrated significantly ( p .01) higher determinism, entropy and laminarity in the superior group compared to the moderate group. Pearson’s correlation revealed a significant ( p .01) negative correlation between the consistency of task performance (CoV) and the RQA measures. The results demonstrated that eye gaze dynamics can be used as an objective measure of performance. Participants classified as superior performers consistently demonstrated a systematic gaze activity which were in line with the task structure. The methods presented here are applicable to observe and evaluate operators’ strategic distribution of gaze. Specifically, for tactical monitoring and decision making in task environments where spatial locations of elements-of-interest vary continuously.
Publisher: MDPI AG
Date: 20-04-2022
Abstract: Path planning of unmanned aerial vehicles (UAVs) is one of the vital components that supports their autonomy and deployment ability in real-world applications. Few path-planning techniques have been thoroughly considered for multirotor UAVs for pursuing ground moving targets (GMTs) with variable speed and direction. Furthermore, most path-planning techniques are generally devised without taking into consideration wind disturbances as a result, they are less suitable for real-world applications as the wind effect usually causes the UAV to drift and tilt from its original course, impacting the mission’s main objective of having an adequate non-deviant camera aim point and steady coverage over the GMT. This paper presents a novel UAV path-planning technique, based on the artificial potential field (APF) for following GMTs in windy environments, to provide steady and continuous coverage over the GMT, by proposing a new modified attractive force to enhance the UAV’s sensitivity to wind speed and direction. The modified wind resistance attractive force function accommodates for any small variation of relative displacement caused by wind leading the UAV to drift in a certain direction. This enables the UAV to maintain its position by tilting (i.e., changing its roll and pitch angles) against the wind to retain the camera aim point on the GMT. The proposed path-planning technique is hardware-independent, does not require an anemometer for measuring wind speed and direction, and can be adopted for all types of multirotor UAVs equipped with basic sensors and an autopilot flight controller. The proposed path-planning technique was evaluated in a Gazebo-supported PX4-SITL and a robot operating system (ROS) for various simulation scenarios. Its performance demonstrated superiority in handling wind disturbances and showed high suitability for deployment in real-world applications.
No related grants have been discovered for Samer Hanoun.