The Collaborative Power Inspection Task Allocation Method of “Unmanned Aerial Vehicle and Operating Vehicle”

Zheng, Hongxing; Wang, Hongxing; Tianpei, Zhang; Yu, Bin

doi:10.1109/access.2021.3074710

Cited by 8 publications

(2 citation statements)

References 34 publications

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“…For the robot used in this study, we chose an LBR Iiwa robot arm, that was securely affixed to a table for its task. An inspection task was selected for the study, representative of tasks commonly found in industrial production [55,56]. More specifically, the selected task required the robot to inspect specific documents (see Fig.…”

Section: Experimental Taskmentioning

confidence: 99%

Mixed Reality Representation of Hazard Zones while Collaborating with a Robot: Sense of Control over Own Safety

Martin,

Kildal,

Lazkano

2023

Preprint

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Safety is the main concern in human-robot collaboration (HRC) in work environments. Standard safety measures based on reducing robot speed affect productivity of collaboration, and do not inform workers adequately about the state of the robot, leading to stressful situations due to uncertainty. To grant the user control over safety, we investigate using audio, visual and audio-visual mixed reality (MR) displays that inform about the boundaries of zones with different levels of hazard. We describe the design of the hazard displays for scenario of collaboration with a real robot. We then report an experimental user study with 24 users, comparing performance and user experience (UX) obtained with the auditory display, the visual display, and the audio-visual display resulting from combining both. Findings suggest that all modalities are suitable for HRC scenarios, warranting similar performance during collaboration. However, distinct qualitative results were observed between displays, indicating differences in the UX obtained.

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Section: Experimental Taskmentioning

confidence: 99%

Mixed Reality Representation of Hazard Zones while Collaborating with a Robot: Sense of Control over Own Safety

Martin,

Kildal,

Lazkano

2023

Preprint

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“…It has the advantages of simplicity, wide applicability, and fast convergence speed. Therefore, it is frequently applied to solve multi-UAV task assignment problems [34][35][36]. The basic process of the k-means clustering algorithm is commonly represented as:…”

Section: Coverage Scanning Clustering Algorithm-based Coverage-path P...mentioning

confidence: 99%

Clustering-Based Multi-Region Coverage-Path Planning of Heterogeneous UAVs

Xiao,

Li,

Xie

et al. 2023

Drones

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Unmanned aerial vehicles (UAVs) multi-area coverage-path planning has a broad range of applications in agricultural mapping and military reconnaissance. Compared to homogeneous UAVs, heterogeneous UAVs have higher application value due to their superior flexibility and efficiency. Nevertheless, variations in performance parameters among heterogeneous UAVs can significantly amplify computational complexity, posing challenges to solving the multi-region coverage path-planning problem. Consequently, this study studies a clustering-based method to tackle the multi-region coverage path-planning problem of heterogeneous UAVs. First, the constraints necessary during the planning process are analyzed, and a planning formula based on an integer linear programming model is established. Subsequently, this problem is decomposed into regional allocation and visiting order optimization subproblems. This study proposes a novel clustering algorithm that utilizes centroid iteration and spatiotemporal similarity to allocate regions and adopts the nearest-to-end policy to optimize the visiting order. Additionally, a distance-based bilateral shortest-selection strategy is proposed to generate region-scanning trajectories, which serve as trajectory references for real flight. Simulation results in this study prove the effective performance of the proposed clustering algorithm and region-scanning strategy.

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A Review of Task Allocation Methods for UAVs

Skaltsis,

Shin,

Tsourdos

2023

J Intell Robot Syst

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Unmanned aerial vehicles, can offer solutions to a lot of problems, making it crucial to research more and improve the task allocation methods used. In this survey, the main approaches used for task allocation in applications involving UAVs are presented as well as the most common applications of UAVs that require the application of task allocation methods. They are followed by the categories of the task allocation algorithms used, with the main focus being on more recent works. Our analysis of these methods focuses primarily on their complexity, optimality, and scalability. Additionally, the communication schemes commonly utilized are presented, as well as the impact of uncertainty on task allocation of UAVs. Finally, these methods are compared based on the aforementioned criteria, suggesting the most promising approaches.

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The Collaborative Power Inspection Task Allocation Method of “Unmanned Aerial Vehicle and Operating Vehicle”

Cited by 8 publications

References 34 publications

Mixed Reality Representation of Hazard Zones while Collaborating with a Robot: Sense of Control over Own Safety

Mixed Reality Representation of Hazard Zones while Collaborating with a Robot: Sense of Control over Own Safety

Clustering-Based Multi-Region Coverage-Path Planning of Heterogeneous UAVs

A Review of Task Allocation Methods for UAVs

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