Multi-UAV Coverage through Two-Step Auction in Dynamic Environments

Sun, Yihao; Tan, Qinggui; Yan, Chao; Chang, Yuan; Xiang, Xiaojia; Zhou, Han

doi:10.3390/drones6060153

Cited by 8 publications

(4 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Xu et al [20] studied a Complete Coverage Neural Network (CCNN) for an unmanned surface vehicle for complete coverage of a search area and combined it with an improved A* algorithm to escape deadlock situations efficiently. Sun et al [21] proposed a two-step auction method to coordinate multiple UAV to cover a mutual search area, considering the avoidance of obstacles and the energy constraints of the UAV.…”

Section: Uav Flight Path Planning For Sarmentioning

confidence: 99%

Optimal UAV Hangar Locations for Emergency Services Considering Restricted Areas

Braßel¹,

Zeh²,

Fricke³

et al. 2023

Preprint

View full text Add to dashboard Cite

With Unmanned Aerial Vehicles (UAV), a swift response to urgent needs like search \& rescue missions or medical deliveries can be realized. Simultaneously, the legislator is establishing so-called geographical zones, which restrict UAV operations to mitigate the air and ground risk to third parties. These geographical zones serve a particular safety interest, but they may also hinder the efficient usage of UAVs on time-critical missions with a range-limiting battery capacity. In this study, we address a facility location problem for up to two UAV hangars with a robust optimization model considering demand hotspots, geographical zones as restricted areas, a standard mission to satisfy battery capacity constraints, and the impact of wind scenarios. To this end, water rescue missions are used exemplary, for which positive and negative location factors for UAV hangars and areas of increased drowning risk as demand points are derived from open-source georeferenced data. Optimal UAV mission trajectories are computed with an A* algorithm considering five different restriction scenarios. As this pathfinding is very time-consuming, binary occupancy grids and image processing algorithms accelerate the computation by identifying either entirely inaccessible or restriction-free connections beforehand. For the optimal UAV hangar locations, we maximize accessibility while minimizing the service time to the hotspots, resulting in a decrease from the average service time of 570.4 s for all facility candidates to 351.1 s for one and 287.2 s for two optimal UAV hangar locations.

show abstract

Section: Uav Flight Path Planning For Sarmentioning

confidence: 99%

Optimal UAV Hangar Locations for Emergency Services Considering Restricted Areas

Braßel¹,

Zeh²,

Fricke³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…A different obstacle characteristic is considered by [66] while solving the area coverage problem. Not all obstacles have the same threat levels, nor are all of them equal in terms of dimensions and nature.…”

Section: Area Coveragementioning

confidence: 99%

Towards Resilient UAV Swarms—A Breakdown of Resiliency Requirements in UAV Swarms

Phadke

Medrano

2022

Drones

View full text Add to dashboard Cite

UAVs have rapidly become prevalent in applications related to surveillance, military operations, and disaster relief. Their low cost, operational flexibility, and unmanned capabilities make them ideal for accomplishing tasks in areas deemed dangerous for humans to enter. They can also accomplish previous high-cost and labor-intensive tasks, such as land surveying, in a faster and cheaper manner. Researchers studying UAV applications have realized that a swarm of UAVs working collaboratively on tasks can achieve better results. The dynamic work environment of UAVs makes controlling the vehicles a challenge. This is magnified by using multiple agents in a swarm. Resiliency is a broad concept that effectively defines how well a system handles disruptions in its normal functioning. The task of building resilient swarms has been attempted by researchers for the past decade. However, research on current trends shows gaps in swarm designs that make evaluating the resiliency of such swarms less than ideal. The authors believe that a complete well-defined system built from the ground up is the solution. This survey evaluates existing literature on resilient multi-UAV systems and lays down the groundwork for how best to develop a truly resilient system.

show abstract

“…Development of the concept of UAVaaS requires a transition to the more efficient and systematic use of UAVs in smart cities considering many circumstances and tendencies such as [10][11][12][13]:…”

Section: Introduction 1motivationmentioning

confidence: 99%

“…No customers are in the system, and all servers (UAVs) are in operable states S10 One customer is in the system, and all servers (UAVs) are in operable states…”

mentioning

confidence: 99%

UAV Fleet as a Dependable Service for Smart Cities: Model-Based Assessment and Application

et al. 2022

View full text Add to dashboard Cite

The paper suggests a model-based approach to assessment and choice of parameters of unmanned aerial vehicle (UAV) fleets applied as one of the main services for Smart Cities and recommendations to assure their dependability. The principles of building and modeling a UAV Fleet as a Dependable Service (UAVFaaDS) for Smart Cities are formulated. Dependability issues for UAVFaaDS including a taxonomy of UAVF failures caused by equipment faults and attacks on assets were specified. The main results cover methodology, classification of UAVFaaDS models as models of queuing systems, and a set of queueing theory-based models for assessment of UAVFaaDS performance, and availability allowing for analysis and choice of fleet parameters. The efficiency of UAVFaaDS is assessed by the probability of successful delivery of services. The proposed modeling base and algorithms provide a choice of appropriate models for analysis and synthesis of UAVFaaDS, grounding of parameters of UAV fleets considering operation modes, and maintenance policy. The application of the developed models and algorithms during the synthesis of UAVFaaDS allows choosing the appropriate parameters of the fleet and ensuring the dependability of services, as well as service of orders with a probability of 0.9–0.99 depending on the requirements. Two cases of UAVFaaDS application for delivery of medicines in normal and emergence modes, models’ development, and recommendations for their utilization are discussed.

show abstract

Multi-UAV Coverage through Two-Step Auction in Dynamic Environments

Cited by 8 publications

References 28 publications

Optimal UAV Hangar Locations for Emergency Services Considering Restricted Areas

Optimal UAV Hangar Locations for Emergency Services Considering Restricted Areas

Towards Resilient UAV Swarms—A Breakdown of Resiliency Requirements in UAV Swarms

UAV Fleet as a Dependable Service for Smart Cities: Model-Based Assessment and Application

Contact Info

Product

Resources

About