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 7 publications

(1 citation statement)

References 28 publications

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“…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

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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.

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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

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

Phadke

Medrano

2022

Drones

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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.

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Onboard Distributed Trajectory Planning through Intelligent Search for Multi-UAV Cooperative Flight

Lu¹,

Hu²,

Zheng³

et al. 2022

Drones

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Trajectory planning and obstacle avoidance play essential roles in the cooperative flight of multiple unmanned aerial vehicles (UAVs). In this paper, a unified framework for onboard distributed trajectory planning is proposed, which takes full advantage of intelligent discrete and continuous search algorithms. Firstly, the Monte Carlo tree search (MCTS) is used as the task allocation algorithm to solve the cooperative obstacle avoidance problem. Taking the task allocation decisions as the constraint, knowledge-based particle swarm optimization (Know-PSO) is used as the optimization algorithm to solve the onboard distributed cooperative trajectory planning problem. Simulation results demonstrate that the proposed intelligent MCTS-PSO search framework is effective and flexible for multiple UAVs to conduct the cooperative trajectory planning and obstacle avoidance. Further, it has been applied in practical experiments and achieved promising results.

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

Cited by 7 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

Onboard Distributed Trajectory Planning through Intelligent Search for Multi-UAV Cooperative Flight

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