Unmanned aerial vehicle (UAV)-assisted unmanned ground vehicle (UGV) systems design, implementation and optimization

Wei, Yingxin; Qiu, Haonan; Liu, Yuanhao; Du, Jingxin; Pun, Man-On

doi:10.1109/compcomm.2017.8323042

Cited by 8 publications

(6 citation statements)

References 1 publication

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“…In the first stage, F A in Equation ( 32) is optimized under the constraint (33) to obtain the UAVs' paths X uav and task time T uav . Based on the UAVs' minimum energy threshold φ, we calculate C charge in Equation ( 34) from X uav .…”

Section: Discussionmentioning

confidence: 99%

“…Generate X uav and T uav from Equations ( 32) and (33) Obtain C charge from X uav and φ Obtain C * charge from Equation (34) Generate X ugv and T ugv from Equations (35) and (36) Calculate T total based on T uav and T ugv in Equation ( 24)…”

Section: Discussionmentioning

confidence: 99%

“…The UAV detected blind spots inaccessible to the UGVs, obtaining global information to assist the UGVs in obstacle avoidance and low-energy path planning. Wei et al [33] focused on path planning in general outdoor and indoor environments where GPS cannot provide normal localization. The UAV provided aerial visibility to the UGVs, while the UGVs considered obstacle avoidance and optimized energy consumption during path planning.…”

Section: Introductionmentioning

confidence: 99%

“…The UAV provided aerial visibility to the UGVs, while the UGVs considered obstacle avoidance and optimized energy consumption during path planning. Both studies [32,33] involved UAVs providing auxiliary visibility for UGVs in path planning. In contrast, this paper specifically investigates how UGVs assist in enhancing the endurance of UAVs during path planning.…”

Section: Introductionmentioning

confidence: 99%

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A Path Planning Method for Collaborative Coverage Monitoring in Urban Scenarios

Xu,

Zhou,

Liu

et al. 2024

Remote Sensing

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In recent years, unmanned aerial vehicles (UAVs) have become a popular and cost-effective technology in urban scenarios, encompassing applications such as material transportation, aerial photography, remote sensing, and disaster relief. However, the execution of prolonged tasks poses a heightened challenge owing to the constrained endurance of UAVs. This paper proposes a model to accurately represent urban scenarios and an unmanned system. Restricted zones, no-fly zones, and building obstructions to the detection range are introduced to make sure the model is realistic enough. We also introduced an unmanned ground vehicle (UGV) into the model to solve the endurance of the UAVs in this long-time task scenario. The UGV and UAVs constituted a heterogeneous unmanned system to collaboratively solve the path-planning problem in the model. Building upon this model, this paper designs a Three-stage Alternating Optimization Algorithm (TAOA), involving two crucial steps of prediction and rolling optimization. A three-stage scheme is introduced to rolling optimization to effectively address the complex optimization process for the unmanned system. Finally, the TAOA was experimentally validated in both synthetic scenarios and scenarios modeled based on a real-world location to demonstrate their reliability. The experiments conducted in the synthetic scenarios aimed to assess the algorithm under hypothetical conditions, while the experiments in the scenarios based on real-world locations provided a practical evaluation of the proposed methods in more complex and authentic environments. The consistent performance observed across these experiments underscores the robustness and effectiveness of the proposed approaches, supporting their potential applicability in various real-world scenarios.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Path Planning Method for Collaborative Coverage Monitoring in Urban Scenarios

Xu,

Zhou,

Liu

et al. 2024

Remote Sensing

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“…The first ones are ground-moving docking stations. Some of them are Unmanned Ground Vehicles which have landing systems mounted on, like in [65][66][67][68][69][70][71]. The UGV can be used to swap the battery of a UAV, like in [72] or to wirelessly charge the battery- [35,73,74].…”

Section: Mobile Docking Stationmentioning

confidence: 99%

A Decade of UAV Docking Stations: A Brief Overview of Mobile and Fixed Landing Platforms

Grlj

Krznar²,

Pranjic

2022

Drones

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Unmanned Aerial Vehicles have advanced rapidly in the last two decades with the advances in microelectromechanical systems (MEMS) technology. It is crucial, however, to design better power supply technologies. In the last decade, lithium polymer and lithium-ion batteries have mainly been used to power multirotor UAVs. Even though batteries have been improved and are constantly being improved, they provide fairly low energy density, which limits multirotors’ UAV flight endurance. This problem is addressed and is being partially solved by using docking stations which provide an aircraft to land safely, charge (or change) the batteries and to take-off as well as being safely stored. This paper focuses on the work carried out in the last decade. Different docking stations are presented with a focus on their movement abilities. Rapid advances in computer vision systems gave birth to precise landing systems. These algorithms are the main reason that docking stations became a viable solution. The authors concluded that the docking station solution to short ranges is a viable option, and numerous extensive studies have been carried out that offer different solutions, but only some types, mainly fixed stations with storage systems, have been implemented and are being used today. This can be seen from the commercially available list of docking stations at the end of this paper. Nevertheless, it is important to be aware of the technologies being developed and implemented, which can offer solutions to a vast number of different problems.

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