Path Planning for Multi-UAV Formation

Chen, Yongbo; Yu, Jianqiao; Su, Xiaolong; Luo, Guan-chen

doi:10.1007/s10846-014-0077-y

Cited by 108 publications

(54 citation statements)

References 20 publications

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“…Unlike conventional methods which seek for the optimal result for the whole time period, an on-the-fly strategy is used by the RHC to only minimise the cost function for a relatively short horizon in each time step and compute the according control input, which could largely decrease the computation time. Based on such an online scheme, Chen et al 86 designed a formation hybrid formation path planner by combining the RHC and the APF methods for UAVs. An additional control force generated by APF was added to the system control input to improve the collision avoidance capability of the formation.…”

Section: Optimal Control Methodsmentioning

confidence: 99%

A survey of formation control and motion planning of multiple unmanned vehicles

2018

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SUMMARYThe increasing deployment of multiple unmanned vehicles systems has generated large research interest in recent decades. This paper therefore provides a detailed survey to review a range of techniques related to the operation of multi-vehicle systems in different environmental domains, including land based, aerospace and marine with the specific focuses placed on formation control and cooperative motion planning. Differing from other related papers, this paper pays a special attention to the collision avoidance problem and specifically discusses and reviews those methods that adopt flexible formation shape to achieve collision avoidance for multi-vehicle systems. In the conclusions, some open research areas with suggested technologies have been proposed to facilitate the future research development.

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Section: Optimal Control Methodsmentioning

confidence: 99%

A survey of formation control and motion planning of multiple unmanned vehicles

2018

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“…Concurrently, additional missions can be delivered to decrease the risk of failure due to mobile UAVs or component failures [147]. Multi-UAVs are essentially required to operate in cooperative relationships [148]. The formation flight control of multi-UAVs is a significant direction.…”

Section: ) Swarm Formationmentioning

confidence: 99%

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

Shakeri

Al-Garadi

Badawy

et al. 2019

IEEE Commun. Surv. Tutorials

196

100

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Unmanned Aerial Vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed. CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms. The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere. Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs. Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications. We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications. Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications. We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application. Finally, we summarize potential new directions and ideas that could shape future research in these areas. M 2 respectively. However, as shown in Fig.1, both technologies provide two extremes for surveillance with respect to accuracy versus intrusive deployment and maintenance. SRS provides slow large-scale monitoring, while CSNs require widespread deployment and constant human intervention for setup and maintenance, which may not be feasible particularly in harsh environments. On the one hand, SRS cannot be used for finegrain monitoring, such as monitoring of structural objects, with high accuracy, which limits its usage in many applications that require close monitoring and context detection [5]. On the other hand, traditional sensor networks (SNs) are spreading in countless number of Internet of Things (IoT) applications to provide automatic monitoring of objects, humans, and/or infrastructure. SNs, however, require permanent deployment of large magnitude of localized sensors, intrusively to the environment in many cases, and also require constant human intervention to manage and maintain. The proliferation of lowcost UAVs [6] is emerging to provide innovative ways of low altitude sensing with zero-deployment (i.e., no fixed deployment requirements) that can fundamentally change the way such applications are designed.Similar to surveillance applications, UAV technologies have progressed significantly in several applications in the past decade, making commercial and civil applications feasible and compelling. As it occurs with many disruptive technolo...

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“…In the path following process, the real dynamic model of UAVs can be applied to simulate the real UAV flying state. The quadrotor helicopter is used to follow the planning results which include the position information of the UAV [27]. Obviously, the position information is time-varying.…”

Section: Path Following Processmentioning

confidence: 99%