Multi‐<scp>UAV</scp>Cooperation

Maza, Iván; Capitán, Jesús; Merino, Luís; Ollero, A.

doi:10.1002/9780470686652.eae1130

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…For the specific case of multi-drone cooperation, several architectures have been proposed [18]. Relevant research has been performed so far to advance the applications of multidrone systems, especially in regard to the analysis of drone communication aspects [19], [20], [21].…”

Section: Related Workmentioning

confidence: 99%

Learning-in-Games Approach for the Mission Planning of Autonomous Multi-Drone Spatio-Temporal Sensing

Castrillo,

Pascarella,

Pigliasco

et al. 2024

IEEE Access

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Mission planners are one of the major classes of autonomy software and their design is especially challenging in the case cooperation autonomy is required for unmanned multi-vehicle systems. A clear example of this is given by the applications of teams of drones, such as multi-drone spatio-temporal sensing. Here, drone teams act as mobile and cooperative sensor networks to simultaneously collect sensor data in areas of interest and to allow detailed computation on the sensed data. For the design of cooperative and autonomous drone teams, mission planning shall be accomplished in the form of coordinated sensing to optimally assign the different sensing tasks and routes to each drone, employing task allocation and route planning as the basic pillars to maximize the multi-drone mission effectiveness. This work proposes a dynamic and decentralized mission planner for a drone team performing autonomous and cooperative spatiotemporal sensing. The design exploits the learning-in-games framework for the processing of optimal routes in reasonable time frames. Two ad-hoc variants of the binary log-linear learning are proposed as a coordination algorithm to manage both task allocation and route planning, by demonstrating reachability and reversibility properties. Also, the work describes an experimental analysis of the proposed solutions by means of model-in-the-loop simulations, in order to provide a preliminary tune of the main learning parameters for both solutions.INDEX TERMS Multi-drone systems, multi-drone sensing, learning in games, multi-agent systems.

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Section: Related Workmentioning

confidence: 99%

Learning-in-Games Approach for the Mission Planning of Autonomous Multi-Drone Spatio-Temporal Sensing

Castrillo,

Pascarella,

Pigliasco

et al. 2024

IEEE Access

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“…where d iC and d kC indicate the distance between the crossing point and the current position of i and k, respectively. If T i C < T k C , at its current speed i will reach x i,k C , y i,k C before k. In this case, a lower bound is defined on v i , so to 12 Solutions obtained for either r < 0 or s < 0 would correspond to cases where the future trajectory of one node crosses the past trajectory of the other. These cases are not considered by the Collision Avoidance rule, since an actual collision is impossible even if the nodes were to be at distance lower than d CA min at crossing time.…”

Section: B Speed Bounds Identificationmentioning

confidence: 99%

“…• search and rescue in response to emergency calls or disasters. Best practice rules require operators to work in groups of at least two individuals who keep visual or voice contact with one another [10]; • tactical and security teams, with on-demand formation, merging, and splitting of groups [11]; • swarms of Unmanned Aerial Vehicles (UAVs) flying in variable formation [12], [13]; • cooperative communications in cognitive networks [14]. Note that although distances between devices in a same group may be small (i.e.…”

Section: Introductionmentioning

confidence: 99%

Mo³: A Modular Mobility Model for Future Generation Mobile Wireless Networks

Nardis

Benedetto²

2022

IEEE Access

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Mobility modeling in 5G and beyond 5G must address typical features such as time-varying correlation between mobility patterns of different nodes, and their variation ranging from macro-mobility (kilometer range) to micro-mobility (sub-meter range). Current models have strong limitations in doing so: the widely used reference-based models, such as the Reference Point Group Mobility (RPGM), lack flexibility and accuracy, while the more sophisticated rule-based (i.e. behavioral) models are complex to set-up and tune. This paper introduces a new rule-based Modular Mobility Model, named Mo 3 , that provides accuracy and flexibility on par with behavioral models, while preserving the intuitiveness of the reference-based approach, and is based on five rules: 1) Individual Mobility, 2) Correlated Mobility, 3) Collision Avoidance, 4) Obstacle Avoidance and 5) Upper Bounds Enforcement. Mo 3 avoids introducing acceleration vectors to define rules, as behavioral models do, and this significantly reduces complexity. Rules are mapped one-to-one onto five modules, that can be independently enabled or replaced. Comparison of time-correlation features obtained with Mo 3 vs. reference-based models, and in particular RPGM, in pure micro-mobility and mixed macro-mobility / micro-mobility scenarios, shows that Mo 3 and RPGM generate mobility patterns with similar topological properties (intra-group and inter-group distances), but that Mo 3 preserves a spatial correlation that is lost in RPGM -at no price in terms of complexity -making it suitable for adoption in 5G and beyond 5G.

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“…Recently, CPP algorithms have been developed, considering the parameters required for more efficient data retrieval from remote sensing sensors [ 12 , 13 ]. In addition, algorithms have been developed that use multi-UAV to cover the area, thus reducing the coverage time of the area of interest [ 14 , 15 ]. The way to cover an area with autonomous robots differs depending on the algorithm [ 2 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Coverage Path Planning Methods Focusing on Energy Efficient and Cooperative Strategies for Unmanned Aerial Vehicles

Fevgas

Λάγκας

Argyriou

et al. 2022

Sensors

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The coverage path planning (CPP) algorithms aim to cover the total area of interest with minimum overlapping. The goal of the CPP algorithms is to minimize the total covering path and execution time. Significant research has been done in robotics, particularly for multi-unmanned unmanned aerial vehicles (UAVs) cooperation and energy efficiency in CPP problems. This paper presents a review of the early-stage CPP methods in the robotics field. Furthermore, we discuss multi-UAV CPP strategies and focus on energy-saving CPP algorithms. Likewise, we aim to present a comparison between energy efficient CPP algorithms and directions for future research.

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Multi‐UAVCooperation

Cited by 7 publications

References 23 publications

Learning-in-Games Approach for the Mission Planning of Autonomous Multi-Drone Spatio-Temporal Sensing

Learning-in-Games Approach for the Mission Planning of Autonomous Multi-Drone Spatio-Temporal Sensing

Mo³: A Modular Mobility Model for Future Generation Mobile Wireless Networks

Coverage Path Planning Methods Focusing on Energy Efficient and Cooperative Strategies for Unmanned Aerial Vehicles

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Multi‐UAVCooperation

Cited by 7 publications

References 23 publications

Learning-in-Games Approach for the Mission Planning of Autonomous Multi-Drone Spatio-Temporal Sensing

Learning-in-Games Approach for the Mission Planning of Autonomous Multi-Drone Spatio-Temporal Sensing

Mo3: A Modular Mobility Model for Future Generation Mobile Wireless Networks

Coverage Path Planning Methods Focusing on Energy Efficient and Cooperative Strategies for Unmanned Aerial Vehicles

Contact Info

Product

Resources

About

Mo³: A Modular Mobility Model for Future Generation Mobile Wireless Networks