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UAV Swarm Intelligence: Recent Advances and Future Trends
2020
IEEE Access
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The dynamic uncertain environment and complex tasks determine that the unmanned aerial vehicle (UAV) system is bound to develop towards clustering, autonomy,and intelligence. ...
Then we look inside to investigate the research work by classifying UAV swarm intelligence research into five layers, i.e., decision-making layer, path planning layer, control layer, communication layer ...
[48]
2017
Genetic
Algorithm
An adaptive mission planning system for
heterogeneous unmanned aerial vehicle target
search has been developed. ...
doi:10.1109/access.2020.3028865
fatcat:bvru22a2jregnmzbpehefg7irm
Dynamic Task Planning of Aerial Robotic Platforms for Ground Sensor Data Collection and Processing
[chapter]
2015
Advances in Intelligent Systems and Computing
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In such scenarios, airborne robotic platforms like unmanned aerial vehicles (UAVs) can provide valuable services for data collection, communication relaying and higher level supervision. ...
A gradient scheme is introduced for decision support of the UAV task planning. The results are validated by simulation. ...
It is assumed that both the sensor network and the aerial robotic platform support over-the-air reprogramming in order to dynamically adjust the algorithms parameters and mission objectives. ...
doi:10.1007/978-3-319-21290-6_40
fatcat:6kyaghtwcnctth5oqet5xp4b54
Mission Planning for Unmanned Aerial Vehicles Based on Voronoi Diagram-Tabu Genetic Algorithm
2021
Wireless Communications and Mobile Computing
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We then adopt the tabu genetic algorithm (TGA) to obtain multi-UAV mission planning. ...
Such abilities enable adaptation to more complex and dynamic mission environments. We first examine the mission planning of a single unmanned aerial vehicle. ...
In this paper, we first examine the mission planning of a single unmanned aerial vehicle. ...
doi:10.1155/2021/4154787
fatcat:meyqyzvcejh6bj6t2cssxtwjiy
Survey on Coverage Path Planning with Unmanned Aerial Vehicles
2019
Drones
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This paper aims to explore and analyze the existing studies in the literature related to the different approaches employed in coverage path planning problems, especially those using UAVs. ...
The performance metrics usually applied to evaluate the success of the coverage missions are also presented. ...
[98] proposed a Multi-Objective Path Planning (MOPP) with a Genetic Algorithm for Search and Rescue (SAR) missions using multiple UAVs. The mission is composed by two steps, search and response. ...
doi:10.3390/drones3010004
fatcat:j3nsrywfnjcy3aw3zrug6xzmyu
DISTRIBUTED COMMUNICATION AND CONTROL FOR MULTIAGENT SYSTEMS: MICROINDUSTRIAL VEHICLE ROTORS (MAV)
2018
SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE
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This paper main objective is the use of multi-agent systems to model microunmanned aerial vehicles (MAVs) for a distributed control load. ...
Finally, we review the most relevant work on autonomous motorway planning. ...
Emphasizing the importance of using heterogeneous autonomous systems instead of traditional hierarchical structures, Rathbun and Capozzi [18] have developed an efficient route planning algorithm for ...
doi:10.19062/2247-3173.2018.20.25
fatcat:wf7rw24inrhank7bwmvpnofd3u
An Integrated Mission Planning Framework for Sensor Allocation and Path Planning of Heterogeneous Multi-UAV Systems
2021
Sensors
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We present an integrated mission planning framework based on a two-level adaptive variable neighborhood search algorithm to address the coupled problem. ...
Meanwhile, airborne sensor allocation and path planning are the critical components of heterogeneous multi-UAVs system mission planning problems, which affect the mission profit to a large extent. ...
Meanwhile, a mission planning framework based on a genetic algorithm is developed to solve the problem. Deng et al. ...
doi:10.3390/s21103557
pmid:34065325
fatcat:fmeqygvkcjcvfojeoow6zt6xga
Design and Optimization of a Heterogeneous Platform for multiple UAV use in Precision Agriculture Applications
2014
IFAC Proceedings Volumes
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One way to mitigate this issue is to perform the aforementioned inspections using multiple vehicles cooperating to accomplish the missions (i.e. inspections) goal. ...
planning. ...
By doing this we are able to perform the DSE, using genetic algorithms and compare the results with Lee at al. [Lee 2007 ]. ...
doi:10.3182/20140824-6-za-1003.02261
fatcat:wsg6h4ogu5dtjmw2up6qml4wbq
Genetic Fuzzy based Artificial Intelligence for Unmanned Combat Aerial Vehicle Control in Simulated Air Combat Missions
2016
Journal of Defense Management
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Within this white paper, the authors introduce ALPHA, an Artificial Intelligence that controls flights of Unmanned Combat Aerial Vehicles in aerial combat missions within an extreme-fidelity simulation ...
Breakthroughs in genetic fuzzy systems, most notably the development of the Genetic Fuzzy Tree methodology, have allowed fuzzy logic based Artificial Intelligences to be developed that can be applied to ...
A genetic fuzzy system is a methodology in which a genetic algorithm creates all of the components of the controller [3] . ...
doi:10.4172/2167-0374.1000144
fatcat:75w7yk2lpfe25mxikfcpxdqn5i
Multi-Constraint Optimized Planning of Tasks on Virtualized-Service Pool for Mission-Oriented Swarm Intelligent Systems
2019
Applied Sciences
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On this basis, we mapped this planning problem to an optimization searching problem, and then proposed a Genetic-Algorithm-based mechanism. ...
Experimental results show that this new mechanism is efficient to solve such resource-related and mission-oriented cooperation problems in complicated environments. ...
further, a multi-constraint optimized planning genetic algorithm is designed. ...
doi:10.3390/app9153010
fatcat:37nl7xym25blrjmlexnxc5igia
Final Program
2020
2020 International Conference on Unmanned Aircraft Systems (ICUAS)
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We are certain that all of us will take pleasure in visiting Athens and in travelling through the city's incredible history. We look forward to seeing all of you in Athens. ...
As such, and if the after the COVID-19 'new normal' will allow, from 2021 onwards, the plan is to organize the conference in Europe, again, Canada, South America, South East Asia, and in the United States ...
A target-oriented 3D path planning algorithm, directly using point clouds to compute optimized trajectories for a UAV, is presented in this article. ...
doi:10.1109/icuas48674.2020.9214039
fatcat:7jr6chhfija47kgtwoxqmfmmoe
An aerial/ground robot team for autonomous firefighting in urban GNSS-denied scenarios
2022
Field Robotics
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This paper presents a framework for urban firefighting with a heterogeneous aerial/ground robot team. The system was developed to address Challenge 3 of the MBZIRC'20. ...
scenarios, a Global Planner and a fast local re-planning system for robot navigation, Infrared-Based Perception and robot actuation control for fire extinguishing, and Mission Executive and coordination ...
Unmanned aerial vehicles Regarding to the aerial platforms, the models used for the competition are adaptations of the DJI Matrice 210 V2 (M210) and the DJI Matrice 600 Pro (M600) (see Figure 3 ). ...
doi:10.55417/fr.2022010
fatcat:fcqfne3p3nfdfhb52lqrcrtir4
Towards an Ontology for Autonomous Robots
2012
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
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At the global mission level, the system ontologies must be able to model entities and relationship of multiple autonomous systems. ...
For autonomous systems, the focus is on the cooperation, coordination, and communication of multiple unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and autonomous underwater vehicles ...
, genetic algorithms, or neural networks. ...
doi:10.1109/iros.2012.6386119
dblp:conf/iros/PaullSRABDGHNRGSSSTL12
fatcat:qwcfhlfeurhktfx6g72majlurm
Research Frontier
1971
The Physics Teacher
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Figure 2 : (a) Unmanned aerial vehicle (UAV) path planning in reconnaissance mission; (b) Local heuristics for refinement of UAV path [17] . … … … 1(b) 2(a) 2(b)
III. ...
optimization of low-energy isomers on the landscapes of molecular structures, particularly water clusters (H 2 O) n , and mission path-planning for unmanned aerial vehicle are shown. ...
doi:10.1119/1.2351668
fatcat:lx3tv336hfbnxb4xwpaao6albm
Applications of multi-objective evolutionary algorithms to air operations mission planning
2008
Proceedings of the 2008 GECCO conference companion on Genetic and evolutionary computation - GECCO '08
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, surveillance, and reconnaissance (ISR) asset mission planning, and unmanned aerial systems (UAS) planning. ...
This paper presents several applications of multi-objective evolutionary algorithms for discovering suitable plans in the air operations domain, including dynamic targeting for air strike assets, intelligence ...
One of the more popular algorithms currently used is the Non-dominated Sorting Genetic Algorithm II (NSGA-II) [7] . ...
doi:10.1145/1388969.1388994
dblp:conf/gecco/RosenbergRLTS08
fatcat:clavdji5r5enboa3ekbhefkmke
Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective
[article]
2018
arXiv
pre-print
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Unmanned aerial vehicle (UAV) networks are playing an important role in various areas due to their agility and versatility, which have attracted significant attention from both the academia and industry ...
In [161] , the authors have addressed the task assignment and trajectory planning subproblems concurrently using the genetic algorithm and Voronoi diagram with a centralized approach. ...
(c) Intelligent algorithms Recently, there are many intelligent algorithms used to solve the mission-related decision making problems, such as path planning, task allocations, machine vision and image ...
arXiv:1812.06821v1
fatcat:5b65dzzsunepnl5fzfvxsf442y
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