Journal Home > Volume 3
CAAI Artificial Intelligence Research 2024, 3: 9150033 https://doi.org/10.26599/AIR.2024.9150033
Article | Open Access | Issue | Published: 08 May 2024

Survey on Collaborative Task Assignment for Heterogeneous UAVs Based on Artificial Intelligence Methods

Show Author's Information Mengzhen Li1,  Na Li2,  Xiaoyu Shao1,  Jiahe Wang1,  Dachuan Xu1( )
Department of Operations Research and Information Engineering, Beijing University of Technology, Beijing 100124, China
Beijing Jinghang Research Institute of Computing and Communication, Beijing 100074, China
Keywords:
heterogeneous unmanned aerial vehicles (UAVs), collaborative task assignment, artificial intelligence methods
Cite this article:
Li M, Li N, Shao X, et al. Survey on Collaborative Task Assignment for Heterogeneous UAVs Based on Artificial Intelligence Methods. CAAI Artificial Intelligence Research, 2024, 3: 9150033. https://doi.org/10.26599/AIR.2024.9150033
Download citation
EndNote(RIS)
BibTeX

303

Views

52

Downloads

Citations

0

Crossref

N/A

WoS

N/A

Scopus

N/A

CSCD

Abstract Full text About this article

Heterogeneous unmanned aerial vehicle (UAV) swarms have garnered significant attention from researchers worldwide due to their remarkable flexibility, diverse mission capabilities, and wide-ranging potential applications. Mission planning stands at the core of UAV swarm operations, requiring consideration of various factors including mission environment, requirements, and inherent characteristics. In this paper, we investigate the model of the cooperative tasking problem in heterogeneous UAV swarms. We provide a comprehensive review of artificial intelligence algorithms applied in UAV swarm mission planning, analyzing their strengths and weaknesses in multi-UAV cooperative environments. By discussing these key techniques and their practical applications, the article highlights future research trends and challenges. This review serves as a valuable reference for understanding the current state of AI algorithm applications in heterogeneous UAV swarm task assignments.


menu
Abstract
Full text
Outline
About this article

Survey on Collaborative Task Assignment for Heterogeneous UAVs Based on Artificial Intelligence Methods

Show Author's information Mengzhen Li1, Na Li2, Xiaoyu Shao1, Jiahe Wang1, Dachuan Xu1( )
Department of Operations Research and Information Engineering, Beijing University of Technology, Beijing 100124, China
Beijing Jinghang Research Institute of Computing and Communication, Beijing 100074, China

Abstract

Heterogeneous unmanned aerial vehicle (UAV) swarms have garnered significant attention from researchers worldwide due to their remarkable flexibility, diverse mission capabilities, and wide-ranging potential applications. Mission planning stands at the core of UAV swarm operations, requiring consideration of various factors including mission environment, requirements, and inherent characteristics. In this paper, we investigate the model of the cooperative tasking problem in heterogeneous UAV swarms. We provide a comprehensive review of artificial intelligence algorithms applied in UAV swarm mission planning, analyzing their strengths and weaknesses in multi-UAV cooperative environments. By discussing these key techniques and their practical applications, the article highlights future research trends and challenges. This review serves as a valuable reference for understanding the current state of AI algorithm applications in heterogeneous UAV swarm task assignments.

Keywords: heterogeneous unmanned aerial vehicles (UAVs), collaborative task assignment, artificial intelligence methods

References(80)

[1]

J. F. Wang, G. W. Jia, J. C. Lin, and Z. X. Hou, Cooperative task allocation for heterogeneous multi-UAV using multi-objective optimization algorithm, J. Cent. South Univ., vol. 27, no. 2, pp. 432–448, 2020.
DOI Google Scholar
[2]
N. Basilico and S. Carpin, Deploying teams of heterogeneous UAVs in cooperative two-level surveillance missions, in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), Hamburg, Germany, 2015, pp. 610–615.
DOI
[3]

Y. Gong, K. Chen, T. Niu, and Y. Liu, Grid-Based coverage path planning with NFZ avoidance for UAV using parallel self-adaptive ant colony optimization algorithm in cloud IoT, J. Cloud Comput., vol. 11, no. 1, p. 29, 2022.
DOI Google Scholar
[4]

A. Cesetti, E. Frontoni, A. Mancini, A. Ascani, P. Zingaretti, and S. Longhi, A visual global positioning system for unmanned aerial vehicles used in photogrammetric applications, J. Intell. Robot. Syst., vol. 61, pp. 157–168, 2011.
DOI Google Scholar
[5]
T. M. Cabreira, P. R. Ferreira, C. Di Franco, and G. C. Buttazzo, Grid-based coverage path planning with minimum energy over irregular-shaped areas with uavs, in Proc. Int. Conf. Unmanned Aircraft Systems (ICUAS), Atlanta, GA, USA, 2019, pp. 758–767.
DOI
[6]

J. Valente, J. Del Cerro, A. Barrientos, and D. Sanz, Aerial coverage optimization in precision agriculture management, Comput. Electron. Agric., vol. 99, no. C, pp. 153–159, 2013.
DOI Google Scholar
[7]
L. C. Batista da Silva, R. M. Bernardo, H. A. de Oliveira, and P. F. F. Rosa, Multi-UAV agent-based coordination for persistent surveillance with dynamic priorities, in Proc. Int. Conf. Military Technologies (ICMT), Brno, Czech Republic, 2017, pp. 765–771.
DOI
[8]

I. Maza, F. Caballero, J. Capitán, J. R. Martínez-de-Dios, and A. Ollero, Experimental results in multi-UAV coordination for disaster management and civil security applications, J. Intell. Rob. Syst., vol. 61, no. 1, pp. 563–585, 2011.
DOI Google Scholar
[9]

J. Song, K. Zhao, and Y. Liu, Survey on mission planning of multiple unmanned aerial vehicles, Aerospace, vol. 10, no. 3, p. 208, 2023.
DOI Google Scholar
[10]

B. Fan, G. Zhao, Y. Bo, and X. Wu, Collaborative ask allocation method for multi-target air-ground heterogeneous unmanned system (in Chinese), Acta Armamentarii, vol. 44, no. 6, pp. 1564–1575, 2023.
Google Scholar
[11]

L. Han, Z. Ren, X. Dong, and Q. Li, Research on cooperative control method and application for multiple unmanned aerial vehicles (in Chinese), Navig. Position. Timing, vol. 5, no. 4, pp. 1–7, 2018.
Google Scholar
[12]

D. Liu, J. Wang, K. Xu, Y. Xu, Y. Yang, Y. Xu, Q. Wu, and A. Anpalagan, Task-driven relay assignment in distributed UAV communication networks, IEEE Trans. Veh. Technol., vol. 68, no. 11, pp. 11003–11017, 2019.
DOI Google Scholar
[13]

Z. Zhou, J. Feng, B. Gu, B. Ai, S. Mumtaz, J. Rodriguez, and M. Guizani, When mobile crowd sensing meets UAV: Energy-efficient task assignment and route planning, IEEE Trans. Commun., vol. 66, no. 11, pp. 5526–5538, 2018.
DOI Google Scholar
[14]

Y. J. Chen, D. K. Chang, and C. Zhang, Autonomous tracking using a swarm of UAVs: A constrained multi-agent reinforcement learning approach, IEEE Trans. Veh. Technol., vol. 69, no. 11, pp. 13702–13717, 2020.
DOI Google Scholar
[15]
A. Whitbrook, Q. Meng, and P. W. H. Chung, A novel distributed scheduling algorithm for time-critical multi-agent systems, in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), Hamburg, Germany, 2015, pp. 6451–6458.
DOI
[16]
J. Turner, Q. Meng, and G. Schaefer, Increasing allocated tasks with a time minimization algorithm for a search and rescue scenario, in Proc. IEEE Int. Conf. Robotics and Automation (ICRA), Seattle, WA, USA, 2015, pp. 3401–3407.
DOI
[17]

A. Gao, Q. Wang, W. Liang, and Z. Ding, Game combined multi-agent reinforcement learning approach for UAV assisted offloading, IEEE Trans. Veh. Technol., vol. 70, no. 12, pp. 12888–12901, 2021.
DOI Google Scholar
[18]

H. Huang and A. Savkin, Navigating UAVs for optimal monitoring of groups of moving pedestrians or vehicles, IEEE Trans. Veh. Technol., vol. 70, no. 4, pp. 3891–3896, 2021.
DOI Google Scholar
[19]

H. Huang, Y. Yang, H. Wang, Z. Ding, H. Sari, and F. Adachi, Deep reinforcement learning for UAV navigation through massive MIMO technique, IEEE Trans. Veh. Technol., vol. 69, no. 1, pp. 1117–1121, 2020.
DOI Google Scholar
[20]

H. Qin, Z. Meng, W. Meng, X. Chen, H. Sun, F. Lin, and M. H. Ang, Autonomous exploration and mapping system using heterogeneous UAVs and UGVs in GPS-denied environments, IEEE Trans. Veh. Technol., vol. 68, no. 2, pp. 1339–1350, 2019.
DOI Google Scholar
[21]

F. Wang, Z. L. Huang, M. C. Han, L. N. Xing, and L. Wang, A knee point based coevolution multi-objective particle swarm optimization algorithm for heterogeneous UAV cooperative multi-task allocation (in Chinese), Acta Autom. Sin., vol. 49, no. 2, pp. 399–414, 2023.
Google Scholar
[22]

N. H. Motlagh, M. Bagaa, and T. Taleb, Energy and delay aware task assignment mechanism for UAV-based IoT platform, IEEE Internet Things J., vol. 6, no. 4, pp. 6523–6536, 2019.
DOI Google Scholar
[23]

W. L. Wu, X. S. Zhou, B. Shen, and Y. Zhao, A review of swarm robotic systems property evaluation research (in Chinese), Acta Autom. Sin., vol. 48, no. 5, pp. 1153–1172, 2022.
Google Scholar
[24]

S. Han, C. Fan, X. Li, X. Luo, and Z. Liu, A modified genetic algorithm for task assignment of heterogeneous unmanned aerial vehicle system, Meas. Contr., vol. 54, nos. 5&6, pp. 994–1014, 2021.
DOI Google Scholar
[25]

L., J. Chen, and N. Wang, Overview of air vehicle mission planning techniques (in Chinese), Acta Aeronaut. Astronaut. Sin., vol. 35, no. 3, pp. 593–606, 2014.
Google Scholar
[26]
A. Tsourdos, B. White, and M. Shanmugavel, Cooperative Path Planning of Unmanned Aerial Vehicles, Chichester, UK: Wiley, 2010.
DOI
[27]

F. Yan, X. Zhu, Z. Zhou, and Y. Tang, Real-time task allocation for a heterogeneous multi-UAV simultaneous attack, Sci. Sin. -Inf., vol. 49, no. 5, pp. 555–569, 2019.
DOI Google Scholar
[28]

G. Jia and J. Wang, Research review of UAV swarm mission planning method (in Chinese), Syst. Eng. Electron., vol. 43, no. 1, pp. 99–111, 2021.
Google Scholar
[29]
T. Shima and S. Rasmussen, UAV Cooperative Decision and Control: Challenges and Practical Approaches, Philadelphia, PA, USA: SIAM, 2008.
DOI
[30]
T. Shima and C. Schumacher, Assignment of cooperating UAVs to simultaneous tasks using genetic algorithms, in Proc. AIAA Guidance, Navigation, and Control Conf. and Exhibit, San Francisco, CA, USA, 2005, pp. 1–14.
DOI
[31]

T. Shima and C. Schumacher, Assigning cooperating UAVs to simultaneous tasks on consecutive targets using genetic algorithms, J. Oper. Res. Soc., vol. 60, no. 7, pp. 973–982, 2009.
DOI Google Scholar
[32]

T. Shima, S. J. Rasmussen, A. G. Sparks, and K. M. Passino, Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms, Comput. Oper. Res., vol. 33, no. 11, pp. 3252–3269, 2006.
DOI Google Scholar
[33]

Q. Deng, J. Yu, and N. Wang, Cooperative task assignment of multiple heterogeneous unmanned aerial vehicles using a modified genetic algorithm with multi-type genes, Chin. J. Aeronaut., vol. 26, no. 5, pp. 1238–1250, 2013.
DOI Google Scholar
[34]

Z. Jia, J. Yu, X. Ai, X. Xu, and D. Yang, Cooperative multiple task assignment problem with stochastic velocities and time windows for heterogeneous unmanned aerial vehicles using genetic algorithm, Aerosp. Sci. Technol., vol. 76, pp. 112–125, 2018.
DOI Google Scholar
[35]

E. Edison and T. Shima, Integrated task assignment and path optimization for cooperating uninhabited aerial vehicles using genetic algorithms, Comput. Oper. Res., vol. 38, no. 1, pp. 340–356, 2011.
DOI Google Scholar
[36]

Y. Chen, D. Yang, and J. Yu, Multi-UAV task assignment with parameter and time-sensitive uncertainties using modified two-part wolf pack search algorithm, IEEE Trans. Aerosp. Electron. Syst., vol. 54, no. 6, pp. 2853–2872, 2018.
DOI Google Scholar
[37]

X. Hu, H. Ma, Q. Ye, and H. Luo, Hierarchical method of task assignment for multiple cooperating UAV teams, J. Syst. Eng. Electron., vol. 26, no. 5, pp. 1000–1009, 2015.
DOI Google Scholar
[38]

E. I. Grøtli and T. A. Johansen, Path planning for UAVs under communication constraints using SPLAT! and MILP, J. Intell. Rob. Syst., vol. 65, no. 1, pp. 265–282, 2012.
DOI Google Scholar
[39]

T. Shima, S. Rasmussen, and D. Gross, Assigning micro UAVs to task Tours in an urban terrain, IEEE Trans. Contr. Syst. Technol., vol. 15, no. 4, pp. 601–612, 2007.
DOI Google Scholar
[40]

S. J. Rasmussen and T. Shima, Tree search algorithm for assigning cooperating UAVs to multiple tasks, Int. J. Robust Nonlinear Contr., vol. 18, no. 2, pp. 135–153, 2008.
DOI Google Scholar
[41]
J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, Cambridge, MA, USA: MIT Press, 1992.
DOI
[42]
K. A. De Jong, An analysis of the behavior of a class of genetic adaptive systems, Tech. Rep. University of Michigan, Ann Arbor, MI, USA, 1975.
[43]
M. Mitchell, An Introduction to Genetic Algorithms, Cambridge, MA, USA: MIT Press 1998.
DOI
[44]
D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Boston, MA, USA: Addison-Wesley Professional, 1989.
[45]

Z. Tian and X. F. Wang, Cooperative multiple task assignment or heterogeneous multi-UAVs with multi-chromosome genetic algorithm (in Chinese), Flight Dyn., vol. 37, no. 1, pp. 39–44, 2019.
Google Scholar
[46]

R. Poli, J. Kennedy, and T. Blackwell, Particle swarm optimization: An overview, Swarm Intell., vol. 1, pp. 33–57, 2007.
DOI Google Scholar
[47]
J. Kennedy and R. Eberhart, Particle swarm optimization, in Proc. Int. Conf. Neural Networks (ICNN’95), Perth, Australia, 1995, pp. 1942–1948.
[48]
J. Zhang, P. Wen, and A. Xiong, Application of improved quantum particle swarm optimization algorithm to multi-task assignment for heterogeneous UAVs, in Proc. 6th Asian Conf. Artificial Intelligence Technology (ACAIT), Changzhou, China, 2022, pp. 1–5.
DOI
[49]

F. Wang, H. Zhang, M. C. Han, and L. N. Xing, Co-evolution based mixed-variable multi-objective particle swarm optimization for UAV cooperative multi-task allocation problem, Chin. J. Comput., vol. 44, no. 10, pp. 1967–1983, 2021.
Google Scholar
[50]
A. Colorni, M. Dorigo, and V. Maniezzo, Distributed optimization by ant colonies, in Proc. 1st European Conf. Artificial Life (ECAL’91), Paris, France, 1991, pp. 134–142.
[51]
T. Stutzle and H. Hoos, MAX-MIN Ant System and local search for the traveling salesman problem, in Proc. 1997 IEEE Int. Conf. Evolutionary Computation (ICEC ’97), Indianapolis, IN, USA, 1997, pp. 309–314.
[52]

Y. Cao, L. Lei, and Y. D. Fang, Application of ant colony algorithm to job-shop scheduling problem, Adv. Mater. Res., vol. 411, pp. 407–410, 2011.
DOI Google Scholar
[53]

S. Gao, J. Wu, and J. Ai, Multi-UAV reconnaissance task allocation for heterogeneous targets using grouping ant colony optimization algorithm, Soft Comput., vol. 25, no. 10, pp. 7155–7167, 2021.
DOI Google Scholar
[54]
V. M. M. O. Ompusunggu, M. K. D. Hardhienata, and K. Priandana, Application of ant colony optimization for the selection of multi-UAV coalition in agriculture, in Proc. Int. Conf. Computer Science and Its Application in Agriculture (ICOSICA), Bogor, Indonesia, 2020, pp. 1–8.
DOI
[55]
Z. Wang, W. Zhang, and G. Li, UAV’s task allocation using multiple colonies of ants, in Proc. 2009 IEEE Int. Conf. Automation and Logistics, Shenyang, China, 2009, pp. 371–374.
[56]

L. Chen, W. L. Liu, and J. Zhong, An efficient multi-objective ant colony optimization for task allocation of heterogeneous unmanned aerial vehicles, J. Comput. Sci., vol. 58, p. 101545, 2022.
DOI Google Scholar
[57]

R. K. Blashfield and M. S. Aldenderfer, The literature on cluster analysis, Multivar. Behav. Res., vol. 13, no. 3, pp. 271–295, 1978.
DOI Google Scholar
[58]

J. Tang, X. Chen, X. Zhu, and F. Zhu, Dynamic reallocation model of multiple unmanned aerial vehicle tasks in emergent adjustment scenarios, IEEE Trans. Aerosp. Electron. Syst., pp. 1–43, 2022.
DOI Google Scholar
[59]

X. Fu, P. Feng, B. Li, and X. Gao, A two-layer task assignment algorithm for UAV swarm based on feature weight clustering, Int. J. Aerosp. Eng., vol. 2019, p. 3504248, 2019.
DOI Google Scholar
[60]

D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, et al., A general reinforcement learning algorithm that Masters chess, shogi, and Go through self-play, Science, vol. 362, no. 6419, pp. 1140–1144, 2018.
DOI Google Scholar
[61]

M. Jaderberg, W. M. Czarnecki, I. Dunning, L. Marris, G. Lever, A. G. Castañeda, C. Beattie, N. C. Rabinowitz, A. S. Morcos, A. Ruderman, et al, Human-level performance in 3D multiplayer games with population-based reinforcement learning, Science, vol. 364, no. 6443, pp. 859–865, 2019.
DOI Google Scholar
[62]

D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, et al, Mastering the game of Go without human knowledge, Nature, vol. 550, no. 7676, pp. 354–359, 2017.
DOI Google Scholar
[63]

V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al, Human-level control through deep reinforcement learning, Nature, vol. 518, no. 7540, pp. 529–533, 2015.
DOI Google Scholar
[64]

X. Li, Z. Lv, S. Wang, Z. Wei, X. Zhang, and L. Wu, A middle game search algorithm applicable to low-cost personal computer for go, IEEE Access, vol. 7, pp. 121719–121727, 2019.
DOI Google Scholar
[65]
V. Oriol, F. Meire, and J. Navdeep, Pointer networks, in Proc. 28th Int. Conf. Neural Information Processing Systems (NIPS’15), Montreal, Canada, 2015, pp. 2692–2700.
[66]
I. Bello, H. Pham, Q. V. Le, M. Norouzi, and S. Bengio, Neural combinatorial optimization with reinforcement learning, arXiv preprint arXiv: 1611.09940, 2016.
[67]
W Kool, H Van Hoof, and M Welling, Attention, learn to solve routing problems! arXiv preprint arXiv: 1803.08475, 2018.
[68]

X. Zhao, Q. Zong, B. Tian, B. Zhang, and M. You, Fast task allocation for heterogeneous unmanned aerial vehicles through reinforcement learning, Aerosp. Sci. Technol., vol. 92, pp. 588–594, 2019.
DOI Google Scholar
[69]

D. Liu, L. Dou, R. Zhang, X. Zhang, and Q. Zong, Multi-agent reinforcement learning-based coordinated dynamic task allocation for heterogenous UAVs, IEEE Trans. Veh. Technol., vol. 72, no. 4, pp. 4372–4383, 2023.
DOI Google Scholar
[70]

L. Yue, R. Yang, J. Zuo, Y. Zhang, Q. Li, and Y. Zhang, Unmanned aerial vehicle swarm cooperative decision-making for SEAD mission: A hierarchical multiagent reinforcement learning approach, IEEE Access, vol. 10, pp. 92177–92191, 2022.
DOI Google Scholar
[71]

P. Zhu and X. Fang, Multi-UAV cooperative task assignment based on half random Q-learning, Symmetry, vol. 13, no. 12, p. 2417, 2021.
DOI Google Scholar
[72]
Z. Wang, Q. Liu, H. Tao, and J. Li, Multiple task planning based on TS algorithm for multiple heterogeneous unmanned aerial vehicles, in Proc. 2014 IEEE Chinese Guidance, Navigation and Control Conf., Yantai, China, 2014, pp. 630–635.
[73]

J. Wang, Y. F. Zhang, L. Geng, J. Y. H. Fuh, and S. H. Teo, A heuristic mission planning algorithm for heterogeneous tasks with heterogeneous UAVs, Un. Sys., vol. 3, no. 3, pp. 205–219, 2015.
DOI Google Scholar
[74]

H. X. Chen, Y. Nan, and Y. Yang, Multi-UAV reconnaissance task assignment for heterogeneous targets based on modified symbiotic organisms search algorithm, Sensors, vol. 19, no. 3, p. 734, 2019.
DOI Google Scholar
[75]

R. Luo, H. Zheng, and J. Guo, Solving the multi-functional heterogeneous UAV cooperative mission planning problem using multi-swarm fruit fly optimization algorithm, Sensors, vol. 20, no. 18, p. 5026, 2020.
DOI Google Scholar
[76]
H. Chen, J. Xu, and C. Wu, Multi-UAV task assignment based on improved Wolf Pack Algorithm, in Proc. 2020 Int. Conf. Cyberspace Innovation of Advanced Technologies, Guangzhou China, 2020, pp. 109–115.
DOI
[77]

W. Liu, X. Zheng, and H. Garg, Multi-UAV cooperative task assignment based on orchard picking algorithm, Int. J. Comput. Intell. Syst., vol. 14, no. 1, p. 1461, 2021.
DOI Google Scholar
[78]

C. Fan, S. Han, X. Li, T. Zhang, and Y. Yuan, A modified nature-inspired meta-heuristic methodology for heterogeneous unmanned aerial vehicle system task assignment problem, Soft Comput., vol. 25, no. 22, pp. 14227–14243, 2021.
DOI Google Scholar
[79]
J. Zhang, P. Wen, and A. Xiong, Multi-task Assignment Research for Heterogeneous UAVs based on Improved Simulated Annealing Particle Swarm Optimization Algorithm, in Proc. Int. Conf. Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), Suzhou, China, 2022, pp. 284–288.
DOI
[80]

Y. Cui, W. Dong, D. Hu, and H. Liu, The application of improved harmony search algorithm to multi-UAV task assignment, Electronics, vol. 11, no. 8, p. 1171, 2022.
DOI Google Scholar
Publication history
Copyright
Rights and permissions

Publication history

Received: 31 October 2023
Revised: 22 January 2024
Accepted: 22 February 2024
Published: 08 May 2024
Issue date: December 2024

Copyright

© The author(s) 2024.

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return