Similar articles

Abstract: We propose multi-objective social learning pigeon-inspired optimization (MSLPIO) and apply it to obstacle avoidance for unmanned aerial vehicle (UAV) formation. In the algorithm, each pigeon learns from the better pigeon but not necessarily the global best one in the update process. A social learning factor is added to the map and compass operator and the landmark operator. In addition, a dimension-dependent parameter setting method is adopted to improve the blindness of parameter setting. We simulate the flight process of five UAVs in a complex obstacle environment. Results verify the effectiveness of the proposed method. MSLPIO has better convergence performance compared with the improved multi-objective pigeon-inspired optimization and the improved non-dominated sorting genetic algorithm.

基于多目标社会学习鸽群优化的多无人机避障控制

[1]Alonso-Mora J, Montijano E, Schwager M, et al., 2016. Distributed multi-robot formation control among obstacles: a geometric and optimization approach with consensus. Proc IEEE Int Conf on Robotics and Automation, p.5356-5363.

[2]Benjamin MR, Defilippo M, Robinette P, et al., 2019. Obstacle avoidance using multiobjective optimization and a dynamic obstacle manager. IEEE J Ocean Eng, 44(2):331-342.

[3]Biro D, Sasaki T, Portugal SJ, 2016. Bringing a time-depth perspective to collective animal behaviour. Trends Ecol Evol, 31(7):550-562.

[4]Cheng R, Jin YC, 2015. A social learning particle swarm optimization algorithm for scalable optimization. Inform Sci, 291:43-60.

[5]Di B, Zhou R, Duan HB, 2015. Potential field based receding horizon motion planning for centrality-aware multiple UAV cooperative surveillance. Aerosp Sci Technol, 46:386-397.

[6]Duan HB, Qiao PX, 2014. Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. Int J Intell Comput Cybern, 7(1):24-37.

[7]Faisal M, Hedjar R, Al Sulaiman M, et al., 2013. Fuzzy logic navigation and obstacle avoidance by a mobile robot in an unknown dynamic environment. Int J Adv Robot Syst, 10(1):37.

[8]Ferri G, Munafò A, LePage KD, 2018. An autonomous underwater vehicle data-driven control strategy for target tracking. IEEE J Ocean Eng, 43(2):323-343.

[9]Floreano D, Wood RJ, 2015. Science, technology and the future of small autonomous drones. Nature, 521(7553):460-466.

[10]Forestiero A, 2017. Bio-inspired algorithm for outliers detection. Multimed Tools Appl, 76(24):25659-25677.

[11]Guo X, Lu JQ, Alsaedi A, et al., 2018. Bipartite consensus for multi-agent systems with antagonistic interactions and communication delays. Phys A, 495:488-497.

[12]Ho HS, Do CS, 2017. Multi-agent based design of autonomous UAVs for both flocking and formation flight. J Adv Korea Navig Technol, 21(6):521-528.

[13]Kownacki C, Ambroziak L, 2017. Local and asymmetrical potential field approach to leader tracking problem in rigid formations of fixed-wing UAVs. Aerosp Sci Technol, 68:465-474.

[14]Luo QN, Duan HB, 2017. Distributed UAV flocking control based on homing pigeon hierarchical strategies. Aerosp Sci Technol, 70:257-264.

[15]Mohanty PK, Parhi DR, 2012. Path generation and obstacle avoidance of an autonomous mobile robot using intelligent hybrid controller. Proc 3^rd Int Conf on Swarm, Evolutionary, and Memetic Computing, p.240-247.

[16]Molinos EJ, Llamazares Á, Ocana M, 2019. Dynamic window based approaches for avoiding obstacles in moving. Robot Auton Syst, 118:112-130.

[17]Paul AK, Shill PC, 2018. New automatic fuzzy relational clustering algorithms using multi-objective NSGA-II. Inform Sci, 448-449:112-133.

[18]Qiu HX, Duan HB, 2017a. Multiple UAV distributed close formation control based on in-flight leadership hierarchies of pigeon flocks. Aerosp Sci Technol, 70:471-486.

[19]Qiu HX, Duan HB, 2017b. Pigeon interaction mode switch-based UAV distributed flocking control under obstacle environments. ISA Trans, 71:93-102.

[20]Qiu HX, Duan HB, 2020. A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles. Inform Sci, 509:515-529.

[21]Sun F, Turkoglu K, 2017. Distributed real-time non-linear receding horizon control methodology for multi-agent consensus problems. Aerosp Sci Technol, 63:82-90.

[22]Wang J, Ahn IS, Lu YF, et al., 2017. A distributed estimation algorithm for collective behaviors in multiagent systems with applications to unicycle agents. Int J Contr Autom Syst, 15(6):2829-2839.

[23]Wu ST, 2013. Flight Control System (2^nd Ed.). Beihang University Press, Beijing, China (in Chinese).

[24]Yan MD, Zhu X, Zhang XX, et al., 2017. Consensus-based three-dimensional multi-UAV formation control strategy with high precision. Front Inform Technol Electron Eng, 18(7):968-977.

[25]Zhang T, Li Q, Zhang CS, et al., 2017. Current trends in the development of intelligent unmanned autonomous systems. Front Inform Technol Electron Eng, 18(1):68-85.

[26]Zhao WW, Chu HR, Zhang MY, et al., 2019. Flocking control of fixed-wing UAVs with cooperative obstacle avoidance capability. IEEE Access, 7:17798-17808.