[1] LIU W N, LIU L L, HE G L, et al. UAV inspection path planning based on transmission line technology[J]. Journal of Physics: Conference Series, 2020,1648(4). DOI: 10.1088/1742-6596/1648/4/042083.
[2] 蔡炜,徐圣兵,罗干,等. 输电线路鸟巢识别中的无人机优化巡检研究[J]. 人工智能与机器人研究, 2020,9(2):110-122.
[3] NIU W N, NING B F, ZHOU H. Design of data transmission system of human-autonomous devices for UAV inspection of transmission line status[J]. Journal of Ambient Intelligence and Humanized Computing, 2019. DOI: 10.1007/s12652-019-01504-x.
[4] RAMIREZ-ATENCIA C, RODRIGUEZ-FERNANDEZ V, CAMACHO D. A revision on multi-criteria decision making methods for multi-UAV mission planning support[J]. Expert Systems with Applications, 2020,160. DOI: 10.1016/j.eswa.2020. 113708.
[5] 赵明. 多无人机系统的协同目标分配和航迹规划方法研究[D]. 哈尔滨:哈尔滨工业大学, 2016.
[6] 丁家如. 多无人机任务分配与路径规划算法研究[D]. 杭州:浙江大学, 2016.
[7] BRAUN V, LUPKEN A, FLEGEL S, et al. Active debris removal of multiple priority targets[J]. 〖HJ0.68mm〗Advances in Space Research, 2013,51(9):1638-1648.
[8] ZUIANI F, VASILE M. Preliminary design of debris removal missions by means of simplified models for low-thrust. many-revolution transfers[J]. International Journal of Aerospace Engineering, 2012. DOI: 10.1155/2012/836250.
[9] YU J, CHEN X Q, CHEN L H, et al. Optimal scheduling of GEO debris removing based on hybrid optimal control theory[J]. Acta Astronautica, 2014,93:400-409.
[10]WANG Z, CHEN C L, LI H X, et al. A novel incremental learning scheme for reinforcement learning in dynamic environments[C]// 2016 12th World Congress on Intelligent Control and Automation (WCICA). 2016:2426-2431.
[11]LEE M G, YU K M. Dynamic path planning based on an improved ant colony optimization with genetic algorithm[C]// 2018 IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP). 2018:134-135.
[12]CERF M. Multiple space debris collecting mission: Optimal mission planning[J]. Journal of Optimization Theory and Applications, 2015,167(1):195-218.
[13]LIU Y, YANG J N, WANG Y Z, et al. Multi-objective optimal preliminary planning of multi-debris active removal mission in LEO[J]. Science China Information Sciences, 2017,60(7):203-212.
[14]LIU Y, YANG J N, HU Y H, et al. A multi-objective planning method for multi-debris active removal mission in LEO[C]// AIAA Guidance, Navigation, and Control Conference. 2017. DOI: 10.2514/6.2017-1733.
[15]LIOU J C, SHOOTS D. Orbital debris quarterly news[J]. National Aeronautics and Space Administration, 2014,18(4):1-12.
[16]IZZO D, GETZNER I, HENNES D, et al. Evolving Solutions to TSP variants for active space debris removal[C]// 2015 Annual Conference on Genetic and Evolutionary Computation. 2015:1207-1214.
[17]YANG J N, HU Y H, LIU Y, et al. A maximal-reward preliminary planning for multi-debris active removal mission in LEO with a greedy heuristic method[J]. Acta Astronautica, 2018,149:123-142.
[18]STUART J, HOWELL K, WILSON R. Application of multi-agent coordination methods to the design of space debris mitigation tours[J]. Advances in Space Research, 2016,57(8):1680-1697.
[19]BIANCHI R A C, RIBEIRO C H C, COSTA A H R. On the relation between ant colony optimization and heuristically accelerated reinforcement learning[C]// 1st International Workshop on Hybrid Control of Autonomous System. 2009:49-55.
[20]BUSONIU L, BABUSKA R, DE SCHUTTER B. A comprehensive survey of multiagent reinforcement learning[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2008,38(2):156-172.
[21]周文吉,俞扬. 分层强化学习综述[J]. 智能系统学报, 2017,12(5):590-594.
[22]唐振韬,邵坤,赵冬斌,等. 深度强化学习进展:从AlphaGo到AlphaGo Zero[J]. 控制理论与应用, 2017,34(12):1529-1546.
[23]RASHID T, SAMVELYAN M, DE WITT C S, et al. QMIX:Monotonic value function factorisation for deep multi-agent reinforcement learning[C]// Proceedings of the 35th International Conference on Machine Learning. 2018:4295-4304.
|