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张海阔 , 孟秀云 . 基于改进RRT * 算法的无人机在线航迹规划 [J ] . 系统工程与电子技术 , 2024 , 46 ( 12 ): 4157 - 4164 . DOI: 10.12305/j.issn.1001-506X.2024.12.24 http://doi.org/10.12305/j.issn.1001-506X.2024.12.24 针对快速扩展随机树(rapidly-exploring random tree, RRT)*算法应用于无人机(unmanned aerial vehicle, UAV)航迹规划时采样效率低、收敛速度慢、航迹代价大的问题, 采用势场法引导树扩展加快算法收敛速度。采用优化算法重选父节点及重新布线，生成比RRT*算法代价更小的初始航迹。基于初始航迹构建启发式采样区域，以更有效地优化初始航迹, 给出一种改进RRT*算法; 基于模型预测控制, 设计航迹规划策略, UAV在飞行中能良好地应对环境中的动态威胁。数学仿真实验结果表明, 改进算法能快速地生成代价更小的初始航迹, 并在后续航迹优化的过程中更有效地减少航迹代价, 可被应用于无人机在线规划任务。

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李宗刚 , 韩森 , 陈引娟 , 等 . 基于角度搜索和深度Q网络的移动机器人路径规划算法 [J ] . 兵工学报 , 2025 , 46 ( 2 ): 240265 . DOI: 10.12382/bgxb.2024.0265 http://doi.org/10.12382/bgxb.2024.0265 针对深度Q网络(Deep Q Network,DQN)算法在求解路径规划问题时存在学习时间长、收敛速度慢的局限性,提出一种角度搜索(Angle Searching,AS)和DQN相结合的算法(Angle Searching-Deep Q Network,AS-DQN),通过规划搜索域,控制移动机器人的搜索方向,减少栅格节点的遍历,提高路径规划的效率。为加强移动机器人之间的协作能力,提出一种物联网信息融合技术(Internet Information Fusion Technology,IIFT)模型,能够将多个分散的局部环境信息整合为全局信息,指导移动机器人规划路径。仿真实验结果表明:与标准DQN算法相比,AS-DQN算法可以缩短移动机器人寻得到达目标点最优路径的时间,将IIFT模型与AS-DQN算法相结合路径规划效率更加显著。实体实验结果表明:AS-DQN算法能够应用于Turtlebot3无人车,并成功找到起点至目标点的最优路径。

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陈亚萍 , 王楠 , 洪华杰 , 等 . 面向多无人平台区域监视任务的信息素正向激励栅格方法 [J ] . 兵工学报 , 2023 , 44 ( 9 ): 2859 - 2870 . DOI: 10.12382/bgxb.2022.0537 http://doi.org/10.12382/bgxb.2022.0537 密集城市地区作战普遍存在区域监视问题,为使我方撤出最高危险区域以减轻损伤或减少人力消耗,采用无人系统执行侦察监视任务极具军事意义和应用价值。面向环境复杂多变且多无人平台初始位置邻近情况下的协同监控任务,针对现有的控制策略在遍历目标空间时多无人平台容易发生冲突的不足且缺少对多无人平台初始位置邻近情况的研究,在半启发式控制策略和栅格法的基础上通过引入信息素改进目标函数并制定冲突消解规则,构建出信息素正向激励栅格法。试验结果表明,信息素正向激励栅格法在冲突消解方面的表现优于现有控制策略,综合性能表现较好,特别在障碍物数量较多时全局平均空闲时间的表现更好,所提方法的有效性和合理性得到了验证。

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薛阳 , 燕宇铖 , 贾巍 , 等 . 基于改进灰狼算法优化长短期记忆网络的光伏功率预测 [J ] . 太阳能学报 , 2023 , 44 ( 7 ): 207 - 213 . DOI: 10.19912/j.0254-0096.tynxb.2022-0320 http://doi.org/10.19912/j.0254-0096.tynxb.2022-0320 为提高光伏发电功率预测的准确性,提出一种基于改进自适应因子与精英反向学习策略的改进灰狼算法（IGWO）,用以优化长短期记忆网络（LSTM）预测模型。利用IGWO优化LSTM全连接层参数,建立IGWO-LSTM组合模型预测光伏功率,具有较好的收敛速度与求解效率,也可有效避免局部最优解。最后基于常州某光伏发电站实时数据进行仿真,实验结果表明IGWO-LSTM相对于LSTM光伏功率预测更具准确性。

XUE Y , YAN Y C , JIA W , et al. Photovoltaic power prediction model based on IGWO-LSTM [J ] . Acta Energiae Solaris Sinica , 2023 , 44 ( 7 ): 207 - 213 . (in Chinese) DOI: 10.19912/j.0254-0096.tynxb.2022-0320 http://doi.org/10.19912/j.0254-0096.tynxb.2022-0320 Improving the accuracy of PV power prediction is important for improving the operational efficiency of PV power plants and ensuring the safety and stability of grid-connected PV operation. Therefore, an improved gray wolf algorithm （IGWO） based on improved adaptive factor and elite backward learning strategy is proposed to optimize the long short-term memory network （LSTM） prediction model. The IGWO is used to optimize the LSTM fully connected layer parameters and build a combined IGWO-LSTM model to predict PV power, which has better convergence speed and solution efficiency, and also can effectively avoid local optimal solutions. Finally, based on the simulation of real-time data from a PV power station in Changzhou, the experimental results show that the IGWO-LSTM has more accuracy than the LSTM PV power prediction.