With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper pro...With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper proposes an intelligent path planning framework based on grid map modeling.First,an improved Safe and Smooth A*(SSA*)algorithm is employed for global path planning.By incorporating obstacle expansion and cornerpoint optimization,the proposed SSA*enhances the safety and smoothness of the planned path.Then,a Partitioned Dynamic Window Approach(PDWA)is integrated for local planning,which is triggered when dynamic or sudden static obstacles appear,enabling real-time obstacle avoidance and path adjustment.A unified objective function is constructed,considering path length,safety,and smoothness comprehensively.Multiple simulation experiments are conducted on typical port grid maps.The results demonstrate that the improved SSA*significantly reduces the number of expanded nodes and computation time in static environmentswhile generating smoother and safer paths.Meanwhile,the PDWA exhibits strong real-time performance and robustness in dynamic scenarios,achieving shorter paths and lower planning times compared to other graph search algorithms.The proposedmethodmaintains stable performance across maps of different scales and various port scenarios,verifying its practicality and potential for wider application.展开更多
针对变压器故障诊断存在诊断精度不高和麻雀搜索算法(sparrow search algorithm,SSA)存在易陷入局部最优的问题,提出了一种基于融合正余弦和柯西变异的麻雀搜索算法(sine-cosine and Cauchy mutation sparrow search algorithm,SCSSA)...针对变压器故障诊断存在诊断精度不高和麻雀搜索算法(sparrow search algorithm,SSA)存在易陷入局部最优的问题,提出了一种基于融合正余弦和柯西变异的麻雀搜索算法(sine-cosine and Cauchy mutation sparrow search algorithm,SCSSA)优化双向长短期记忆网络(bi-directional long-short term memory,BiLSTM)的变压器故障诊断模型。首先,基于油中溶解气体分析(dissolved gas analysis,DGA)法,以5种特征量作为输入,其次利用正余弦策略和柯西变异策略对麻雀算法进行改进,然后将SCSSA算法、SSA算法和灰狼优化算法(grey wolf optimizer,GWO)在4种测试函数上进行性能对比,验证了SCSSA算法的优越性。最后利用SCSSA算法对BiLSTM网络中的参数进行优化,从而提高BiLSTM网络在变压器故障诊断中的性能。实验结果表明,所提SCSSA-BiLSTM故障诊断模型的综合诊断精度为95.1%,相比于SSA-BiLSTM、GWO-BiLSTM、BiLSTM和LSTM模型分别提高了7.3%、12.2%、14.6%、19.5%,并且SCSSA-BiLSTM模型有着更好的鲁棒性。展开更多
文摘With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper proposes an intelligent path planning framework based on grid map modeling.First,an improved Safe and Smooth A*(SSA*)algorithm is employed for global path planning.By incorporating obstacle expansion and cornerpoint optimization,the proposed SSA*enhances the safety and smoothness of the planned path.Then,a Partitioned Dynamic Window Approach(PDWA)is integrated for local planning,which is triggered when dynamic or sudden static obstacles appear,enabling real-time obstacle avoidance and path adjustment.A unified objective function is constructed,considering path length,safety,and smoothness comprehensively.Multiple simulation experiments are conducted on typical port grid maps.The results demonstrate that the improved SSA*significantly reduces the number of expanded nodes and computation time in static environmentswhile generating smoother and safer paths.Meanwhile,the PDWA exhibits strong real-time performance and robustness in dynamic scenarios,achieving shorter paths and lower planning times compared to other graph search algorithms.The proposedmethodmaintains stable performance across maps of different scales and various port scenarios,verifying its practicality and potential for wider application.
文摘针对变压器故障诊断存在诊断精度不高和麻雀搜索算法(sparrow search algorithm,SSA)存在易陷入局部最优的问题,提出了一种基于融合正余弦和柯西变异的麻雀搜索算法(sine-cosine and Cauchy mutation sparrow search algorithm,SCSSA)优化双向长短期记忆网络(bi-directional long-short term memory,BiLSTM)的变压器故障诊断模型。首先,基于油中溶解气体分析(dissolved gas analysis,DGA)法,以5种特征量作为输入,其次利用正余弦策略和柯西变异策略对麻雀算法进行改进,然后将SCSSA算法、SSA算法和灰狼优化算法(grey wolf optimizer,GWO)在4种测试函数上进行性能对比,验证了SCSSA算法的优越性。最后利用SCSSA算法对BiLSTM网络中的参数进行优化,从而提高BiLSTM网络在变压器故障诊断中的性能。实验结果表明,所提SCSSA-BiLSTM故障诊断模型的综合诊断精度为95.1%,相比于SSA-BiLSTM、GWO-BiLSTM、BiLSTM和LSTM模型分别提高了7.3%、12.2%、14.6%、19.5%,并且SCSSA-BiLSTM模型有着更好的鲁棒性。
