Research on AGV task path planning based on improved A^(*) algorithm

Background Automatic guided vehicles(AGVs)have developed rapidly in recent years and have been used in several fields,including intelligent transportation,cargo assembly,military testing,and others.A key issue in these applications is path planning.Global path planning results based on known environmental information are used as the ideal path for AGVs combined with local path planning to achieve safe and rapid arrival at the destination.Using the global planning method,the ideal path should meet the requirements of as few turns as possible,a short planning time,and continuous path curvature.Methods We propose a global path-planning method based on an improved A^(*)algorithm.The robustness of the algorithm was verified by simulation experiments in typical multiobstacle and indoor scenarios.To improve the efficiency of the path-finding time,we increase the heuristic information weight of the target location and avoid invalid cost calculations of the obstacle areas in the dynamic programming process.Subsequently,the optimality of the number of turns in the path is ensured based on the turning node backtracking optimization method.Because the final global path needs to satisfy the AGV kinematic constraints and curvature continuity condition,we adopt a curve smoothing scheme and select the optimal result that meets the constraints.Conclusions Simulation results show that the improved algorithm proposed in this study outperforms the traditional method and can help AGVs improve the efficiency of task execution by planning a path with low complexity and smoothness.Additionally,this scheme provides a new solution for global path planning of unmanned vehicles.

Design and Implementation of Bidirectional Dijkstra Algorithm

Bidirectional Dijkstra algorithm whose time complexity is 8O(n~2) is proposed. The theory foundation is that the classical Dijkstra algorithm has not any directional feature during searching the shortest path. The algorithm takes advantage of the adjacent link and the mechanism of bidirectional search, that is, the algorithm processes the positive search from start point to destination point and the negative search from destination point to start point at the same time. Finally, combining with the practical application of route-planning algorithm in embedded real-time vehicle navigation system (ERTVNS), one example of its practical applications is given, analysis in theory and the experimental results show that compared with the Dijkstra algorithm, the new algorithm can reduce time complexity, and guarantee the searching precision, it satisfies the needs of ERTVNS.

基于双向A^(*)-APF算法的船舶路径规划研究

为解决目前船舶路径规划算法中存在的全局非最优解和局部最优解等问题,在双向A^(*)算法的基础上加入了优化函数PathOptimization和IsClear,以去除冗余拐点,缩短全局路径距离;在人工势场法(artificial potential field,APF)的基础上,设定离散化步长函数、斥力感应阈值和临时终点,以避免局部最优解和震荡问题;实现两种算法的融合算法(双向A^(*)-APF算法),在MATLAB模拟的相同栅格图中,对比算法改进前后的模拟试验数据。结果表明,融合算法平均减少了50%的冗余拐点,平均减少了47.5%的算法搜索时间,平均缩短了7%的路径距离,能够同时安全规避动态障碍物和静态障碍物。研究表明,双向A^(*)-APF算法可用于解决船舶路径全局非最优解和局部最优解等问题。

基于双向A^(*)算法的自动导引车全局路径规划研究

针对较大环境下传统A^(*)算法全局路径规划,存在搜索节点多、算法运算时间长、路径曲折等问题,提出了一种改进的双向A^(*)算法,该方法引入人工势场法的引力思想,使得搜索更具方向性;加入转弯代价,让路径更平滑;采用障碍因子,倾向选择宽敞的路径扩展。以路径长度、算法运算时间、搜索节点数、转弯次数的平均值作为评判指标,对比传统A^(*)算法、双向A^(*)算法及未采用障碍因子的改进算法,进行了8次仿真计算。仿真结果表明,改进A^(*)算法比双向A^(*)算法搜索节点少75.9%,转弯次数少53.6%,算法运算时间快84.1%。

Direct P-code acquisition algorithm based on bidirectional overlap technique

According to the signal-to-noise ratio （SNR） loss of average algorithms in direct P-code acquisition method, this paper analyzes the SNR performance of the overlap average algorithm quantitatively, and derives the relationship of SNR loss with overlap shift value and initial average phase difference in the overlap average algorithm. On this basis, the bidirectional overlap average algorithm based on optimal correlation SNR is proposed. The algorithm maintains SNR consistent in the entire initial average phase difference space, and has a better SNR performance than the overlap average algorithm. The effectiveness of the algorithm is verified by both theoretical analysis and simulation results. The SNR performance of the bidirectional overlap average algorithm is 5 dB better than that of the direct average algorithm, and 2 dB better than that of the overlap average algorithm, which provides the support for direct P-code acquisition in low SNR.

Bidirectional Viterbi Decoding Algorithm for OvTDM

Overlapped time domain multiplexing(OvTDM)is an innovative encoding scheme that can obtain high spectral efficiency.However,the intentional inter-symbol interference(ISI)caused by OvTDM will make the decoding process more complex.The computational complexity of maximum likelihood sequence detection increases exponentially with the growth of spectral efficiency in OvTDM.As a consequence of high complexity,the decoding effort for a given spectral efficiency may occasionally exceed the physical limitations of the decoder,leading inevitably to buffer overflows and information erasures.In this paper,we propose a bidirectional Viterbi algorithm(BVA)based on the bidirectional sequence decoding for OvTDM.With the BVA,the decoding operation starts simultaneously from the both ends of the corresponding trellis and stops at the middle of trellis.The simulation results show that compared with Viterbi algorithm(VA),the decoding time of BVA can be reduced by about half.And the memory space of two decoders in BVA are about half of that in VA,which means that the BVA has lower memory requirements for decoder.And the decoding performance of BVA is almost the same as VA.

基于优化双向A^(*)与人工势场法的无人机三维航迹规划

针对传统A^(*)算法在无人机航迹规划中面对复杂环境和高动态任务时存在搜索速度慢、冗余节点多等问题,提出了一种基于优化双向A^(*)算法和人工势场相结合的三维无人机航迹规划方法。通过采用双向搜索机制并设置权重系数优化启发函数,引入自适应步长策略,利用调节因子动态调整双向A^(*)搜索步长,综合考虑全局规划与实时避障需求提出了Bi-A^(*)PF算法。仿真实验表明:与传统的航迹规划方法相比,Bi-A^(*)PF算法不仅能够使无人机在三维环境下高效规划出一条期望航迹,还能有效避开突发威胁。

基于改进双向A^(*)算法的软管线束自动布置方法

为了解决传统A^(*)算法在软管线束路径规划时搜索节点过多、折弯过大,以及难以满足多种布线约束等问题,提出一种基于改进双向A^(*)算法的软管线束自动布置方法。首先,为了满足管线两端的接口方向约束,采用双向搜索策略,动态定义正、反向搜索的目标节点;其次,引入双重权重参数优化评价函数并加入折弯代价,减少管线的拐点和折弯角度;然后,提出基于自适应步长的邻域搜索方法,并在搜索过程中设定父拐点和加入折弯角度限制约束,以提高搜索效率并满足管线最小弯曲半径要求;最后,针对搜索路径拐点较多和路径不平滑问题,提出基于节点过滤和B样条曲线插值的软管光顺方法。实验仿真表明,与传统A^(*)算法相比,所提方法规划出的管线路径拐点和搜索路径节点数都有较大减少,生成的软管路径光顺平滑,满足实际布线约束。

基于双向搜索的改进A^(*)算法路径规划研究

为了提高A^(*)算法的搜索效率、保证路径的最优性,提出了一种基于双向搜索的改进A^(*)算法,以正向、反向搜索的当前节点互为目标点进行双向搜索。首先,引入加权曼哈顿作为距离启发函数,动态调整代价函数的权重比,以保证算法的实时性和路径的优越性;其次,针对双向路径搜索过程中存在的局部路径最优问题,在启发函数中加入偏离最优距离作为代价因素。仿真实验结果表明,该算法的搜索效率更高、遍历节点和路径代价更少,验证了算法的有效性。

BIDIRECTIONAL ASSOCIATIVE MEMORY ENSEMBLE

The multiple classifier system （MCS）, composed of multiple diverse classifiers or feed-forward neural networks, can significantly improve the classification or generalization ability of a single classifier. Enlightened by the fundamental idea of MCS, the ensemble is introduced into the quick learning for bidirectional associative memory （QLBAM） to construct a BAM ensemble, for improving the storage capacity and the error-correction capability without destroying the simple structure of the component BAM. Simulations show that, with an appropriate ＂overproduce and choose＂ strategy or ＂thinning＂ algorithm, the proposed BAM ensemble significantly outperforms the single QLBAM in both storage capacity and noise-tolerance capability.

Evolutionary many objective optimization based on bidirectional decomposition

The decomposition based approach decomposes a multi-objective problem into a series of single objective subproblems, which are optimized along contours towards the ideal point. But non-dominated solutions cannot spread uniformly, since the Pareto front shows different features, such as concave and convex. To improve the distribution uniformity of non-dominated solutions, a bidirectional decomposition based approach that constructs two search directions is proposed to provide a uniform distribution no matter what features problems have. Since two populations along two search directions show differently on diversity and convergence, an adaptive neighborhood selection approach is presented to choose suitable parents for the offspring generation. In order to avoid the problem of the shrinking search region caused by the close distance of the ideal and nadir points, a reference point update approach is presented. The performance of the proposed algorithm is validated with four state-of-the-art algorithms. Experimental results demonstrate the superiority of the proposed algorithm on all considered test problems.

基于双向A^(*)算法的路径规划问题研究

针对我军野外无路网条件下机动作业问题,通过网格法将野外环境进行等效,利用路径规划算法规划最短路径。利用传统的A^(*)算法进行路径规划时,由于算法只从单一方向进行探路,收敛速度较慢,尤其是针对区域面积较大、障碍物环境复杂时运算速度会显著地降低。因此,提出了一种经过改进的双向A^(*)算法,其主要思路为利用起点、终点同时进行寻路,当两侧路径的可到达点出现交叉时即完成寻路。根据仿真结果,验证了双向A^(*)算法能够完成最优路径搜索,相较于传统A^(*)算法,能够有效地提高寻路效率,减少搜索时间。

Bidirectional reflectance one-dimensional rough distribution function modeling of surface in the microwave band

In this study, the bidirectional reflectance distribution function （BRDF） of a one-dimensional conducting rough surface and a dielectric rough surface are calculated with different frequencies and roughness values in the microwave band by using the method of moments, and the relationship between the bistatic scattering coefficient and the BRDF of a rough surface is expressed. From the theory of the parameters of the rough surface BRDF, the parameters of the BRDF are obtained using a genetic algorithm. The BRDF of a rough surface is calculated using the obtained parameter values. Further, the fitting values and theoretical calculations of the BRDF are compared, and the optimization results are in agreement with the theoretical calculation results. Finally, a reference for BRDF modeling of a Gaussian rough surface in the microwave band is provided by the proposed method.

基于改进双向A^(*)算法的消防机器人路径规划

为解决消防机器人在复杂封闭环境内的全局路径规划问题,提出一种基于改进启发函数的同步双向A^(*)算法。首先,将传统A^(*)的单向搜索改为同步双向搜索,动态定义正反向搜索的目标节点;其次,在评价函数中增加“归正”因子函数,防止正反向搜索路径不相交的情况,同时为预估代价函数增加动态权重函数以减少搜索过程中冗余节点的产生;针对搜索路径拐点较多和不平滑的问题,提出一种拐角优化和基于Bezier曲线局部平滑的解决方案;最后通过仿真对比和真实环境试验,验证改进算法在路径长度、搜索时间、遍历节点数和拐点数等方面的优越性以及有效性。

面向自动泊车的改进混合A^(*)最优路径算法研究

为解决混合A^(*)算法在自动泊车中搜索时间慢的问题,提出了融合双向搜索算法和混合A^(*)算法的优化方案。首先利用栅格法优化双向搜索算法,解决了在搜索时正向搜索和反向搜索的子节点不重合的问题;然后针对搜索时间慢的问题,优化混合A^(*)算法的节点搜索过程,融合了双向搜索算法,最后设计仿真垂直入库和侧方位停车两种场景,验证了融合算法能够缩短搜索时间。

基于改进A^(*)算法的无人机路径规划研究

针对传统A^(*)算法在无人机路径规划时效率低下、路径点存在大量冗余,且路径转折较多的缺点,提出一种基于双向机制的改进A^(*)算法。首先引入双向搜索机制,分别以对向搜索的起点作为终点,然后判断终点位于起点的象限进行双定向搜索,从而提高搜索效率。最后引入路径平滑策略,将双定向搜索获取的初始路径进行平滑处理,减少冗余路径点和转折点。通过MATLAB平台对传统A^(*)算法和改进A^(*)算法进行对比实验,实验结果表明,相比于传统A^(*)算法,提出的改进A^(*)算法,路径规划时间平均减少了61.61%,路径点平均减少了83.09%,路径转折点平均减少了46.97%,能够有效提高无人机工作效率,生成平滑路径。

位姿约束下的双向扩展机械臂路径规划方法

针对机械臂路径规划方法存在的规划效率低、连杆通过性差、路径粗糙等问题,以渐进最优快速随机搜索树RRT*为基础,提出一种位姿约束下的双向扩展机械臂路径规划方法(PCO-BT-RRT*)。首先,设计目标偏置引导的双向扩展RRT*算法(BT-RRT*),改进了RRT*算法的初始化过程,将起始点和目标点分别作为两棵随机树的初始节点,并通过目标偏置策略引导其以一定概率相向生长,加快探索未知区域,在保证路径代价较低的同时提升路径生成速度。其次,提出一种位姿约束路径优化策略(PCO),采用机械臂运动学模型和碰撞检测规则共同约束新节点扩展过程,寻找机械臂可达空间内的避障路径;对生成路径剪枝剔冗,缩短可行路径长度,同时对消冗节点以迭代调整的方式进行平滑优化,提高路径生成质量。通过仿真实验分析,验证了所提方法在路径规划问题上的显著性成效;在自主研发的BIM信息融合下建筑砌筑系统进行真机避障测试,验证了该方法的实用性。

基于特征选择及ISSA-CNN-BiGRU的短期风功率预测

针对风电功率随机性大、平稳性低,以及直接输入预测模型往往难以取得较高精度等问题,提出了一种基于特征选择及改进麻雀搜索算法(ISSA)优化卷积神经网络-双向门控循环单元(CNN-BiGRU)的短期风电功率预测方法。首先,利用变分模态分解(VMD)将原始功率分解为一组包含不同信息的子分量,以降低原始功率序列的非平稳性,提升可预测性,同时通过观察中心频率方式确定模态分解数。其次,对每一分量采用随机森林(RF)特征重要度的方法进行特征选择,从风速、风向、温度、空气密度等气象特征因素中,选取对各个分量预测贡献度较高的影响因素组成输入特征向量。然后,建立各分量的CNN-BiGRU预测模型,针对神经网络算法参数难调、手动配置参数随机性大的问题,利用ISSA对模型超参数寻优,自适应搜寻最优参数组合。最后,叠加各分量的预测值,得到最终的预测结果。以中国内蒙古某风电场实际数据进行仿真实验,与多种单一及组合预测方法进行对比,结果表明,本文所提方法相比于其他方法具有更高的预测精度,其平均绝对百分比误差值达到2.644 0%;在其他4个数据集上进行的模型准确性及泛化性验证结果显示,模型平均绝对百分比误差值分别为4.385 3%、3.174 9%、1.576 1%和1.358 8%,均保持在5.000 0%以内,证明本文所提方法具有较好的预测精度及泛化能力。

事件触发改进一致性算法的孤岛混合微电网并联互联变流器分布式协调控制策略

在交直流混合微电网中,并联互联变流器(parallel bidirectional power converters,BPCs)可以实现大容量的功率传输,以满足新型电力系统在空间上的供需匹配。如何在占用更少资源的同时协调控制BPCs实现功率的比例共享,是交直流混合微电网中BPCs控制的研究难点。因此,该文设计了一种针对BPCs的事件触发改进一致性协调控制策略。以归一化下垂控制为基础,提出了改进的比例功率一致性算法,实现BPCs间高精度比例功率共享。在此之上,基于BPCs比例功率误差建立事件触发改进一致性算法,并预设触发函数的预判阈值,从而降低系统在稳定状态下的通信次数。最后进行仿真对比分析,结果表明该文提出的方法相比基本一致性算法通信量减少98.35%;同时,与现有控制策略相比,该文提出的方法有着更好的控制性能。

基于改进金豺算法的短期负荷预测

针对电力负荷序列波动性和预测精度不高的问题,提出一种基于变分模态分解、排列熵和改进金豺算法优化双向长短期记忆网络的预测模型。首先,利用变分模态分解重构原始负荷序列,再采用排列熵理论对分解后的子序列进行熵值重组;然后,利用改进金豺算法对双向长短期记忆网络的参数进行优化,并对每个子序列建立预测模型;最后,组合各模型结果得到最终预测值。实验结果表明,本文模型预测精度更高,与真实值拟合度更好。