A Rough Set GA-based Hybrid Method for Robot Path Planning

In this paper, a hybrid method based on rough sets and genetic algorithms, is proposed to improve the speed of robot path planning. Decision rules are obtained using rough set theory. A series of available paths are produced by training obtained minimal decision rules. Path populations are optimised by using genetic algorithms until the best path is obtained. Experiment results show that this hybrid method is capable of improving robot path planning speed.

Research on Evacuation Path Planning Based on Improved Sparrow Search Algorithm

Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years.As part of this effort,an enhanced sparrow search algorithm(MSSA)was proposed.Firstly,the Golden Sine algorithm and a nonlinear weight factor optimization strategy were added in the discoverer position update stage of the SSA algorithm.Secondly,the Cauchy-Gaussian perturbation was applied to the optimal position of the SSA algorithm to improve its ability to jump out of local optima.Finally,the local search mechanism based on the mountain climbing method was incorporated into the local search stage of the SSA algorithm,improving its local search ability.To evaluate the effectiveness of the proposed algorithm,the Whale Algorithm,Gray Wolf Algorithm,Improved Gray Wolf Algorithm,Sparrow Search Algorithm,and MSSA Algorithm were employed to solve various test functions.The accuracy and convergence speed of each algorithm were then compared and analyzed.The results indicate that the MSSA algorithm has superior solving ability and stability compared to other algorithms.To further validate the enhanced algorithm’s capabilities for path planning,evacuation experiments were conducted using different maps featuring various obstacle types.Additionally,a multi-exit evacuation scenario was constructed according to the actual building environment of a teaching building.Both the sparrow search algorithm and MSSA algorithm were employed in the simulation experiment for multiexit evacuation path planning.The findings demonstrate that the MSSA algorithm outperforms the comparison algorithm,showcasing its greater advantages and higher application potential.

Green transformation paths of resource-based cities in China from the configuration perspective

Green transformation is an unavoidable choice for resource-based cities(RBCs)that face resource depletion and environmental pollution.Existing research has focused primarily on specific RBCs,making it challenging to apply green transformation strategies universally across cities.The fuzzy set qualitative comparative analysis(fsQCA)is a combination of qualitative and quantitative analyses that can handle multiple concurrent causality problems and determine how different conditions combine into configurations and generate an outcome.Thus,to address this gap,in this study,we established a research framework for green transformation and utilized the fsQCA to examine the configurations of 113 RBCs in China.By incorporating the element of time,this study explored the dynamic evolution of solutions in 2013,2016,and 2019.The main findings indicate that individual elements do not constitute the necessary conditions for improving the green transformation efficiency(GTE),and the systematic combination of multiple conditions is an effective path for realizing the improvement of the GTE in RBCs.Green transformation paths of RBCs exhibit the same destination through different paths.Additionally,the combination of system environment elements and system structure elements is both complementary and alternative.Differences in RBCs have led to various factor combinations and development paths,but there are some similarities in the key elements of the factor combinations at different stages.Economic environment,government support,and technological innovation are key factors that universally enhance the GTE in RBCs.These insights can assist city managers in formulating policies to drive green transformation and contribute to a better theoretical understanding of green transformation paths in RBCs.

Level Set方法求解机器人路径规划的探讨

移动机器人路径规划是机器人学的一个最基本也是最复杂的问题,路径规划的主要方法有势能方法、单元分解方法、神经网络(NN)等。水平集(level set)方法已经广泛应用于图像处理和计算机图形学领域,因为其具有能够处理拓扑改变、数值稳定性好和独立于参数化的优势。为了探讨Level set方法在求解机器人路径规划中的应用,在介绍水平集法的基本思想和相关技术,以及路径规划的求解方法等的基础上,引入路径规划问题的隐式主动轮廊模型,即水平集模型,并采用快速推进方法(FMM)求解此模型方程,进而给出了路径规划模型的计算结果及其可视化界面,并且与经典势能法的计算结果进行了比较。理论和计算结果证明,Level set方法求解机器人路径规划是可行和有效的,从而为机器人路径规划研究提供了新的思路和方法。

启发式搜索的多智能体异速轨迹规划

在多智能体系统研究中,多智能体路径规划(multi-agent path finding,MAPF)是一个核心难题,其目标是为各个智能体规定独立路径,确保智能体在移动过程中不发生碰撞。这是一个NP难题,亟须高效解决算法。创新性地提出了一种多智能体路径规划算法——启发式导向冲突搜索(heuristic guided conflict-based search,HG-CBS),以解决复杂的MAPF场景,如智能体移动速度不同或各条边的道路长度不同。为优化HG-CBS算法,构建了三种独特的启发式计算方法:(1)加权求和法,以所有启发式的加权总和作为最终启发式;(2)帕累托集合法,构建一个帕累托集并从中选择节点;(3)交替法,在搜索迭代过程中交替使用各种启发式。实验结果显示,相比于传统方法,带有启发值的HG-CBS在成功率、运行时间及扩展节点数量等关键性能指标上均表现更优。例如,在包含16个智能体的复杂场景下,HG-CBS-h3(交替法)将运行时间缩短了89%,将拓展节点的数目减少了95%。此外,随着场景复杂度的提升,HG-CBS-h3的性能优势更加明显。这些结果证明了HG-CBS算法的有效性和高效性,对多智能体轨迹规划问题具有显著的理论和应用价值。

因情施策:如何激活制造企业多元绿色转型路径——基于模糊集定性比较分析方法的研究

服务“双碳”发展目标,探究差异化情境下制造企业多元绿色转型路径日益重要。为明晰制造企业绿色转型驱动因素的多重并发作用机制,以制造企业为研究对象,以跨层次网络嵌入、绿色动态能力等为前因变量,以绿色绩效为结果变量,运用模糊集定性比较分析方法挖掘制造企业绿色转型多组态实现路径。基于研究结果归纳出“主动变革”“能力支撑”和“积极模仿”3类高水平绿色转型路径,以及“有心无力”和“有力无意”两类警示性非高水平绿色转型路径。通过剖析复杂动态网络中制造企业绿色转型各关联因素的交互作用,凸显转型过程与路径的复杂性和主动性,进一步揭示路径差异化构型,为差异化情境下企业绿色转型决策制定、叠代螺旋式阶段路径甄别与匹配提供可行方案。

数智技术驱动新质生产力的生成路径——基于25个案例的fsQCA分析

新质生产力是高质量发展的重要着力点,而数智技术是新质生产力生成的核心要素。根据马克思主义生产力理论,以25个典型案例为研究对象,运用模糊集定性比较分析(fsQCA)方法,探索数智技术驱动新质生产力的生成路径。研究表明:数智技术驱动的新质生产力是劳动者、劳动资料、劳动对象等各要素优化组合的结果,当前存在“创新研究型”“技术研发型”“创新融合型”和“技术转化型”等4条组态路径。根据实证分析结果,提出因企制宜发展新质生产力、加强数智技术研发转化、扩大企业研发经费投入、提升技术型人才培养质量等建议。

研发驱动下中西部企业高质量发展组态分析

为探寻中西部地区研发投入与企业绩效相互促进的组态条件,选取2021年中国沪深股市852家中西部制造业企业,用模糊集定性比较(fsQCA)方法分析研发投入与企业绩效相互促进的条件组态。研究发现,因负债融资需求差异,不同绩效实现路径存在矛盾,企业需依成长潜力选组态路径。高成长潜力企业靠高负债、高政府补助及良好治理环境,助力高研发投入与高成长性循环促进;低成长潜力企业则以低负债率、高政府补助等营造良好治理环境,推动高研发投入与高盈利性、高市值绩效循环促进。最后针对不同成长性企业提出建议,高成长潜力企业可债务融资、增发新股保资金需求;低成长潜力企业可适时增发新股、降负债比率,实现高质量发展。

Density-based rough set model for hesitant node clustering in overlapping community detection

Overlapping community detection in a network is a challenging issue which attracts lots of attention in recent years.A notion of hesitant node（HN） is proposed. An HN contacts with multiple communities while the communications are not strong or even accidental, thus the HN holds an implicit community structure.However, HNs are not rare in the real world network. It is important to identify them because they can be efficient hubs which form the overlapping portions of communities or simple attached nodes to some communities. Current approaches have difficulties in identifying and clustering HNs. A density-based rough set model（DBRSM） is proposed by combining the virtue of densitybased algorithms and rough set models. It incorporates the macro perspective of the community structure of the whole network and the micro perspective of the local information held by HNs, which would facilitate the further ＂growth＂ of HNs in community. We offer a theoretical support for this model from the point of strength of the trust path. The experiments on the real-world and synthetic datasets show the practical significance of analyzing and clustering the HNs based on DBRSM. Besides, the clustering based on DBRSM promotes the modularity optimization.

Design Change Model for Effective Scheduling Change Propagation Paths

Changes in requirements may result in the increasing of product development project cost and lead time, therefore, it is important to understand how require- ment changes propagate in the design of complex product systems and be able to select best options to guide design. Currently, a most approach for design change is lack of take the multi-disciplinary coupling relationships and the number of parameters into account integrally. A new design change model is presented to systematically analyze and search change propagation paths. Firstly, a PDS-Be- havior-Structure-based design change model is established to describe requirement changes causing the design change propagation in behavior and structure domains. Secondly, a multi-disciplinary oriented behavior matrix is utilized to support change propagation analysis of complex product systems, and the interaction relationships of the matrix elements are used to obtain an initial set of change paths. Finally, a rough set-based propagation space reducing tool is developed to assist in narrowing change propagation paths by computing the importance of the design change parameters. The proposed new design change model and its associated tools have been demonstrated by the scheduling change propagation paths of high speed train＇s bogie to show its feasibility and effectiveness. This model is not only supportive to response quickly to diversified market requirements, but also helpful to satisfy customer require- ments and reduce product development lead time. The proposed new design change model can be applied in a wide range of engineering systems design with improved efficiency.

REAIZATION OF THE NEIGHBORING LOGIC SYMMETRY FOR BOOLEAN ORDERED SET

By means of Logic symmetric relation,the single neighboring Logic path for Ndimensions Boolean ordered set is solved.A new method of determining any logic neighboringsubset in limited dimension is proposed.Its results are intuitional and realizable for computer.

An eikonal equation based path planning method using polygon decomposition and curve evolution

Path planning is a key technique of autonomous navigation for robots,and the velocity field is an important part.Constructing velocity field in a complex workspace is still challenging.In this paper,an inner normal guided segmentation algorithm in a complex polygon is proposed to decompose the complex workspace in this paper.The artificial potential field model based on probability theory is then used to calculate the potential field of the decomposed workspace,and the velocity field is obtained by utilizing the potential field of this workspace.Path optimization is implemented by curve evolution,during which the internal force generated in the smoothing process of the initial path by a mean filter and the external force is obtained from the gradient of the workspace potential field.The parameter selection principle is deduced by analyzing the influence of several parameters on the path length and smoothness.Simulation results show that the designed polygon decomposition algorithm can effectively segment complex workspace and that the path optimization algorithm can shorten and smoothen paths.

基于地质和自然环境背景的国家公园生态产品价值实现路径研究

中国设立国家公园是在生物多样保护方面做出的重要举措。如何使国家公园高质量、稳步发展,其中重要的途径是充分发挥国家公园内的生态产品价值,服务社会,促进自然资源保护利用的良性循环,因此对生态产品价值实现路径的研究具有重要意义。本文通过分析第一批5个国家公园(三江源国家公园、大熊猫国家公园、东北虎豹国家公园、武夷山国家公园和海南热带雨林国家公园)地质和自然背景的特征以及自然资源的构成,探讨自然资源与生态产品的密切关系。根据人工的干扰程度,本文把生态产品分为自然生态产品、半自然生态产品和人工生态产品。以陆地水资源为例,分析了影响生态产品价值的主要因素以及自然资源转换为不同的生态产品类型。依据生态产品的特性,提出了其价值实现的三级路径和方式,分析了具有不同地质和自然背景的国家公园的生态产品价值实现路径的差异。

基于fsQCA组态视角的我国医疗资源配置效率提升路径分析

背景当前我国医疗资源配置区域失衡、公平性缺失问题仍然突出。党的“二十大”报告明确提出要“促进优质医疗资源扩容和区域均衡布局”。目的探究提升我国医疗资源配置效率的具体路径,为实现我国医疗资源合理且高效配置、促进基本公共服务均等化提供科学参考。方法于2022年9月—2023年2月开展研究,数据源于《2021中国统计年鉴》《2021中国卫生健康统计年鉴》。将医疗卫生机构数、卫生技术人员数、床位数作为投入指标,以诊疗人次和入院人数为产出指标,通过数据包络分析(DEA)方法对我国2020年31个省份医疗资源配置效率进行测度;以医疗资源配置效率为结果变量,以卫生技术人员占比、每千人口床位数、出院者平均住院日、人均国内生产总值(GDP)、居民可支配收入、财政收入分权和医疗卫生财政预算支出占比为条件变量,运用模糊集定性比较分析(fsQCA)方法从组态视角探究内外部要素对医疗资源配置效率的协同影响机制,剖析高或非高水平医疗资源配置效率的条件组态,明确医疗资源高效率和低效率配置的多重路径。结果2020年我国31个省份医疗资源配置效率整体水平较高,均值为0.852,但省际存在较大差异。组态分析结果可知,医疗资源配置效率的提升是多因素共同作用的结果,共存在3种医疗资源高效率配置的路径。路径1:政府主导型驱动路径,以广西壮族自治区为典型案例。路径2:内外协调型驱动路径,以云南省和甘肃省为代表案例。路径3:均衡型驱动路径,代表案例主要有广东省、福建省和湖北省。非高医疗资源配置效率的路径也存在3条。路径1:政府制约型路径。路径2:经济-政府双重制约型路径,代表案例有黑龙江省和吉林省。路径3:内外制约型路径,典型案例有山西省和西藏自治区。结论内外部各要素及要素间的协同在良性互动过程中共同影响着医疗资源配置效率水平,需优化内外部环境,并对关键资源要素进行有效整合,以形成合力,促进区域医疗资源合理配置。

基于fsQCA方法的高校实验室事故影响因素分析

为探究高校实验室事故与各因素之间的复杂因果关系,该文基于模糊集定性比较分析方法,揭示不同条件组态对实验室事故产生的影响机制。首先,以“人-机-环-管”的安全管理体系为基础,构建针对高校实验室事故影响因素的分析框架,然后,从30起典型事故案例中提取6个条件变量,用于分析事故发生的条件组态。分析结果表明管理条件缺失或仪器设备故障是影响事故发生的核心条件,人的事前行为和事故应急处置通常作为事故发生边缘条件出现。最后,结合各种组态类型所展现出的规律提出了实验室安全管理建议,以期为实验室安全管理提供新视角。

改进势场蚁群算法优化路径自动规划

针对势场与蚁群联合的自主路径规划存在局部最优解、初始路径选择随机导致效率不高及环境适应性差等问题,提出了基于自适应域和参数自适应设置的改进算法。算法首先基于自适应域改进势场目标不可达问题,并过滤震荡点以平滑路径;其次,通过状态转移函数和信息素更新等相关参数的自适应设置,提高算法在收敛效率和搜索能力上的平衡性,进而提高对障碍环境的适应性。实验结果表明,所提算法能够有效避免局部最优、目标不可达和复杂环境的适应性问题,在路径长度和效率上优于实验采用的已有算法,从而验证了算法的有效性。

基于Pareto蚁群算法的双目标路径规划研究

针对复杂建筑环境人员应急疏散单一路径不能满足火灾环境变化需求的问题,基于改进蚁群算法,结合Pareto双目标解集思想,提出一种组合优化解集的双目标蚁群算法,通过排序优化的思想,实现人员多路径动态疏散规划。在构造Pareto解集的阶段协同考虑疏散路径长度以及火灾风险程度2个优化目标,计算各个解之间的支配关系。利用排序优化蚁群算法的正反馈机制将各组解的信息素按一定比例作为最优路径信息素的积累,加快解集的寻找。最后将其与传统双目标蚁群算法相比较,结果表明:优化后的双目标算法更加适合复杂建筑人员疏散路径规划问题,在寻找多组满足要求解的同时展示目标之间的利弊关系,供决策者选择合适的路径,提高疏散效率。

跨学科教育的学科定势现象:反思与破解

创新人才是推动新质生产力形成的主体性力量,而跨学科教育正是培养创新人才的重要渠道。目前我国高校跨学科教育的问题是学科定势思维较为突出,它表现为以学科组织化建设为起点,在制度、认知和资源上都倾向于依赖传统学科建制化的路径来发展跨学科。然而,跨学科的内在属性决定了它与传统的学科组织结构有着完全不同的文化基因与诉求,对组织建设的过于依赖将让这种冲突加剧。结合新质生产力的发展要求,以及国内外的经验,应以跨学科的课程与教学改革为起点,让跨学科教育遍布高等教育的各个阶段以及普通教育到职业教育的各种类型,走出一条跨学科教育的中国式现代化道路。

耕地种植结构演变的影响因素与多元路径——基于30个省份案例区的模糊集定性比较分析

基于模糊集定性比较分析法,分析中国30个省份耕地种植结构演变的影响因素与多元路径。结果表明:(1)耕地“非粮化”或“趋粮化”演变是耕地稀缺性、劳动力外出务工、种粮劳动力成本、农地流转规模、种粮土地成本、农业机械化作业和农业社会化服务7个因素联合作用的结果;(2)存在生产要素驱动型和规模局限型两条“非粮化”路径,以及耕地资源丰裕—种粮规模化和土地成本诱导—种粮规模化两条“趋粮化”子路径;(3)种粮土地成本上涨是“非粮化”的重要诱因,农业现代化生产体系是互补因素,能够缓解种粮土地成本压力对粮食种植的不利影响;(4)劳动力外出务工与农地流转规模、农业机械化作业和农业社会化服务等因素相互关联,对耕地种植结构演变发挥联动影响。

组态视角下中国体育产业效率提升路径研究——基于29个省份的模糊集定性比较分析

提升体育产业效率是实现体育产业高质量发展的必然要求。采用DEA-BCC模型测度2013-2021年29个省份体育产业效率,并以2021年我国体育产业效率值作为结果变量,从市场环境、产业基础、创新驱动、政策支持4个方面选取7个因素作为前因变量,采用模糊集定性比较分析法(fsQCA)进行分析。结果表明:实现体育产业高效率需要充分发挥多条件联动优势。存在3条提升我国体育产业效率的路径,即“市场环境”带动型路径、“产业基础”主导下的双轮驱动型路径、“产业基础+创新驱动+政策支持”多元联动型路径。基于此,提出了强化体育市场主体功能,提升资源配置效率;落实体育区域发展战略,夯实体育产业基础;遵循因地制宜原则,走差异化竞争之路等建议。