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.

Improving path planning efficiency for underwater gravity-aided navigation based on a new depth sorting fast search algorithm

This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.

A path planning method for robot patrol inspection in chemical industrial parks

Safety patrol inspection in chemical industrial parks is a complex multi-objective task with multiple degrees of freedom.Traditional pointer instruments with advantages like high reliability and strong adaptability to harsh environment,are widely applied in such parks.However,they rely on manual readings which have problems like heavy patrol workload,high labor cost,high false positives/negatives and poor timeliness.To address the above problems,this study proposes a path planning method for robot patrol in chemical industrial parks,where a path optimization model based on improved iterated local search and random variable neighborhood descent(ILS-RVND)algorithm is established by integrating the actual requirements of patrol tasks in chemical industrial parks.Further,the effectiveness of the model and algorithm is verified by taking real park data as an example.The results show that compared with GA and ILS-RVND,the improved algorithm reduces quantification cost by about 24%and saves patrol time by about 36%.Apart from shortening the patrol time of robots,optimizing their patrol path and reducing their maintenance loss,the proposed algorithm also avoids the untimely patrol of robots and enhances the safety factor of equipment.

Grid-Based Path Planner Using Multivariant Optimization Algorithm

To solve the shortest path planning problems on grid-based map efficiently,a novel heuristic path planning approach based on an intelligent swarm optimization method called Multivariant Optimization Algorithm( MOA) and a modified indirect encoding scheme are proposed. In MOA,the solution space is iteratively searched through global exploration and local exploitation by intelligent searching individuals,who are named as atoms. MOA is employed to locate the shortest path through iterations of global path planning and local path refinements in the proposed path planning approach. In each iteration,a group of global atoms are employed to perform the global path planning aiming at finding some candidate paths rapidly and then a group of local atoms are allotted to each candidate path for refinement. Further,the traditional indirect encoding scheme is modified to reduce the possibility of constructing an infeasible path from an array. Comparative experiments against two other frequently use intelligent optimization approaches: Genetic Algorithm( GA) and Particle Swarm Optimization( PSO) are conducted on benchmark test problems of varying complexity to evaluate the performance of MOA. The results demonstrate that MOA outperforms GA and PSO in terms of optimality indicated by the length of the located path.

Elite Dung Beetle Optimization Algorithm for Multi-UAV Cooperative Search in Mountainous Environments

This paper aims to address the problem of multi-UAV cooperative search for multiple targets in a mountainous environment,considering the constraints of UAV dynamics and prior environmental information.Firstly,using the target probability distribution map,two strategies of information fusion and information diffusion are employed to solve the problem of environmental information inconsistency caused by different UAVs searching different areas,thereby improving the coordination of UAV groups.Secondly,the task region is decomposed into several high-value sub-regions by using data clustering method.Based on this,a hierarchical search strategy is proposed,which allows precise or rough search in different probability areas by adjusting the altitude of the aircraft,thereby improving the search efficiency.Third,the Elite Dung Beetle Optimization Algorithm(EDBOA)is proposed based on bionics by accurately simulating the social behavior of dung beetles to plan paths that satisfy the UAV dynamics constraints and adapt to the mountainous terrain,where the mountain is considered as an obstacle to be avoided.Finally,the objective function for path optimization is formulated by considering factors such as coverage within the task region,smoothness of the search path,and path length.The effectiveness and superiority of the proposed schemes are verified by the simulation.

Implementation and comparative testing of turn-based algorithm for logit network loading

In order to evaluate the practicality and effectiveness of the turn-based algorithm for logit loading （TALL）, the TALL is implemented using C＋＋, and it is compared with a combination of the network-expanding method and the Dial algorithm based on the analysis of algorithm procedures. The TALL uses the arc-labeling shortest path searching, bidirectional star and the deque structure to directly assign the traffic flow, while the Dial algorithm should be used in an expanded network. The test results over realistic networks of eight cities show the superior performance of the TALL algorithm over the combination of the network-expanding method and the Dial algorithm, and the average processing time is reduced by 55. 4%. Furthermore, it is found that the operational efficiency of the TALL relates to the original densities of the cities. The average processing time is reduced by 65. 1% when the original density is about 14%, but the advantage of the TALL is not obvious with the increase in the original density.

Complete Coverage Path Planning Based on Improved Area Division

It is difficult to solve complete coverage path planning directly in the obstructed area. Therefore, in this paper, we propose a method of complete coverage path planning with improved area division. Firstly, the boustrophedon cell decomposition method is used to partition the map into sub-regions. The complete coverage paths within each sub-region are obtained by the Boustrophedon back-and-forth motions, and the order of traversal of the sub-regions is then described as a generalised traveling salesman problem with pickup and delivery based on the relative positions of the vertices of each sub-region. An adaptive large neighbourhood algorithm is proposed to quickly obtain solution results in traversal order. The effectiveness of the improved algorithm on traversal cost reduction is verified in this paper through multiple sets of experiments. .

复杂城市低空无人机安全风险评估与三维路径规划

针对复杂城市环境内低空无人机飞行安全与效率亟待提升的问题,提出了复杂城市低空无人机安全风险评估与三维路径规划方法。首先,设计了无人机越界冲突率、缓冲空域占比指标,建立了无人机地理围栏安全缓冲间距优化模型,对最佳缓冲间距和栅格粒度进行了标定;然后,构建了由人口密度层、遮蔽层和障碍层构成的无人机风险地图,建立了弹道下降和失控滑行两种模式下的无人机对地风险评估模型,生成了精细化、组合化的城市低空概率风险地图;最后,综合利用地理围栏、概率风险地图和跳点搜索算法,对无人机三维路径进行了初始规划和优化重构。结果表明:弹道下降模式的伤亡风险是失控滑行下降模式的5~75倍;与A*算法相比,跳点搜索算法有效减少了飞行路径的转弯数量,缩短了求解时长,更适合规划无人机飞行路径;与不采用风险地图的方法相比,基于风险地图的无人机路径规划减少了50%的较高风险节点,相应的路径长度仅增加了7.2%和11.4%,整体路径节点的伤亡风险明显降低。研究成果可为复杂城市低空无人机飞行计划制定及安全运行监管提供理论依据和方法支撑。

基于改进蚁群算法的移动机器人路径规划

针对传统蚁群算法在移动机器人路径规划中存在搜索盲目性、收敛速度慢及路径转折点多等问题,提出了一种基于改进蚁群算法的移动机器人路径规划算法。首先,利用跳点搜索(Jump Point Search,JPS)算法不均匀分配初始信息素,降低蚁群前期盲目搜索的概率;然后,引入切比雪夫距离加权因子和转弯代价改进启发函数,提高算法的收敛速度、全局路径寻优能力和搜索路径的平滑程度;最后,提出一种新的信息素更新策略,引入自适应奖惩因子,自适应调整迭代前、后期的信息素奖惩因子,保证了算法全局最优收敛。实验仿真结果表明,在不同地图环境下,与现有文献结果对比,该算法可以有效地缩短路径搜索的迭代次数和最优路径长度,并提高路径的平滑程度。

灌溉机器人全覆盖路径规划方法

灌溉机器人全覆盖行动的各个任务具有较为明显的空间并行性,随着全覆盖范围扩大,在对覆盖区域进行分解阶段,需要充分考虑将整个区域空间分解为哪些区域。但是,灌溉机器人受到视觉感知区域限制,准确匹配和衔接路块间最近端点的难度较大,导致局部路点的连通和线路衔接出现差错,难以有效全覆盖。为了有效解决这一问题,提出一种灌溉机器人全覆盖路径规划方法。通过快速搜索随机算法展开需要覆盖区域的边界检测,考虑视觉传感器的感知范围受限因素,采用灰度质心法展开区域视图边界提取,根据提取结果建立地图。在地图上建立线段序列,通过曼哈顿最小距离原则连接地图上的部分路径线段,形成多个弓形线路块。使用分治算法匹配和衔接各个弓形线路块间最近端点对,引入改进A*算法对全局以及局部路点的连通和线路衔接,实现灌溉机器人的全覆盖路径规划。实验结果表明:针对简单灌溉区域,该方法的路径重复率为0.041%,灌溉覆盖率为98.90%;针对复杂灌溉区域,该方法的路径重复率为0.017%,灌溉覆盖率为99.87%。这说明针对不同的灌溉环境,该方法均可以实现理想的路径规划,不仅可以最大限度地实现全覆盖,并有效地减少路径冗余程度,可以获取理想的灌溉机器人全覆盖路径规划方案。

基于改进DQN算法的应召搜潜无人水面艇路径规划方法

针对应召反潜中无人水面艇航向和航速机动的情形,提出一种基于改进深度Q学习(Deep Q-learning,DQN)算法的无人艇路径规划方法。结合应召搜潜模型,引入改进的深度强化学习(Improved-DQN,I-DQN)算法,通过联合调整无人水面艇(Unmanned Surface Vessel,USV)的动作空间、动作选择策略和奖励等,获取一条最优路径。算法采用时变动态贪婪策略,根据环境和神经网络的学习效果自适应调整USV动作选择,提高全局搜索能力并避免陷入局部最优解;结合USV所处的障碍物环境和当前位置设置分段非线性奖惩函数,保证不避碰的同时提升算法收敛速度;增加贝塞尔算法对路径平滑处理。仿真结果表明,在相同环境下新方法规划效果优于DQN算法、A^(*)算法和人工势场算法,具有更好的稳定性、收敛性和安全性。

自适应搜索距离的改进A*算法研究

为了更好解决全局路径规划中扩展搜索范围大、路径容易发生碰撞的问题,提出一种自适应搜索距离的改进A*算法。首先,在路径扩展搜索时采用8个方向上自适应调整搜索距离机制代替原有固定搜索距离,以减少扩展搜索节点数量,减少搜索时间。然后,在障碍物周围容易发生碰撞的节点处,设置防碰距离函数,使规划路径与障碍物间具有适当安全距离。最后,在Robot Operating System(ROS)中,对自适应搜索距离的改进A*算法进行仿真并进行了实验室环境验证。结果表明:在静态结构化场景下实行全局路径规划,对照传统A*算法,所提算法可以显著提高搜索效率、减少碰撞概率。

非冷链商品配送路径优化研究--以京东配送为例

如何有效降低商品配送成本是物流企业的关注重点,学界已就带时间窗的商品配送路径优化算法展开了相关研究,但相关算法还存在着过早陷入局部最优或无法收敛的问题。由此提出一种改进的变邻域遗传搜索算法(VNS-GA),以非冷链商品配送为研究对象,构造起求解物流配送车辆路径规划的数学模型。首先,以配送成本和缺货惩罚成本的最小化作为实现目标,构建了包括车辆使用成本、配送运输成本和时间窗口惩罚成本的配送路径优化模型;其次,运用变邻域遗传优化算法来实现多目标物流配送路径的优化;最后,以京东某北京配送中心的物流配送为例,运用MATLAB软件对VNS-GA算法模型的科学性及有效性进行仿真验证。经实证,VNS-GA算法与传统算法相比具有更好的全局和局部搜索能力。研究期望为配送车辆调度与配送路径规划提供更优的路径选择模型,从而降低物流配送成本和减少便利店因缺货造成的损失。

基于改进蚁群-麻雀算法的建筑火灾疏散路径规划研究

结合改进蚁群算法(IACO)和改进麻雀搜索算法(ISSA),提出一种考虑火灾实时蔓延的动态疏散路径规划模型。采用火灾动力学软件(FDS)得到火灾环境参数,以表示火灾实时蔓延的危险程度。基于IACO强大的全局搜索能力得到初始疏散路径。采用收敛速度快的ISSA对初始路径进行优化,以提高路径的稳定性。以某综合建筑为例进行2组不同火灾环境下的仿真实验,结果表明:IACO-ISSA模型相比ACO能够根据火灾发展情况实时调整疏散路径,从而有效躲避火灾危险区域,避免了忽略火灾动态蔓延而引导疏散人员至危险区域的现象,进一步提高了疏散路径的安全性。

基于改进蚁群算法的邮船舱室模块移运路径规划

针对大型邮船舱室模块运输过程中存在的移运路线长、路线混乱、舱室模块易与障碍物发生碰撞等问题,提出应用加入动态搜索模型的蚁群算法对邮船舱室模块进行路线规划,为运输舱室模块提供清晰、便捷的移运路线。对主竖区的障碍物进行分析,建立模拟实际工况的栅格地图,采用改进蚁群算法寻找移运路径。对不同位置所经过的栅格地图和蚁群数量进行动态调整。采用模拟退火算法寻找蚁群算法的参数。采用离散点分析确定移运路径的主、支通道。仿真试验结果表明,应用改进蚁群算法建立主、支通道进行舱室模块移运可有效提高舱室模块的运输效率。

基于ISSA和IA^(*)的AGV集成作业调度及其路径规划

针对单一算法在求解车间调度和路径问题时最优性和多样性方面的缺陷,提出了优化飞鼠搜索算法ISSA(improved squirrel search algorithm)和优化A^(*)算法并建立集成作业调度和AGV路径规划的双层模型。首先,采用贪婪策略融合飞鼠搜索算法建立考虑能耗的AGV集成作业调度上层模型;其次,将安全距离因子引入A^(*)算法,构建AGV路径规划下层模型,并通过梯度下降法进行路径平滑;进而,运用6个测试函数和kacem实例验证ISSA的寻优能力,结果表明ISSA的其收敛速度较快,运行效率较高,且不容易陷入局部最优;最后,基于栅格法建模进行对比仿真实验,IA^(*)比A^(*)算法拐点数量降低了22%,同时节约了21%的行驶时间,ISSA和IA^(*)均得到了良好的验证。结果表明,ISSA和IA^(*)能够更有效求解AGV集成作业调度及其路径规划问题。

基于改进A^(*)算法的AGV全局路径规划

采用八邻域搜索策略的传统A^(*)算法对AGV(自动导引车)进行全局路径规划时,存在搜索邻域过多、实时性差和生成路径拐点多等问题,本研究采用三邻域与八邻域混合搜索策略对传统A^(*)算法的搜索策略进行改进,改进后的A^(*)算法在当前搜索点周围不存在障碍物时,选取指向终点的三个栅格作为搜索邻域,当搜索点周围出现障碍物,则转换为传统的八邻域搜索,并在完成搜索后,对搜索路径进行拉直处理,消除多余拐点,减少路径长度。仿真实验结果表明,改进A^(*)搜索算法能有效缩短搜索时间、减少路径拐点数量并缩短路径长度,提高AGV运行效率。

基于六向搜索A^(*)算法的移动机器人路径规划

针对移动机器人利用传统A^(*)算法在复杂环境中进行路径规划时,存在着扩展节点数多导致的搜索效率低,以及路径平滑性不足等问题,提出了一种基于六向搜索的A^(*)算法。首先,在传统A^(*)算法启发函数的基础上利用曼哈顿距离进行加权,减少了算法的搜索时间和扩展节点数;其次,对传统A^(*)算法搜索策略进行改进,提出一种六向搜索策略,进一步减少算法扩展节点数,并同时提升路径平滑性;最后,利用路径平滑策略来对规划出来的路径进行平滑处理。实验结果表明,基于六向搜索的A^(*)算法在不同地图规模的仿真环境中都能获得较高的搜索效率,且扩展节点数更少、转折角度更小、更有利于移动机器人的路径规划。

面向人员岸滩行进的三维路径规划算法研究

针对当前无法为人员岸滩行进提供科学合理的路径规划这一问题,论文基于蚁群算法提出了面向岸滩行进的最优路径规划算法。首先对基本的蚁群算法进行了改良,包括路径搜索方式、信息素更新策略和启发函数的合理设计等,改善了算法的收敛效率;然后定量结合多类岸滩场路径规划影响因子,构建了满足岸滩行进的代价函数;最终实现了面向岸滩行进的算法构建。该算法可为实现复杂地形条件下岸滩行进的最优路径解算和基于蚁群算法的相关三维路径规划分析研究提供参考借鉴。

基于多策略麻雀搜索算法的机器人路径规划

通过多种策略对基本麻雀搜索算法(SSA)进行改进,以解决麻雀搜索算法后期由于种群多样性丢失而导致的全局优化精度和速度问题。首先,改进无限折叠迭代映射(ICMIC)初始化种群,将自适应分段步长因子引入麻雀探测器的位置更新公式中,使麻雀搜索算法观察者的固定比例系数随迭代次数动态变化。然后,将观察者的位置与新公式和正弦余弦算法(SCA)相结合,并干扰先前的观察者步长。最后,在基准测试函数上比较了改进的麻雀搜索算法(ISSA)、麻雀搜索算法(SSA)、鲸鱼算法(WOA)、灰狼算法(GWO)、改进的灰狼算法(CGWO)、正弦余弦算法(SCA)和粒子群优化算法(PSO)的收敛性和准确性,并将其应用于路径规划。实验表明改进的麻雀搜索算法具有良好的优化性能。