基于改进Informed-RRT^(*)的机械臂抓取运动规划

为提高工业机械臂对目标物体抓取及对障碍物躲避的效率和成功率,提出一种基于改进抓取信息引导的快速随机树星(GI-RRT^(*))的机械臂路径规划算法.首先,预先设定最大迭代次数和自适应函数,缩短机械臂运动轨迹生成时间,增强采样导向性和质量;其次,基于椭圆形子集直接采样,对采样点位置进行约束,提高采样效率;最后,采用贪心算法删除机械臂运动轨迹的冗余点,并使用三次B样条曲线平滑约束机械臂运动轨迹,提高机械臂运动轨迹的柔顺性.利用生成残差卷积神经网络模型预测,输入深度相机采集的彩色图像和深度图像,输出视场中物体的适当映射抓取位姿.为验证机械臂的抓取效果,选择三指气动柔性夹爪,设计柔性抓取模块,并结合法奥(FR3)协作机械臂构建自主抓取系统,进行二维地图仿真和机械臂样机实验.结果表明,与传统的信息引导的快速随机树星算法相比,GI-RRT^(*)算法运动轨迹长度缩短10.11%,轨迹生成时间缩短62.68%.同时,算法具有较强的鲁棒性.机械臂能独立地避开障碍物、抓取目标物体,满足其自主抓取的需求.

目标区域引导的RRT^(*)机械臂路径规划算法

针对传统RRT^(*)算法在机械臂路径规划的过程中存在规划效率低、路径质量不佳、机械臂位姿不当等问题,提出一种目标区域引导的RRT^(*)机械臂路径规划算法(TA-RRT^(*))。在传统RRT^(*)算法基础上,引入目标偏向策略并使用球形子集约束采样,缩小采样范围并使新节点朝向目标点扩展,增强目标导向性;对新节点采用直连策略,让算法可以更快地收敛从而提升路径生成速度。对初始规划路径去除冗余点并使用三次B样条曲线转换成平滑路径,优化了路径质量。对机械臂进行位姿约束,通过机械臂逆运动学判断机械臂连杆位姿可达性,并利用包络盒模型判断机械臂是否与障碍物碰撞。实验结果表明,在二维以及三维场景下,TA-RRT^(*)算法在采样次数、规划时间、路径长度以及平滑度等方面的性能均优于RRT^(*)算法,验证了该方法的正确性及可行性。机械臂仿真实验以及在真实环境下的测试结果显示,加入位姿约束后机械臂运行规划好的轨迹时,机械臂各个关节在运行规划路径的过程中并未与障碍物发生碰撞且具有良好的稳定性。

改进RRT^(*)-APF-DP融合算法的机械臂路径规划

针对基本的快速拓展随机树算法(rapidly-exploring random tree,RRT^(*))存在搜索随机性大、效率低、路径非最优的缺点,提出一种引入人工势场法算法(artificial potential field method,APF)和Douglas-Peucker算法的改进RRT^(*)-APF-DP路径规划算法.在RRT*算法的采样点生成阶段引入变采样范围偏置搜索与步长自适应调整策略,融合重新设计的APF算法的引力与斥力函数,增强路径扩展导向性与绕过障碍物能力.采用重采样策略改进DP算法,优化避障代价与控制点数量.实验结果表明,本算法规划的避障路径满足机械臂的运动要求,且算法规划的避障路径代价、规划时间和路径控制节点数均得到有效改善.

基于改进Informed-RRT^(*)算法的舰载机甲板平面路径规划

针对舰载机甲板路径规划问题,在Informed-RRT^(*)(informed rapidly-exploring random tree)的椭圆采样基础上,提出使用正态分布方式采样的IN-RRT^(*)(informed normal-RRT^(*))算法。首先,针对舰载机与运动场景建模,定义舰载机运动约束和避障策略;其次,将正态分布采样策略与椭圆采样相结合,获取优质高效采样点;引入人工势场法,自适应调节随机树的搜索步长值;使用向心Catmull-Rom样条插值法对路径进行平滑优化处理;提出针对动态障碍改进的动态窗口法,实现局部动态避障。最后,运用甲板平面环境实验检验算法性能。结果表明,IN-RRT^(*)算法能显著优化搜索时间和搜索路径质量,可应对动态场景规划出合理可行的平滑路径。

基于改进APF-Informed-RRT^(*)的机械臂避障路径规划

针对Informed-RRT^(*)算法在避障路径规划中缺乏目的性和方向性,存在规划时间长、迭代效率低等问题,提出了结合人工势场法和Informed-RRT^(*)算法的避障规划算法。首先,针对传统人工势场法存在目标点不可达、易与障碍物碰撞的问题,提出了改进后的人工势场法,并将其融入Informed-RRT^(*)算法中,使随机树沿势场下降的方向生长,增强其方向性;其次,依据随机树与障碍物间的距离,提出了一种自适应生长步长策略,提高了对空间的探索能力;最后,引入贪心算法的思想,在生长时直接判断随机树能否直达目标点,提高了路径规划效率。在二维和三维环境下对改进后的算法与传统算法及其衍生算法进行对比实验,仿真结果表明改进后的Informed-RRT^(*)算法相较于原始算法规划的路径长度和规划耗时分别减少了17.42%和36.21%。

融合RRT^(*)与DWA算法的移动机器人动态路径规划

为实现移动机器人在复杂动态障碍物环境中的避障,提出一种改进的快速随机扩展树(rapidly-exploring random tree,RRT^(*))与动态窗口法(dynamic window approach,DWA)相融合的动态路径规划方法。基于已知环境信息,利用改进RRT^(*)算法生成全局最优安全路径。通过消除RRT^(*)算法产生的危险节点,来确保全局路径的安全性;使用贪婪算法去除路径中的冗余节点,以缩短全局路径的长度。利用DWA算法跟踪改进RRT^(*)算法规划的最优路径。当全局路径上出现静态障碍物时,通过二次调整DWA算法评价函数的权重来避开障碍物并及时回归原路线;当环境中出现移动障碍物时,通过提前检测危险距离并转向加速的方式安全驶离该区域。仿真结果表明:该算法在复杂动态环境中运行时间短、路径成本小,与障碍物始终保持安全距离,确保在安全避开动态障碍物的同时,跟踪最优路径。

Dynamic path planning strategy based on improved RRT^(*)algorithm

In order to solve the problem of path planning of mobile robots in a dynamic environment,an improved rapidly-exploring random tree^(*)(RRT^(*))algorithm is proposed in this paper.First,the target bias sampling is introduced to reduce the randomness of the RRT^(*)algorithm,and then the initial path planning is carried out in a static environment.Secondly,apply the path in a dynamic environment,and use the initially planned path as the path cache.When a new obstacle appears in the path,the invalid path is clipped and the path is replanned.At this time,there is a certain probability to select the point in the path cache as the new node,so that the new path maintains the trend of the original path to a greater extent.Finally,MATLAB is used to carry out simulation experiments for the initial planning and replanning algorithms,respectively.More specifically,compared with the original RRT^(*)algorithm,the simulation results show that the number of nodes used by the new improved algorithm is reduced by 43.19%on average.

基于贪心策略改进RRT^(*)算法机械臂路径规划

RRT^(*)(rapidly-exploring random tree star)算法是机械臂路径规划中的一个重要工具,但在高维空间内的应用表现存在搜索效率低下、对维数的敏感度高、难以快速收敛至优化路径等问题。此外机械臂避障的规划需要考虑到路径的平滑性,但是算法生成的路径往往缺乏所需的平滑性,难以直接应用于实际的机械臂操作。针对这些问题,研究提出了一个基于贪心策略的RRT^(*)算法改进版本。新算法改进了代价函数和重连策略,并在高维搜索环境中,通过贪心算法进行偏执采样,自适应地选取预设路径节点,从而提高搜索效率,增强轨迹的平滑性并进行直接应用。通过Matlab、ROS仿真和机械臂实际应用避障实验,验证了改进的RRT^(*)算法在三维空间中的高效性和优越性,尤其是在搜索效率与路径平滑性等方面。

基于改进RRT^(*)算法的无人艇路径规划快速求解算法

针对快速扩展随机树(RRT)算法在无人艇路径规划工作中目的性较弱的问题,提出一种改进的无人艇路径规划快速求解算法。对人工势场法进行改进,额外添加4个方向的受力分析,综合计算无人艇所受合力;重新定义转向角度的计算方法,避免其进入局部最优陷阱,使其可以顺利抵达目标点,得到一条初始路径;利用该初始路径来设定快速扩展随机树算法的随机点采样区域,通过降低随机采样点生成在无价值区域的概率,以提高算法的目的性和时效性,得到二次规划路径;对二次规划路径进行冗余点去除操作,减少路径节点的同时可以进一步降低路径代价,得到最终的规划路径。实验结果表明:改进算法在取得相近代价的路径时,运行时间最多降低了84.14%,采样点数量最多减少了70.09%,算法质量更好,运行效率更高。

基于改进RRT^(*)的无人摆渡车泊车路径规划

为提高无人摆渡车在园区泊车的安全性和便利性,对无人摆渡车自动泊车系统的路径规划算法的研究具有重要意义。建立低速泊车状态下的车辆运动学模型和车辆碰撞检测模型,采用RRT*算法进行路径规划,并对其扩展方式、代价函数和采样方式进行改进,之后在垂直和平行泊车环境下对该路径规划算法进行仿真验证。仿真结果表明,改进后的算法规划出的路径不仅满足避障要求和运动学约束,而且路径搜索效率和路径质量更具有优越性。

基于DBS-RRT^(*)算法的机械臂复杂狭窄场景路径规划

针对目前RRT^(*)算法在机械臂复杂狭窄场景路径规划中,存在着规划时间长、路径冗长、狭窄环境规划成功率低的问题,提出一种动态偏置采样DBS-RRT^(*)(dynamic biased sampling RRT^(*))算法。首先,DBS-RRT^(*)算法采用动态偏置率,设计智能椭球子集采样作为偏向采样方法,利用自适应生长策略调整新节点的生长方向与步长,实现动态选择采样方法,提高采样效率,减少无效空间探索,改善搜索导向性的效果;然后,通过设计二维实验验证算法的有效性,实验证明DBS-RRT^(*)算法与RRT^(*)算法相比,规划效率更高,规划路径更短;最后,将DBS-RRT^(*)算法应用于复杂狭窄场景中的机械臂仿真实验。实验数据表明,DBS-RRT^(*)算法与RRT^(*)算法相比,规划路径长度减少了26%,规划时间减少了22.6%,成功率提高了32%。DBS-RRT^(*)算法在复杂狭窄场景中,相比RRT^(*)算法,能够更加有效地实现机械臂避障路径规划。

Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm

The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.

An Improved Q-RRT^(*) Algorithm Based on Virtual Light

The Rapidly-exploring Random Tree(RRT)algorithm is an efficient path-planning algorithm based on random sampling.The RRT^(*)algorithm is a variant of the RRT algorithm that can achieve convergence to the optimal solution.However,it has been proven to take an infinite time to do so.An improved Quick-RRT^(*)(Q-RRT^(*))algorithm based on a virtual light source is proposed in this paper to overcome this problem.The virtual light-based Q-RRT^(*)(LQRRT^(*))takes advantage of the heuristic information generated by the virtual light on the map.In this way,the tree can find the initial solution quickly.Next,the LQRRT^(*)algorithm combines the heuristic information with the optimization capability of the Q-RRT^(*)algorithm to find the approximate optimal solution.LQRRT^(*)further optimizes the sampling space compared with the Q-RRT^(*)algorithm and improves the sampling efficiency.The efficiency of the algorithm is verified by comparison experiments in different simulation environments.The results show that the proposed algorithm can converge to the approximate optimal solution in less time and with lower memory consumption.

MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).

基于改进Informed-RRT^(*)算法的机械臂路径规划

针对机械臂路径规划算法目标导向性不足和路径规划效率低的问题,提出一种改进Informed-RRT^(*)算法用于机械臂路径规划。通过引入目标点偏置策略、目标导向性策略增加向目标点搜索的趋势,同时引入非线性变步长策略提升算法的效率和路径质量;在MATLAB中进行三维环境的算法仿真实验,实验结果表明,改进Informed-RRT^(*)算法显著缩短了规划时间、减少了路径长度、提高了规划成功率。将算法应用在以动车组底部检测为背景的机械臂路径规划仿真中,并使用三次B样条曲线对路径进行平滑处理,算法同样表现出优越的性能,同时机械臂各关节运动曲线平滑连续、无明显突变,验证了其在实际应用中的有效性。

基于改进双树RRT^(*)算法的冗余机械臂末端路径规划

针对冗余机械臂的冗余特性与相关RRT^(*)算法在规划机械臂末端路径的应用中存在的搜索效率较低、收敛性不稳定以及没有充分考虑到机械臂末端几何构型与自身运动特性对路径规划影响的问题,提出一种改进策略。首先,引入一种基于根尾节点连线夹角的采样点选择方式,并设置目标逼近区域。根据连续采样成功次数动态选择改进采样与随机采样。接着,将双树扩展策略与上述方法相结合。最后,将初始可行路径进行二次重连得到最终的优化路径。通过验证,改进双树RRT^(*)方法能够有效地提升搜索效率、收敛性以及路径的优越性。虚拟碰撞体与胶囊碰撞体的引入也能较好地应对机械臂末端结构与运动特性带来的影响。使用Mujoco物理仿真引擎进行机械臂运动验证,证明该策略可以为冗余机械臂末端规划出一条较优的可行路径。

基于改进人工势场法的RRT^(*)无人船路径规划算法

为了使RRT^(*)能够更好地适应不同复杂程度的环境,并快速生成一条平滑的较优路径,本文在RRT^(*)算法的基础上引入人工势场法,设计了基于改进人工势场法的RRT^(*)算法。首先,对全局地图进行划分,并进行分区偏置采样;然后,改进节点拓展方式,引入障碍物大小因子来改进斥力势场函数,引导新节点的生成;同时,引入自适应变步长策略,根据距离障碍物的远近,以不同的步长拓展路径点。为了使规划路径更符合无人船的航行特性,采用三次非均匀B样条对改进算法生成的路径进行了平滑处理。为了验证本文改进算法的优势,通过设计特殊障碍物环境、简单障碍物环境以及复杂障碍物环境,对比分析了RRT、RRT^(*)、人工势场法和本文算法,发现了本文改进算法生成的路径平均长度短于RRT、RRT^(*)和人工势场法所规划的路径长度,路径规划效率更高,面对不同障碍物环境有更好的适用性。

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime.Many studies have been conducted for homogeneous networks,but few have been performed for heterogeneouswireless sensor networks.This paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial energies.The proposed algorithms lack algorithm-specific parameters and metaphorical connotations.The proposed algorithms examine the search space based on the relations of the population with the best,worst,and randomly assigned solutions.The proposed algorithms can be evaluated using any routing protocol,however,we have chosen the well-known routing protocols in the literature:Low Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Gathering in Sensor Information Systems(PEAGSIS),Partitioned-based Energy-efficient LEACH(PE-LEACH),and the Power-Efficient Gathering in Sensor Information Systems Neural Network(PEAGSIS-NN)recent routing protocol.We compare our optimized method with the Jaya,the Particle Swarm Optimization-based Energy Efficient Clustering(PSO-EEC)protocol,and the hybrid Harmony Search Algorithm and PSO(HSA-PSO)algorithms.The efficiencies of our proposed algorithms are evaluated by conducting experiments in terms of the network lifetime(first dead node,half dead nodes,and last dead node),energy consumption,packets to cluster head,and packets to the base station.The experimental results were compared with those obtained using the Jaya optimization algorithm.The proposed algorithms exhibited the best performance.The proposed approach successfully prolongs the network lifetime by 71% for the PEAGSIS protocol,51% for the LEACH protocol,10% for the PE-LEACH protocol,and 73% for the PEGSIS-NN protocol;Moreover,it enhances other criteria such as energy conservation,fitness convergence,packets to cluster head,and packets to the base station.

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.