Ant Colony Optimization with Potential Field Based on Grid Map for Mobile Robot Path Planning

For the mobile robot path planning under the complex environment,ant colony optimization with artificial potential field based on grid map is proposed to avoid traditional ant colony algorithm's poor convergence and local optimum.Firstly,the pheromone updating mechanism of ant colony is designed by a hybrid strategy of global map updating and local grids updating.Then,some angles between the vectors of artificial potential field and the orientations of current grid are introduced to calculate the visibility of eight-neighbor cells of cellular automata,which are adopted as ant colony's inspiring factor to calculate the transition probability based on the pseudo-random transition rule cellular automata.Finally,mobile robot dynamic path planning and the simulation experiments are completed by this algorithm,and the experimental results show that the method is feasible and effective.

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.

基于Grid-GSA算法的植保无人机路径规划方法

为了提高植保无人机的作业效率,研究了一种路径规划方法。运用栅格法构建环境模型,根据实际的作业区域规模、形状等环境信息和无人机航向,为相应栅格赋予概率,无人机优先选择概率高的栅格行进。基于上述机制实现了在形状不规则的作业区域内进行往复回转式全覆盖路径规划;以每次植保作业距离为变量,根据仿真算法得出返航点数量与位置来确定寻优模型中的变量维数范围,以往返飞行、电池更换与药剂装填等非植保作业耗费时间最短为目标函数,通过采用引力搜索算法,实现对返航点数量与位置的寻优;为无人机设置必要的路径纠偏与光顺机制,使无人机能够按既定路线与速度飞行。对提出的路径规划方法进行了实例检验,结果显示,相比于简单规划与未规划的情况,运用Grid-GSA规划方法得出的结果中往返飞行距离总和分别减少了14%与68%,非植保作业时间分别减少了21%与36%,其它各项指标也均有不同程度的提高。在验证测试试验中,实际的往返距离总和减少了322 m,实际路径与规划路径存在较小偏差。验证了路径规划方法具有合理性、可行性以及一定的实用性。

Robot Global Path Planning Based on an Improved Ant Colony Algorithm

Aiming at the disadvantages of the basic ant colony algorithm, this paper proposes an improved ant colony algorithm for robot global path planning. First, adjust the pheromone evaporation rate dynamically to enhance the global search ability and convergence speed, and then modify the heuristic function to improve the state transition probabilities in order to find the optimal solution as quickly as possible;and finally change the pheromone update strategy to avoid premature by strengthening pheromone on the optimal path and limiting pheromone level. Simulation results verify the effectiveness of the improved algorithm.

A novel method for robot path planning

Path planning is one of the most important problems in the design of a mobile robot.A novel approach called generalized Voronoi diagrams(GVD)may deal with this matter.First,a method was introduced to normalize the obstacles and present efficient techniques for generating GVDs.Then a best path searching algorithm was presented.Examples implemented were given to indicate the availability of the mentioned algorithms.The approaches in this paper can also be used in applications including visualization,spatial data manipulation,etc.

A Linear Interpolation-Based Algorithm for Path Planning and Replanning on Girds

Field D* algorithm is widely used in mobile robot navigation since it can plan and replan any-angle paths through non-uniform cost grids. However, it still suffers from inefficiency and sub-optimality. In this article, a new linear interpolation-based planning and replanning algorithm, Update-Reducing Field D*, is proposed. It employs different approaches during initial planning and replanning respectively in order to reduce the number of updates of the rhs-values of vertices. Experiments have shown that Update-Reducing Field D* runs faster than Field D* and returns smoother and lower-cost paths.

基于全局导向的智能车辆路径规划融合算法研究

针对曲线道路的路径规划问题,本文提出一种基于全局导向人工势场法的路径规划融合算法。考虑持续转弯的弯曲道路工况,构建基于变形栅格的栅格地图;考虑道路环境中的行车风险,基于行车风险场理论优化A^(*)算法启发函数从而改进A^(*)算法。改进传统人工势场法的局限性及固有缺陷,在局部路径规划中考虑自车、环境车辆及障碍物的轮廓形状,引入全局导向路径进一步改进人工势场法。以改进A^(*)算法规划路径为全局最优导向路径,设计基于改进人工势场法的路径规划融合算法。仿真结果表明,提出的融合算法可以生成有效的行驶路径,与数据集提取的实车路径接近,且在障碍物环境中规划的路径安全高效,满足车辆的行驶要求。

基于避障寻优改进蚁群算法的机器人路径规划

针对蚁群算法在处理路径规划过程中存在收敛速度慢,规划路径冗余等问题,提出了一种基于避障信息和快速寻优策略的改进蚁群算法。为了改善蚁群的首次搜索效率和精度,引入切比雪夫距离改进距离启发函数,在转移概率中增加目标点对机器人的引导作用;采用自适应转移概率调整路径规划过程中节点的选择方式,并根据节点周围的障碍物分布设置初始信息素,使得蚂蚁首次生成有效路径的比率从60%提高至92%;同时删除生成路径的垃圾信息,提高最优路径节点的信息素浓度,平衡了蚁群的局部和全局搜索能力,加快了最优路径的速度;通过平滑生成的路径,减少机器人转弯次数,缩短了路径距离。选择SSA、ACO、IACO、I-ACO等算法在3种栅格环境上进行性能测试。结果表明,改进的蚁群算法路径寻优上优于其他算法。

基于拟水流算法在移动机器人路径规划中的应用

针对传统算法在移动机器人路径规划中存在搜索效率低的问题,提出了一种拟水流算法。该算法利用主流点搜索模型得到所有主流点。从起点逐步流动,通过拟水流避障算法和拟病毒算法进行避障,直至终点,并平滑处理所得路径。通过栅格法对多种地图环境进行建模,将拟水流算法与蚁群算法、Dijkstra算法、Floyd算法和A^(*)算法的路径长度及运算时间进行对比仿真实验。实验结果显示,与获得最短路径和最少时间的A^(*)算法相比,拟水流算法获得的平均路径长度减少了2.40%~6.30%,平均用时减少了35.71%~53.51%。最后,将拟水流算法应用于移动机器人Turtlebot2,并与A^(*)算法进行了对比实测实验。实验结果显示,拟水流算法相较A^(*)算法,实测路径增加了3.83%,寻路时间减少了10.77%,拐点数减少了42.86%。

NEW APPROACH ON CUTTER PATH GENERATION FOR PLANE MILING

A new approach on cutter path generation for plane milling is proposed. The cutter feed status at the position of each grid mesh can be determined by using a specific algorithm consisting of data pro- cessing and some heuristic rules. From the cutter feed status and the coordinates of the grid meshes, the cutter path for milling plane can be generated.

The Effect of Nanometer Size Effect on the Optical Property of Metallic Wire Grid

We mainly investigated the effect of metallic wire grid on its optical property. At first, we give one simple model to deduce an expression which can describe the relationship of the optical property with the width of metallic wire grid. This expression could be used to calculate the reflectance of the metallic wire grid. We also give the corresponding computer simulation. Our simulation shows that the reflectance would increase when the width of metallic wire grid increase. The wider the metallic wire grid is, the higher the reflectance is. The reflectance would reach the maximum value only when the width is over the free path of electronic.

APL-based flexibility analysis of manufacturing grid

With the characteristics of diversity, randomness, concurrency and decomposability, tasks in manufacturing field are very complicated, and so manufacturing grid (MG) should have considerable flexibility to deal with this problem. With the definition of node and arc, MG structure is converted into a small-world network. Given construction cost constraint, the problem of shortest task waiting time is transformed into the constrained optimization problem, and a corresponding ?exibility analysis model based on average path length (APL) is proposed, and the premise of arc-length and node-distance are defined. The results of application example show that the analysis model is effiective.

改进哈里斯鹰算法的仓储机器人路径规划研究

为提高静态环境下仓储移动机器人路径规划效率,解决传统哈里斯鹰(Harris Hawks optimization, HHO)算法在路径规划中存在收敛速度慢且易陷入局部最优的问题,提出了一种基于Tent混沌映射融合柯西反学习变异的哈里斯鹰优化算法(HHO algorithmbasedon Tentchaotic mapping hybrid Cauchy mutation and inverse learning, TCLHHO)。通过Tent混沌映射增加种群多样性,以提高算法的收敛速度;提出指数型的猎物逃逸能量更新策略,以平衡算法的全局搜索和局部开发能力;通过柯西反学习变异策略对最优个体进行扰动,扩大算法的搜索范围,增强全局搜索能力。根据真实仓储环境搭建二维栅格环境模型,并在Matlab中进行仿真对比实验。结果表明:该算法的规划速度、最优路径长度以及最优路径转折次数较对比算法具有较好的效果,验证了应用于智能仓储环境下改进的HHO路径规划问题的可行性和鲁棒性。

多功能草方格沙障自动铺设机械车

为有效提高草方格铺设的效率和质量、促进草方格铺设由机械化向自动化、智能化迈进,根据人工铺设草方格流程,以散草作为原材料,设计了一种自动进行草方格铺设的机械车,用于完成分草、送草、插草和固草动作,并在此基础上赋予机械车播种和喷洒土壤改良剂功能,解决人工铺草工序繁多的问题,将工程治沙、生物治沙和土壤改良剂使用相结合实现综合治沙。另外,进行路径优化,创新铺设模式,通过三边错位铺设模式和单边模式相结合,有效避免对已铺设草方格的破坏。为铺设散草、机械化向智能化转变提供解决思路。

基于栅格法的农业机器人路径规划方法研究

智能农业机器人是无人农场实现机器完全替换人工劳动的重要一环,而路径规划是农业机器人的重要关键技术。为此,针对农田特殊作业环境及农艺要求,提出了农业机器人全局路径规划方法和局部最优规划方法。阐述了农田全覆盖路径规划思想,设计了基于神经元激励网络法的全覆盖路径规划方法和基于Dijkstra算法的局部最优路径规划方法。通过MatLab仿真试验验证路径规划方法的可行性,建立栅格地图,并随机设置障碍物信息模拟实际作业环境。研究结果表明:生物激励与神经元激励两种规划方法覆盖完成率均为100%,路径长度与转弯次数两者相差不大,但神经元激励法的路径重复率5.49%远远小于生物激励法的12.66%;基于神经元激励网络法的全覆盖路径规划方法比生物激励网络法的全覆盖路径规划方法重耕率更低,可实现农田地块大规模覆盖作业;基于Dijkstra算法的局部最优路径规划方法可针对不同复杂程度的障碍物地图实现自主最优路径规划。

基于改进A^(*)算法的车间物料配送路径规划

针对传统避障搜索算法在车间物料配送中仅能解决单点配送且未充分考虑多点配送及往返取货需求的问题,提出一种结合遗传算法优化的A^(*)算法.该方法利用A^(*)算法的成本计算方式完成有障碍物条件下各配送点之间的成本计算,并融合遗传算法的迭代寻优特性,实现了对多点配送及往返取货需求的高效稳定全局搜索.通过某车间物料配送的实际算例验证,该改进算法能有效规划障碍环境下的配送路径,显著提升配送效率.

基于A^(*)算法的四面体机器人路径规划研究

针对四面体机器人在三角网格地图中的可避障路径规划问题,提出了一种基于A^(*)算法的四面体机器人路径规划方法。基于机器人的运动原理,分析了机器人的运动轨迹为三角形网格;基于三角网格地图,提出了机器人位姿表示方法;在此基础上,提出了基于A^(*)算法的机器人路径规划方法。利用Matlab软件对四面体机器人的路径规划进行仿真试验,验证了方法的可行性与准确性,为四面体机器人深空陆巡探测奠定了重要基础。

基于冲突搜索的多AGV路径规划

为实现仓储环节的自动化操作,对多AGV路径规划进行了研究,使用栅格地图法构建地图环境,以单路径A^(*)搜索算法为基础,采用双层路径规划的冲突搜索算法解决多AGV路径冲突问题。具体实施方法是在高层进行冲突检测和添加约束,在底层使用A^(*)算法进行单路径规划。最后使用Python的matplotlib库编程对路径规划算法进行仿真。仿真结果表明,该课题所使用的规划方法行之有效。

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。

基于遗传算法改进的AGV路径规划研究

自动引导搬运车(AGV)节能路径规划可提高AGV使用过程的能效,是实现车间生产节能的一种可行方法。为解决AGV在栅格地图中规划路径时易出现的拐弯角度较多和穿墙等问题,并降低AGV在移动时的能耗,提出一种基于遗传算法改进的AGV路径规划方案。首先减少障碍物角点数,并将路径长度作为目标函数;然后对变异算子进行改进,减少拐弯次数,降低AGV在运动过程中的能耗,提高AGV运行的安全性。仿真结果表明,相比普通遗传算法,改进方法能够找到长度短、能耗更低且安全的路径。