Improved Dijkstra Algorithm for Mobile Robot Path Planning and Obstacle Avoidance

Optimal path planning avoiding obstacles is among the most attractive applications of mobile robots(MRs)in both research and education.In this paper,an optimal collision-free algorithm is designed and implemented practically based on an improved Dijkstra algorithm.To achieve this research objectives,first,the MR obstacle-free environment is modeled as a diagraph including nodes,edges and weights.Second,Dijkstra algorithm is used offline to generate the shortest path driving the MR from a starting point to a target point.During its movement,the robot should follow the previously obtained path and stop at each node to test if there is an obstacle between the current node and the immediately following node.For this aim,the MR was equipped with an ultrasonic sensor used as obstacle detector.If an obstacle is found,the MR updates its diagraph by excluding the corresponding node.Then,Dijkstra algorithm runs on the modified diagraph.This procedure is repeated until reaching the target point.To verify the efficiency of the proposed approach,a simulation was carried out on a hand-made MR and an environment including 9 nodes,19 edges and 2 obstacles.The obtained optimal path avoiding obstacles has been transferred into motion control and implemented practically using line tracking sensors.This study has shown that the improved Dijkstra algorithm can efficiently solve optimal path planning in environments including obstacles and that STEAM-based MRs are efficient cost-effective tools to practically implement the designed algorithm.

Global optimal path planning for mobile robot based onimproved Dijkstra algorithm and ant system algorithm

A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.

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.

Ant Colony Algorithm for Path Planning Based on Grid Feature Point Extraction

Aimed at the problems of a traditional ant colony algorithm,such as the path search direction and field of view,an inability to find the shortest path,a propensity toward deadlock and an unsmooth path,an ant colony algorithm for use in a new environment is proposed.First,the feature points of an obstacle are extracted to preprocess the grid map environment,which can avoid entering a trap and solve the deadlock problem.Second,these feature points are used as pathfinding access nodes to reduce the node access,with more moving directions to be selected,and the locations of the feature points to be selected determine the range of the pathfinding field of view.Then,based on the feature points,an unequal distribution of pheromones and a two-way parallel path search are used to improve the construction efficiency of the solution,an improved heuristic function is used to enhance the guiding role of the path search,and the pheromone volatilization coefficient is dynamically adjusted to avoid a premature convergence of the algorithm.Third,a Bezier curve is used to smooth the shortest path obtained.Finally,using grid maps with a different complexity and different scales,a simulation comparing the results of the proposed algorithm with those of traditional and other improved ant colony algorithms verifies its feasibility and superiority.

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.

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.

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.

Ant Colony System Algorithm for Real-Time Globally Optimal Path Planning of Mobile Robots

为活动机器人计划的即时全球性最佳的路径的一个新奇方法基于蚂蚁殖民地系统(交流) 被建议算法。这个方法包括三步：第一步正在利用 MAKLINK 图理论建立活动机器人的空间模型，第二步正在利用 Dijkstra 算法发现一条非最优的没有碰撞的路径，并且第三步正在利用 ACS 算法优化非最优的路径的地点以便产生全球性最佳的路径。建议方法是有效的并且能在即时路径被使用活动机器人计划的计算机模拟实验表演的结果。建议方法比与优秀人材模型一起基于基因算法计划方法的路径处于集中速度，答案变化，动态集中行为，和计算效率有更好的性能，这被验证了。

Two-Dimension Path Planning Method Based on Improved Ant Colony Algorithm

Nowadays, path planning has become an important field of research focus. Considering that the ant colony algorithm has numerous advantages such as the distributed computing and the characteristics of heuristic search, how to combine the algorithm with two-dimension path planning effectively is much important. In this paper, an improved ant colony algorithm is used in resolving this path planning problem, which can improve convergence rate by using this improved algorithm. MAKLINK graph is adopted to establish the two-dimensional space model at first, after that the Dijkstra algorithm is selected as the initial planning algorithm to get an initial path, immediately following, optimizing the select parameters relating on the ant colony algorithm and its improved algorithm. After making the initial parameter, the authors plan out an optimal path from start to finish in a known environment through ant colony algorithm and its improved algorithm. Finally, Matlab is applied as software tool for coding and simulation validation. Numerical experiments show that the improved algorithm can play a more appropriate path planning than the origin algorithm in the completely observable.

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。

Grid A^(*):面向野外空地协同应急处置的快速路径规划

在野外应急救援活动中,灾害现场或事故区域通常缺乏地面交通工具可直达的现成道路,但该区域地表环境仍可满足部分越野车辆的通行。在空地协同系统中,无人机可提供行进路径周边环境的影像,地面终端可快速提取影像中地表类型以及地形起伏等特征信息,通过分析计算便可为车辆提供通往救援目标点的导航路径。本文针对这一应用需求,对现有A^(*)算法进行了改进,主要有3个方面的创新:其一,针对户外地表环境的应用特点,提出一种综合地表类型与地表高程信息的通行性代价函数;其二,针对无人机影像分辨率与实际车辆通行路径之间的尺度关系,提出一种基于格网单元的路径快速搜索算法;其三,在顾及格网单元内部地表类型连通分布特点的基础上,选择格网边缘特征点用于通行性路径规划,在提高算法搜索效率的同时,兼顾了格网单元内部的地形信息,从而使算法在优化计算的同时,能充分利用到无人机影像的细节信息。实验表明,算法搜索得到的可通行路径具有较高的可靠性,从路径三维可视化结果来看,符合越野车辆通行的需要。此外,同等情况下,本算法的运行时间降至传统A^(*)算法的15%,提高了野外应急救援应用的时效性。

基于Dijkstra算法的平滑路径规划方法

移动机器人在复杂环境下沿Dijkstra算法规划的路径运动时,由于所规划的路径存在转折点多、部分转折角度小等问题,导致移动机器人不得不频繁转向,甚至要暂停才能完成转向,严重影响机器人的工作效率。利用几何拓扑学方法,结合实际场景信息,提出一种基于Dijkstra算法的平滑路径规划方法。根据应用场景获取连续化地图,将连续化地图离散化后随机生成离散点阵,计算各点之间的欧氏距离,选取与各离散点距离较近、且连线不跨越障碍的多个点,将其连接并生成离散图。在离散图中利用Dijkstra算法搜索最优路径作为引导路径。当移动机器人沿引导路径运动时,结合实际场景信息,采用几何拓扑学计算出移动机器人每一时刻应该采取的最佳动作和运行路线。实验结果表明:所提方法能够有效减少移动机器人运动中的累计转弯角度,增大最小平均转折角度,提高所规划路径的平滑度,从而缩短移动机器人的运动时间,提升机器人的工作效率。

融合改进Dijkstra算法和动态窗口法的移动机器人路径规划

为解决移动机器人在智能制造车间的全局路径规划和局部动态避障问题,提出一种融合改进的Dijkstra算法和改进的DWA算法,对传统Dijkstra算法的路径进行平滑优化,使得路径轨迹更加平滑,动态改变DWA算法中速度评价权重函数,提高避障效率。仿真结果表明,改进Dijkstra算法路径平滑优化后,平均路程缩短比例为0.65%,平均偏航角震荡次数减少了67.70%,改进后的DWA算法运行路程缩小9.68%,路径转折次数降低了33%,运行时间缩短3.88%。基于改进的Dijkstra算法和改进的DWA算法提出一种融合算法,仿真和样机实验结果表明:面对静态、动态障碍物,机器人运行线速度平缓,轨迹光滑,角速度波动明显,证明机器人运动稳定,实时调整方位,具有良好的避障能力。并且多次机器人循环定点实验中机器人纵向(X轴方向)平均误差≤30 mm,横向(Y轴)平均误差≤30 mm,定位精度满足工业需求。

基于Dijkstra算法的搬运车省时路径规划研究

近年来,制造业快速向着自动化、智能化发展,智能搬运车逐渐映入我们的眼帘。随着智能搬运车的普及,其路径规划的重要性也被重视。Dijkstra算法是经典的路径规划算法之一,而传统的Dijkstra算法只能保留一条最短路径。本文对Dijkstra算法进行改进,使其能够保留所有最短路径,再将保留的所有路径进行行驶时间的计算,经计算对比筛选出耗时最短的路径。达到不仅路程短,还耗时少的目标,可以有效地节省工作时间,提高效率。

基于Dijkstra算法的移动机器人最短路径规划设计研究

本研究针对移动机器人的路径规划问题,结合Dijkstra算法进行了深入探讨。设计了一套针对移动机器人的最短路径规划系统,该系统全面考虑了机器人的运动约束、环境信息以及目标位置等关键因素。在此基础上,提出了一种基于Dijkstra算法的路径规划方案,旨在实现高效且精准的路径规划,为移动机器人的自主导航提供有力支持。

基于自动化码头的改进Dijkstra算法路径规划研究

针对自动化码头多路径规划中Dijkstra算法存有路径冲突的问题,引入时间窗防冲突判断模型对其进行改进。在Dijkstra算法从源节点向其他各节点逐步遍历的过程中,将时间窗冲突判断模型加到各路径节点上。通过改变规划中的路径节点向量,将每一个节点的所有前节点记录在路径节点向量中,在所有的路径中,搜索出一条最短路径,保证此最短路径与其他路径无冲突。在计算机上运用Matlab仿真软件进行算法验证,仿真结果显示,算法经过改进后,在规划多路径任务中能够得到预期效果。

基于地铁站场景的改进型Dijkstra算法疏散路径规划研究

地铁站是城市交通枢纽的重要组成部分,由于其结构和功能具有特殊性,当发生突发事件时,易造成较大的公共安全事故。及时和科学合理地制定人员疏散方案,将人员快速疏散到地上安全区域可以有效降低事件造成的损失与影响。针对城市地铁站内人员疏散路径规划问题,提出一种对疏散路径自身特性进行综合评价的方法。对疏散路径的实际长度、有效宽度及通行难易度3种特征属性进行综合评价,引入路径通行难易度的理论计算公式,用当量长度作为路径综合评价的结果。其次,为兼顾人员数量及拥挤程度在疏散时对人员路径选择的影响,提出节点拥挤度概念,将节点拥挤度作为算法搜索时路径选择的决策原则之一。最后,在标准Dijkstra算法基础上提出一种改进型Dijkstra算法计算疏散路径模型,以温州地铁奥体中心站为背景,进行具体的实例应用。研究结果表明:相比于标准Dijkstra算法模型,改进型Dijkstra算法人员疏散路径规划模型可优化安全出口使用率,均衡各安全出口人流量,提高整体人员疏散效率。研究结果可为地铁站场景人员疏散路径规划以及人流诱导提供一定的理论基础和技术支撑。

基于Dijkstra算法的封闭环境全局路径规划

随着自动驾驶技术的快速发展,其关键技术被划分为感知、决策、规划及控制四大模块,路径规划在其中承担着重要角色。良好的路径规划算法能够在行车前规划出一条最优路径,并在行车过程中能够结合感知、决策传送回来的环境信息,实时规划出微调后的局部路径,在完成避障、换道等功能的基础上,兼顾着行驶轨迹的安全性、舒适性。文章从全局路径规划出发,基于Dijkstra最短路径规划算法,构建封闭环境下拓扑节点地图,改进算法中不适用自动驾驶实车测试中的痛点情况,以实现任意起始点至终点的全局路径规划任务。最终在实车试验中进行验证,试验结果表明,在封闭且闭环的环境下,自动驾驶汽车可规划出理想的最短路径进行行驶。

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。