Heterogeneous-ants-based path planner for global path planning of mobile robot applications

Mobile robots can be applied to a wide range of problems, and the demand for these applications has risen in recent years, increasing interest in the study of mobile robotics. Many studies have examined the path planning problem, one of the most important issues in mobile robotics. However, the grid paths found by traditional planners are often not the true shortest paths or are not smooth because their potential headings are artificially constrained to multiples of 45 degrees. These paths are unfit for application to mobile robots because the high number of heading changes increases the energy required to move the mobile robot. Some studies have proposed a post-processing step to smooth the grid path. However, in this case, the post-smoothed path may not necessarily find the true shortest path because the post-smoothed path is still constrained to headings of multiples of 45 degrees. This study attempts to develop a global path planner that can directly find an optimal and smoother path without post-processing to smooth the path. We propose a heterogeneous-ants-based path planner (HAB-PP) as a global path planner to overcome the shortcomings mentioned above. The HAB-PP was created by modifying and optimizing the global path planning procedure from the ant colony optimization (ACO) algorithm. The proposed algorithm differs from the traditional ACO path planning algorithm in three respects: modified transition probability function for moving ants, modified pheromone update rule, and heterogeneous ants. The simulation results demonstrate the effectiveness of the HAB-PP.

     

基于粒子群算法的移动机器人全局最优路径规划*

以栅格法和粒子群算法为基础,提出了一种新的机器人实时全局最优路径规划方法.该方法包括采用栅格法对环境进行建模和直接运用粒子群算法在环境模型中搜索全局最优路径.在计算机上进行了仿真,仿真结果证明了该方法的可行性和有效性.     

Navigation integration of a mobile robot in dynamic environments

This article presents a design and experimental study of navigation integration of an intelligent mobile robot in dynamic environments. The proposed integration architecture is based on the virtual‐force concept, by which each navigation resource is assumed to exert a virtual force on the robot. The resultant force determines how the robot will move. Reactive behavior and proactive planning can both be handled in a simple and uniform manner using the proposed integration method. A real‐time motion predictor is employed to enable the mobile robot to deal in advance with moving obstacles. A grid map is maintained using on‐line sensory data for global path planning, and a bidirectional algorithm is proposed for planning the shortest path for the robot by using updated grid‐map information. Therefore, the mobile robot has the capacity to both learn and adapt to variations. To implement the whole navigation system efficiently, a blackboard model is used to coordinate the computation on board the vehicle. Simulation and experimental results are presented to verify the proposed design and demonstrate smooth navigation behavior of the intelligent mobile robot in dynamic environments. ©1999 John Wiley & Sons, Inc.     

基于改进粒子群算法的移动机器人路径规划

为了提高复杂环境下移动机器人的精准导航作用,提出了移动机器人路径规划的改进粒子群优化(PSO)算法,即利用粒子个体极值的加权平均值,同时加入惯性权重.建立了移动机器人工作环境的栅格模型,利用Matlab软件进行移动机器人路径规划仿真分析.仿真结果表明:改进后的粒子群算法容易使粒子移动到最佳位置,加强了全局寻优能力,在复杂环境中搜索路径性能优于传统算法.     

机器人在已知三维自然环境中的路径规划算法

研究了移动机器人在三维环境的路径规划问题,针对该问题中存在环境适应性和全局性差的不足,对人工势场法进行改进,提出了一种新的路径规划的算法.该算法首先对已知的三维自然环境进行栅格化,建立栅格运行费用的评估模型,计算每个栅格的运行费用;然后依据栅格的运行费用建立斥力场,以目标点为中心的建立引力场,同时提出解决局部最小值的问题的方法;最后将两者合力的方向作为移动机器人在该点的路径走向,规划出一条从起始点到目标点的运行费用较低的路径.仿真实验结果表明,该算法能有效降低运行费用,适应性和全局性好,适合应用于移动机器人在三维自然环境中的路径规划.     

基于自适应机制改进蚁群算法的移动机器人全局路径规划

针对基本蚁群算法在二维静态栅格地图下进行移动机器人路径规划时出现的搜索效率低下、收敛速度缓慢、局部最优解等问题,提出一种自适应机制改进蚁群算法,用于移动机器人在二维栅格地图下的路径规划.首先采用伪随机状态转移规则进行路径选择,定义一种动态选择因子以自适应更新选择比例,引入距离参数计算转移概率,提高算法的全局搜索能力以及搜索效率;然后基于最大最小蚂蚁模型和精英蚂蚁模型,提出一种奖励惩罚机制更新信息素增量,提高算法收敛速度;最后定义一种信息素自适应挥发因子,限制信息素浓度的上下限,提高算法全局性的同时提高算法的收敛速度.在不同规格的二维静态栅格地图下进行移动机器人全局路径规划对比实验,实验结果表明自适应机制改进蚁群算法具有较快的收敛速度,搜索效率明显提高且具有较好的全局搜索能力,验证了所提算法的实用性和优越性.     

基于JPS策略的ACS移动机器人全局路径规划

针对蚁群系统（ACS）算法收敛速度慢、易陷入局部最优、路径转折点数量过多等问题,提出了一种基于跳点搜索（JPS）策略的ACS全局路径规划算法．该算法在迭代前加入一只特殊蚂蚁,利用方向因子引导该蚂蚁始终朝着目标方向前进,并查询是否存在最简路径;在蚂蚁查询下一个节点时,利用JPS算法思想舍去大部分不需要计算的节点．最后,为验证该方法的有效性,使用不同规格的栅格地图进行了仿真实验,仿真结果表明,改进的ACS算法相比于ACS算法,收敛速度加快、收敛时间缩短,且路径更优．最后将算法应用到实际的基于机器人操作系统（ROS）的移动机器人导航实验中,实验结果表明,改进的ACS算法能够有效地解决移动机器人全局路径规划问题,且能明显提升机器人全局路径规划的效率．     

基于几何法的移动机器人路径规划

旨在解决动态环境中移动机器人与障碍物发生碰撞可能性的判断和避开障碍的路径规划。提出了采用几何计算的方法判断机器人和障碍物之间发生碰撞的条件,规划出机器人沿着收敛曲线运动到安全圆周,在安全圆周上作动态圆周运动,最后沿着圆弧退出圆周到达预定的避障路径。将基本的避开障碍的理论和几何算法有机地结合起来,获得了光滑的路径,提高了机器人避开障碍的效率。     

基于多传感器的移动机器人路径规划

提出一种基于多传感器的移动机器人路径规划策略。利用声纳传感器和CCD摄像机对环境进行探测,得到关于障碍物的信息,通过一种简单、快速的数据融合算法计算出障碍物相对于机器人的位置坐标。采用切线法进行路径规划,实现了移动机器人在不确定环境下的路径规划,使机器人可以很好地避开障碍物,并以局部最优或次最优路径到达指定位置。实验结果验证了该路径规划算法的良好性能。     

一种基于改进人工蜂群算法的机器人实时路径规划方法

在移动机器人控制领域,路径规划是最重要并具有挑战性的问题之一。本文首先采用栅格法对环境进行建模,然后提出一种改进的人工蜂群算法用于机器人的路径规划。在该算法中,为了提高人工蜂群算法的收敛速度,提出自适应的搜索方式,并采用精英保留选择策略以避免机器人路径规划陷入局部最优。仿真实验结果表明,所提算法在机器人路径规划上的可行性与有效性。     

The Approximate Cell Decomposition with Local Node Refinement Global Path Planning Method: Path Nodes Refinement and Curve Parametric Interpolation

The paper presents a novel global path planning approach for mobile robot navigation in two dimensional workspace cluttered by polygonal obstacles. The core of the planning method introduced is based on the approximate cell decomposition method. The advantage of the new method is the employment of novel path refinement procedures of the paths produced by approximate cell decomposition that are based on local characteriscics of the workspace. Furthermore, the refined path is parametrically interpolated by cubic splines via a physical centripetal model, introducing the dynamic constraints of mobile robots' motion to the path construction. The method has been implemented both in a computer graphics simulation and on a real mobile robot cruising at indoor environments. Planned paths on several configurations are presented.     

一种基于声纳信息的移动机器人实时导航方法

提出了一种基于声纳信息的移动机器人实时导航方法。首先建立声纳感知数据向地图映射的概率模型,将声纳感知到的环境信息以基于栅格的概率值进行表示,并利用D-S证据理论对其进行数据融合,得到机器人的局部环境。在此基础上,采用基于滚动窗口的方法进行移动机器人路径规划,最终实现实时导航。试验结果表明该方法是可行和有效的。     

一种蚂蚁遗传融合的机器人路径规划新算法

针对栅格法建模的不足,本文研究一种全新的蚂蚁算法与遗传算法融合的机器人路径规划算法.该方法首先用栅格法建立机器人运动空间模型,在此基础上利用蚂蚁算法进行全局搜索得到全局导航路径,然后用遗传算法局部调节全局导航路径上的路径点,得到更优路径.计算机仿真实验表明,即使在复杂的环境下,利用本算法也可以规划出一条全局优化路径,且能安全避障.     

CONCURRENT PATH CONSTRUCTION FOR GLOBAL MAP BUILDING IN UNKNOWN INDOOR ENVIRONMENTS

The map building problem is the most fundemental issue in intelligent mobile robot reasearch. Several map construction methods have been proposed, but most of them require a predefined path before the construction of an environment map. In this study, a novel concurrent path construction algorithm for map building problem in a completely unknown indoor environment is presented. In the concurrent path construction task, the main idea is based on a lookahead strategy that estimates the next location where the robot will go, using its latest state and range readings. A local map is derived from the readings of 2D range-finders and line segments constituting the local map are also used to update a global map. A significant feature of this study is that it does not reqire a predefined path for navigation to construct an environment map. Moreover, optimum sensor readings are aimed to decrease the data processing time needed for a complete map construction by reducing the number of range readings from the surroundings. Simulation results on widely used and custom indoor architectures demonstrate that the proposed approach generates highly efficient paths and global maps for all handled problem instances.     

Real-Time Robot Path Planning Based on a Modified Pulse-Coupled Neural Network Model

This paper presents a modified pulse-coupled neural network (MPCNN) model for real-time collision-free path planning of mobile robots in nonstationary environments. The proposed neural network for robots is topologically organized with only local lateral connections among neurons. It works in dynamic environments and requires no prior knowledge of target or barrier movements. The target neuron fires first, and then the firing event spreads out, through the lateral connections among the neurons, like the propagation of a wave. Obstacles have no connections to their neighbors. Each neuron records its parent, that is, the neighbor that caused it to fire. The real-time optimal path is then the sequence of parents from the robot to the target. In a static case where the barriers and targets are stationary, this paper proves that the generated wave in the network spreads outward with travel times proportional to the linking strength among neurons. Thus, the generated path is always the global shortest path from the robot to the target. In addition, each neuron in the proposed model can propagate a firing event to its neighboring neuron without any comparing computations. The proposed model is applied to generate collision-free paths for a mobile robot to solve a maze-type problem, to circumvent concave U-shaped obstacles, and to track a moving target in an environment with varying obstacles. The effectiveness and efficiency of the proposed approach is demonstrated through simulation and comparison studies.      

移动机器人全覆盖信度函数路径规划算法

针对移动机器人全覆盖路径规划问题,给出一种基于栅格信度函数的全覆盖路径规划算法。目的是为了控制移动机器人能够遍历工作区域中所有的可到达点,同时保证能够自动避开障碍物。首先,根据环境的信息对栅格地图进行赋值,使用不同的函数值表示障碍物、已覆盖栅格和未覆盖栅格;其次,判断机器人是否陷入死区引入不同方向信度函数,对栅格函数值进行调整;最后,机器人根据栅格信度函数值规划覆盖路径。本文所提及的算法不仅能够引导移动机器人实现工作区域的全覆盖而且能够快速逃离死区,实现覆盖路径的低重复率。仿真实验中,通过与生物启发神经网络算法的比较,证明本文提及算法有更高的覆盖效率。     

一种车型机器人路径规划方法

在自主移动机器人的许多应用中,路径规划技术顺序地设置一套分散的路径点来引导机器人以最短的时间从起始位置到达目标点。针对移动机器人路径规划问题,提出了一种非完整型机器人路径规划技术,该技术采用基本原子操纵方法来解决车型机器人路径规划问题,并采用平滑路径规划方法来产生更多的连续路径用以解决基本原子操纵技术在做路径规划时具有很不连续的缺点从而为机器人获得最优路径。仿真结果证明了该方法的有效性和实用性。     

一种移动机器人全局最优路径规划算法

针对移动机器人全局最优路径规划问题,首先提出一种基于线性操作的遍历式算法,该算法通过场扫描方式生成步长转换矩阵(STM,Step Transform Matrix),并在步长转换矩阵中搜索考虑方向一致的最短路径,从而可以得到避免不必要路径转折的全局最短路径.其次提出了一种评价标准来区分路径优劣.最后通过仿真与圆形波传播算法进行了路径规划对比实验,实验结果表明本文所提算法在所提出的路径评价标准下可以获得比波传播算法更优的路径.     

基于免疫遗传算法的移动机器人全局路径规划

提出了基于免疫遗传算法的静态环境下移动机器人全局路径规划方法。该方法首先建立机器人工作空间中环境信息的神经网络模型,并利用该模型建立机器人免碰撞路径与神经网络输出的关系,将免碰撞要求和路径最优要求融合成免疫遗传算法的一个简单适应度函数。将抗体选择概率表示成一个基于抗体矢量距和抗体浓度的融合函数,同时保证了抗体的多样性和成熟收敛。通过仿真,并与遗传算法相比,性能有很大提高,证明了该全局路径规划方法的正确性和有效性。     

微粒群与蚂蚁融合的机器人路径规划新算法

针对栅格法建模的不足,研究了一类全新的微粒群与蚂蚁算法融合的机器人路径规划算法。该方法首先用栅格法建立机器人运动空间模型,在此基础上利用蚂蚁算法进行搜索得到全局导航路径,然后用微粒群算法局部调节导航路径上的路径点,得到更优路径。计算机仿真实验表明,即使在蚂蚁算法得到的导航路径不佳的情况下,利用本算法也可以规划出一条全局优化路径,且能安全避障。