动态未知环境下一种新的机器人路径规划方法

针对机器人动态路径规划问题,提出了一种机器人在复杂动态环境中实时路径规划方法.该方法基于滚动窗口的路径规划和避障策略,通过设定可视点子目标、绕行障碍物和对动态障碍物的分析预测,实现机器人在复杂动态环境下的路径规划.针对障碍物分布情况,合理设计可视点法和绕行算法之间转换,有效地解决了局部路径规划的死循环与极小值问题.该方...     

基于模糊势场法的移动机器人局部路径规划

针对采用传统人工势场法进行移动机器人局部路径规划时存在的局部极小点和规划路径过长等问题,提出了一种基于虚拟目标点和有限状态机的模糊势场法。构造基于人工势场的虚拟目标点法来解决局部极小点问题,在合适的位置设置虚拟目标点使机器人逃离局部极小点区域。将虚拟目标点法与模糊控制相结合,对障碍物环境进行预测,及时避障,解决机器人在复杂环境中采用虚拟目标点法规划路径时存在的路径过长问题。设计一个有限状态机来判断障碍物环境,执行算法转换策略,使改进算法适用于多种复杂环境。所设计算法在MATLAB平台上进行了仿真验证。结果表明,该算法能够使机器人逃出局部极小点、缩短规划路径。算法不仅适用于简单、离散环境,在传统算法运行困难的、复杂的环境中,例如墙型、U型和多U型障碍物环境,也能规划出可行的优化路径。     

基于多行为的移动机器人路径规划

机器人由当前点向目标点运动的过程中,所处环境经常为动态变化且未知的,这使得传统的路径规划算法对于移动机器人避障过程很难建立精确的数学模型.为此,针对环境信息完全未知的情况,为移动机器人设计一种基于模糊控制思想的多行为局部路径规划方法.该方法通过对各种行为之间进行适时合理的切换,以保证机器人安全迅速地躲避静态和动态障碍物,并利用改进的人工势场法实现对变速目标点的追踪.对于模糊避障中常见的U型陷阱问题,提出一种边界追踪的陷阱逃脱策略,使得机器人成功解除死锁状态.另外,设计一个速度模糊控制器,实现了机器人的智能行驶.最后,基于Matlab平台的仿真结果验证了所提出算法的有效性和实时性,与A*势场法的对比结果更突出了该算法的可行性.     

基于改进蚁群和DWA算法的机器人动态路径规划

路径规划技术是移动机器人研究领域中的一个重要分支,使得机器人能够在多障碍物环境中安全快速地找到一条相对最优路径.针对全局路径规划时蚁群算法盲目性搜索、易陷入局部最优、收敛速度慢以及局部路径规划时DWA算法难以有效地规避动态障碍物等问题,提出一种改进蚁群算法与DWA算法的融合算法.首先,采用GRRT-Connect算法不等分配初始信息素,解决陷阱地图中局部最优问题;然后,增加蚁群接力搜索方法以解决蚂蚁禁忌表自死锁问题,并利用切片取优方法优化最优路径选择机制得到全局最优路径;接着,以最优路径关键点为子目标点运行DWA算法,提出自适应调节速度方法进行最优行驶;最后,提出预计算方法规避动态障碍物达到局部规划效果.仿真结果表明,与现有文献结果相比,融合算法最优路径长度缩短了10.28%,收敛速度加快了6.55%,验证了所提出算法的有效性和优越性.     

基于模糊逻辑的机器人导航系统研究

由于未知环境下机器人导航容易出现死锁问题,设计了一种基于栅格的地图模型叫“数据栅格”,并在此基础上提出了一种基于行为的导航方法即“安全导航法”。数据栅格记录了周围环境中障碍物信息和机器人路径信息,安全导航法就是应用数据栅格技术来解决未知环境下机器人导航遇到的死锁问题。模糊逻辑用来设计和协调各种导航行为。仿真和实际环境的实验结果也证实了该方法的良好性能。     

基于双向搜索的改进蚁群路径规划算法

针对移动机器人在复杂地图环境中移动耗时长、易陷入局部最优等问题,设计了一种基于双向搜索的改进蚁群路径规划算法。基于K-means算法对地图预处理,量化地图的局部复杂度程度,并将局部环境信息融合到状态转移概率函数,使机器人优先选择在复杂程度小的区域进行寻优,减少路径拐点。设定双向搜索规则,改进启发函数,提高算法的局部方向搜索精度和全局搜索效率。针对蚁群算法中蚂蚁遇到U障碍物陷入死锁的问题,提出死锁判断系数,增加了有效蚂蚁的数量,进一步提高了算法性能。仿真结果表明所设计的算法在复杂地图环境中相较于传统蚁群算法移动机器人的路径搜索效率更高。     

足球机器人动态路径规划方法研究

针对机器人足球系统的高度实时性、不确定性,提出了一种基于统计预测的路径规划方法,该方法考虑到障碍物的速度大小和方向的不确定性,用数学统计的方法对障碍物的运动进行建模;机器人在运动过程中,根据得到的环境信息在机器视觉范围内建立预测窗口和避障窗口,在预测窗口内,机器人根据障碍物的信息建立障碍物的预测区域,在避障窗口内,机器人根据自身的位置与障碍物的预测区域,分别调用切线法或滚动窗口法进行路径规划;该方法属于局部路径规划方法,机器人在移动过程中需要不断更新环境信息来进行避障.     

基于双层微粒群优化的机器人全局路径规划

采用微粒群优化解决机器人全局路径规划问题,近年来得到国内外学者广泛关注,并已经取得丰硕的研究成果。但是,已有成果往往难以应用于含有密集障碍物的环境。针对解决含有密集障碍物环境的机器人全局路径规划问题,提出一种双层微粒群优化方法。该方法通过底层微粒群优化,得到若干最优路径;通过顶层微粒群优化,在这些最优路径附近局部搜索,从而得到机器人的全局最优路径;通过对不可行路径实施脱障操作,使其成为可行路径。将所提方法应用于多场景的机器人路径规划,并与已有方法进行比较。实验结果表明,该方法能够找到机器人的全局最优路径。     

基于传感器信息的水下机器人滚动路径规划

针对环境中障碍物为任意形状的水下机器人路径规划问题,提出了基于传感器信息的水下机器人滚动路径规划方法,该法充分利用传感器在每一采样时刻的规划窗口内实时探知的环境信息,用模糊控制方法给出机器人下一步的行走方法,探讨了凹形障碍区域中机器人的逃离方法,仿真结果表明了所提方法是有效的。     

模糊控制在移动机器人路径规划中的应用

针对移动机器人最优路径规划问题,设计了一种模糊智能控制方法。利用超声波传感器对机器人周围环境进行探测,得到关于障碍物和目标的信息。通过设计模糊控制器,把得到的障碍与目标位置信息模糊化,建立模糊规则并解模糊最终使机器人可以很好地避障,并且解决了模糊算法存在的死锁问题,从而实现了移动机器人的路径规划。仿真实验结果表明了模糊算法优于人工势场法,具有有效性和可行性。     

面向未知地图的六足机器人路径规划算法

针对移动机器人路径规划中无法准确得知全局地图的问题,提出了一种基于模糊规则和人工势场法的局部路径规划算法。首先,利用测距组与模糊规则,进行障碍物的形状分类,构建局部地图;其次,在人工势场法中引入了一种修正的斥力函数,基于局部地图,利用人工势场法进行局部路径规划;最后,随着机器人的运动,设置时间断点,以减少路径震荡。针对随机障碍物和凹凸障碍物的地图,分别采用传统人工势场法和改进的人工势场法进行仿真,其结果表明：在遇到随机障碍物时,相比传统人工势场法,改进的人工势场法能够显著减少与障碍物的碰撞;在遇到凹凸障碍物时,改进的人工势场法能够很好地完成路径规划的目标。所提算法对地形变化适应能力强,能够实现在未知地图下的六足机器人路径规划。     

Autonomous multi-mobile robot system: simulation and implementation using fuzzy logic

In an autonomous multi-mobile robot environment, path planning and collision avoidance are important functions used to perform a given task collaboratively and cooperatively. This study considers these important and challenging problems. The proposed approach is based on a potential field method and fuzzy logic system. First, a global path planner selects the paths of the robots that minimize the potential value from each robot to its own target using a potential field. Then, a local path planner modifies the path and orientation from the global planner to avoid collisions with static and dynamic obstacles using a fuzzy logic system. In this paper, each robot independently selects its destination and considers other robots as dynamic obstacles, and there is no need to predict the motion of obstacles. This process continues until the corresponding target of each robot is found. To test this method, an autonomous multi-mobile robot simulator (AMMRS) is developed, and both simulation-based and experimental results are given. The results show that the path planning and collision avoidance strategies are effective and useful for multi-mobile robot systems.     

A 3-level autonomous mobile robot navigation system designed by using reasoning/search approaches

This paper describes how soft computing methodologies such as fuzzy logic, genetic algorithms and the Dempster–Shafer theory of evidence can be applied in a mobile robot navigation system. The navigation system that is considered has three navigation subsystems. The lower-level subsystem deals with the control of linear and angular volocities using a multivariable PI controller described with a full matrix. The position control of the mobile robot is at a medium level and is nonlinear. The nonlinear control design is implemented by a backstepping algorithm whose parameters are adjusted by a genetic algorithm. We propose a new extension of the controller mentioned, in order to rapidly decrease the control torques needed to achieve the desired position and orientation of the mobile robot. The high-level subsystem uses fuzzy logic and the Dempster–Shafer evidence theory to design a fusion of sensor data, map building, and path planning tasks. The fuzzy/evidence navigation based on the building of a local map, represented as an occupancy grid, with the time update is proven to be suitable for real-time applications. The path planning algorithm is based on a modified potential field method. In this algorithm, the fuzzy rules for selecting the relevant obstacles for robot motion are introduced. Also, suitable steps are taken to pull the robot out of the local minima. Particular attention is paid to detection of the robot’s trapped state and its avoidance. One of the main issues in this paper is to reduce the complexity of planning algorithms and minimize the cost of the search. The performance of the proposed system is investigated using a dynamic model of a mobile robot. Simulation results show a good quality of position tracking capabilities and obstacle avoidance behavior of the mobile robot.     

基于模糊Elman网络算法的移动机器人路径规划分析

为了调正移动机器人避障线路,建立了基于模糊Elman网络算法的移动机器人路径规划模型,并应用进行Matlab仿真分析。利用现有障碍物的距离信息来实现机器人步长的实施可控制与调节,防止移动机器人在做出准确避障行为之后因为没有设定合适的步长而导致撞上障碍物,以0.5作为机器人的最初运动步长。仿真结果表明,采用模糊Elman网络可以获得比其它两种方法更优的路径规划效果,同时对障碍物进行高效避让,由此实现最优的路径规划。采用模糊Elman网络来构建得到的路径规划算法能够满足规划任务的要求,同时还能够根据机器人处于不同工作空间中的情况进行灵活调整。     

Mobile robot navigation using motor schema and fuzzy context dependent behavior modulation

This paper presents a novel technique to autonomously select different motor schemas using fuzzy context dependant blending of robot behaviors for navigation. First, a set of motor schemas is formed as behaviors. Both strategic and reactive type schemas have been employed in order to facilitate both the aspects of global and local motion planning. While strategic schemas are formed using the prior knowledge of the environment, the reactive schemas are activated using current sensory data of the robot. For global path planning, a safe path is first created using a Voronoi diagram. For local planning, the Voronoi vertices are treated as immediate subgoals and are used to form schemas leading to achieve optimized traveled distance and goal oriented robot navigation. Two motor schemas are formed as reactive behaviors for obstacle avoidance. The unknown obstacles are modeled using the sensory data. The coordinated behavior is achieved while employing weighed vector summation of the schemas. The adaptation of weights are achieved through a fuzzy inference system where fuzzy rules are used to dynamically generate the weights during navigation. A novel approach is proposed for fuzzy context-dependent blending of schemas. Fuzzy rules are formed using two main criteria into account: the first criterion reasons out the context dependent activity of a schema for achieving goal and the second criterion reasons out cooperative activity of strategic schemas with high priority reactive schemas. Comprehensive results validate that the proposed technique eliminates the existing drawbacks of motor schema approaches available in literature and provides collision free goal oriented robot navigation.