小型移动机器人自主返航路径规划方法

针对遥控小型移动机器人在自主返航实际应用中定位精度低等问题,提出一种小型移动机器人自主返航路径规划方法.介绍小型移动机器人的任务流程及硬件系统,利用膨胀算子对栅格地图中的障碍物进行运算得到栅格Voronoi图.使用双边界路径矢量化方法从栅格Voronoi图中提取出矢量路径,并对该路径进行拓扑优化.通过Dijkstra算法对拓扑路径进行路径规划并进行算法验证.实验结果表明,该方法所得路径可使环境中的机器人与障碍物之间的距离最大化,并使移动机器人的运动轨迹具有较高的可执行性,提高了小型移动机器人自主返航的成功率.     

基于障碍物运动预测的移动机器人路径规划

针对移动机器人局部动态避障路径规划问题开展优化研究。基于动态障碍物当前历史位置轨迹,提出动态障碍物运动趋势预测算法。在移动机器人的动态避障路径规划过程中,考虑障碍物当前的位置,评估动态障碍物的移动轨迹;提出改进的D*Lite路径规划算法,大幅提升机器人动态避障算法的效率与安全性。搭建仿真验证环境,给出典型的单动态障碍物、多动态障碍物场景,对比验证了避障路径规划算法的有效性。     

基于行为的实时路径规划

针对室内移动机器人导航要求,开发了以二维激光雷达作为探测环境的传感器,基于4个反应式行为,设计了一种简单的实时路径规划算法.避障行为使机器人穿过狭小通道,或者在某些障碍物环境下绕出狭窄区域;接近行为使机器人顺着障碍物前进直到开阔地带;搜寻行为使机器人不断朝向目标运动;线性行为使机器人到达目标点.机器人表现出很强的路径寻找能力,并且不需要定位信息.仿真实验表明该算法速度快,实时性好,路径平滑无震荡,实现了有效避障.     

基于环境预测的步进式运动协调策略

为了解决未知动态环境下的多机器人系统的路径规划问题,在改进的势场栅格法的基础上,提出了一种基于环境预测的步进式多机器人运动协调策略。针对机器人躲避移动障碍物的避障运动和机器人之间避碰运动的不同,分别提出了协调策略Ⅰ和协调策略Ⅱ。为了验证协调策略的有效性,将该方法用于多机器人系统的路径规划中,并利用Visual C++进行仿真。仿真结果表明,用该方法进行路径规划是可行的和有效的,并且算法简单、计算量小。     

基于双目视觉和Voronoi图的移动机器人地图构建研究

在双目视觉传感器的基础上,应用改进后的SIFT(Scale-invariant feature transform)算法确定障碍物的三维坐标,提出最大包围盒的思想,将处理后的数据作为Voronoi图的建模生成元,完成移动机器人的全局地图构建;基于V图构建的地图的最大的优点是移动机器人行走的路径是障碍物离散中心的中垂线,可以保证机器人在运行过程中最大程度地远离障碍物;试验结果表明应用该方法构建的地图可靠性高,符合导航的要求。     

基于换位原理与模糊推理的障碍物动态意图预测的研究

碰撞规避问题是多移动机器人路径规划中的关键技术,它是实现两个或多个机器人的无碰撞运动,足球机器人路径规划是在未知环境下的动态碰撞避问题。讨论利用换位原理,根据当前状态,预测障碍物的动态意图,将意图信息量进行模糊划分,通过模糊推理,从而确定足球机器人的路径取向。文中最后给出基于换位原理和模糊推理的碰撞规避算法和仿真结果。     

救援机器人搜索目标路径环境快速建模仿真

为了使救援机器人在灾害发生救援过程中更好地了解环境空间信息,规划出一条最优搜索目标路径,提出救援机器人搜索目标路径环境快速建模方法.采用贝叶斯滤波器计算栅格占用率,获得静态、动态地图逆向传感器,使用URG-04LX暗光条件激光雷达,得到不同方向上障碍物位置信息构成的扇形平面,并利用拓扑方法处理地图,准确体现出障碍物位置关系信息,依据基于主动生长的栅格Voronoi图生成现场环境模型.仿真结果表明,利用所提方法设计的实验机器人在行驶的过程中没有碰到任何障碍物,说明上述环境模型对环境区域描述精准度比较高.     

多障碍环境中移动机器人的光滑轨迹规划

根据轮式移动机器人参数化轨迹生成模型,结合多障碍物结构化环境中障碍物的建模,把其和参数化轨迹规划模型融合,得到了具有一般性的多障碍物环境中轮式移动机器人光滑轨迹规划模型;并利用最优化控制原理,建立了任意性能指标下,多障碍物环境中最优参数化轨迹生成模型。结合数值求解方法,推导了多障碍物环境中参数化轨迹规划非线性求解模型的求解方法。最后通过仿真验证了参数化轨迹规划求解模型的正确性。     

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。     

异构多目标差分-动态窗口算法 及其在移动机器人中的应用

为了实现在多移动机器人和多窄通道的复杂动态环境中机器人的节能运动规划,提出异构多目标差分-动态窗口法(heterogeneous multi-objective differential evolution-dynamic window algorithm,HMODE-DWA).首先,建立行驶时间、执行器作用力和平滑度的3目标优化模型,设计具有碰撞约束的异构多目标差分进化算法来获得3个目标函数的最优解,进而在已知的静态环境中获得帕累托前沿,利用平均隶属度函数获得起点与终点间最优的全局路径;其次,定义基于环境缓冲区域的模糊动态窗口法使机器人完成动态复杂环境中避障,利用所提出的HMODE-DWA算法动态避障的同时实现节能规划.仿真和实验结果表明,所提出的混合路径规划控制策略能够有效降低移动机器人动态避障过程中的能耗.     

Learning real-time search on c-space GVDs

In the context of robotics, configuration space (cspace) is widely used for non-circular robots to engage tasks such as path planning, collision check, and motion planning. In many real-time applications, it is important for a robot to give a quick response to the user’s command. Therefore, a constant bound on planning time per action is severely imposed. However, existing search algorithms used in c-space gain first move lags which vary with the size of the underlying problem. Furthermore, applying real-time search algorithms on c-space maps often causes the robots being trapped within local minima. In order to solve the above mentioned problems, we extend the learning real-time search (LRTS) algorithm to search on a set of c-space generalized Voronoi diagrams (c-space GVDs), helping the robots to incrementally plan a path, to efficiently avoid local minima, and to execute fast movement. In our work, an incremental algorithm is firstly proposed to build and represent the c-space maps in Boolean vectors. Then, the method of detecting grid-based GVDs from the c-space maps is further discussed. Based on the c-space GVDs, details of the LRTS and its implementation considerations are studied. The resulting experiments and analysis show that, using LRTS to search on the c-space GVDs can 1) gain smaller and constant first move lags which is independent of the problem size; 2) gain maximal clearance from obstacles so that collision checks are much reduced; 3) avoid local minima and thus prevent the robot from visually unrealistic scratching.     

基于人工协调场的多移动机器人实时协调避碰规划

为克服传统人工势场在动态未知环境下机器人避碰规划中存在的缺陷,提出人工协调场法(ACF).将场函数与机器人的风险状态相结合,给出并讨论了人工协调场的基本设计.基于人工协调场,考虑机器人的运动约束,实现了多机器人之间以及机器人与环境间的实时协调避碰,提出了一个多移动机器人无死锁实时避碰规划算法.理论分析和仿真试验证明所提方法的有效性.     

基于模糊神经网络的多移动机器人自学习协调系统

研究多移动机器人的运动规划问题．针对机器人模型 未知或不精确以及环境的动态变化,提出一种自学习模糊控制器（FLC）来进行准确的速度 跟踪．首先通过神经网络的学习训练构造FLC,再由再励学习算法来在线调节FLC的输出,以 校正机器人运动状态,实现安全协调避撞．     

A Complete and Scalable Strategy for Coordinating Multiple Robots Within Roadmaps

This paper addresses the challenging problem of finding collision-free trajectories for many robots moving toward individual goals within a common environment. Most popular algorithms for multirobot planning manage the complexity of the problem by planning trajectories for robots individually; such decoupled methods are not guaranteed to find a solution if one exists. In contrast, this paper describes a multiphase approach to the planning problem that uses a graph and spanning tree representation to create and maintain obstacle-free paths through the environment for each robot to reach its goal. The resulting algorithm guarantees a solution for a well-defined number of robots in a common environment. The computational cost is shown to be scalable with complexity linear in the number of the robots, and demonstrated by solving the planning problem for 100 robots, simulated in an underground mine environment, in less than 1.5 s with a 1.5 GHz processor. The practicality of the algorithm is demonstrated in a real-world application requiring coordinated motion planning of multiple physical robots.     

Coordinating multiple groups of robots for gaiting process

Coordinated robots are categorized into different groups when the coordination involves robot interchange or heterogeneous motion during the manipulation process. We investigated problems related to the control and coordination of multiple groups of robots and developed a general strategy and control algorithm for controlling one group of robots. We developed a method for determining the force distribution among the participating robots in a frictional equilibrium case. The control algorithm and the force determination procedure can be adopted in coordinating multiple groups of robots. Scenarios and restrictions for coordinating two groups of robots in turning a disk continuously are discussed. An example is given to illustrate the gaiting process. © 1996 John Wiley & Sons, Inc.     

A Minimum-Jerk Speed-Planning Algorithm for Coordinated Planning and Control of Automated Assembly Manufacturing

We previously proposed a general algorithm for coordinating the motions among multiple machines in a shared assembly environment based on a constant-speed motion model. In this paper, we extend this work to a minimum-jerk polynomial motion model and describe a new speed-planning algorithm to plan automated assembly machines' motions. Machines are planned sequentially, based on their priorities, by mapping the motions of higher-priority machines into forbidden regions in two-dimensional space-time graphs. Collision-free minimum-jerk motions are then planned between the forbidden regions in the graphs. The new speed-planning algorithm is evaluated on a dual-robot surface-mount technology assembly machine in which both robots share a common workspace. Note to Practitioners—Automated assembly processes, especially surface-mount technology manufacturing, require a high degree of precision when placing certain components. This motivated us to find a way of maintaining good positional accuracy by planning smooth motions for the machines that perform these tasks. Since many of these machines have two or more robots, their motions must also be coordinated. We developed an algorithm that combines coordinated motion concepts with a minimum-jerk motion model that can solve these problems. The algorithm plans segmented paths for the robots and then sequentially plans their speeds to prevent collisions between them. The planned speeds ensure position, velocity, and acceleration continuity between path segments. The smooth motions resulting from this method enable high-accuracy component placement. The tradeoff for this improvement is increased cycle time compared to other speed-planning methods.     

Incremental reconstruction of generalized Voronoi diagrams on grids

We present an incremental algorithm for constructing and reconstructing Generalized Voronoi Diagrams (GVDs) on grids. Our algorithm, Dynamic Brushfire, uses techniques from the path planning community to efficiently update GVDs when the underlying environment changes or when new information concerning the environment is received. Dynamic Brushfire is an order of magnitude more efficient than current approaches. In this paper we present the algorithm, compare it to current approaches on several experimental domains involving both simulated and real data, and demonstrate its usefulness for multirobot path planning.     

Efficient grid-based spatial representations for robot navigation in dynamic environments

In robotics, grid maps are often used for solving tasks like collision checking, path planning, and localization. Many approaches to these problems use Euclidean distance maps (DMs), generalized Voronoi diagrams (GVDs), or configuration space (c-space) maps. A key challenge for their application in dynamic environments is the efficient update after potential changes due to moving obstacles or when mapping a previously unknown area. To this end, this paper presents novel algorithms that perform incremental updates that only visit cells affected by changes. Furthermore, we propose incremental update algorithms for DMs and GVDs in the configuration space of non-circular robots. These approaches can be used to implement highly efficient collision checking and holonomic path planning for these platforms. Our c-space representations benefit from parallelization on multi-core CPUs and can also be integrated with other state-of-the-art path planners such as rapidly-exploring random trees.In various experiments using real-world data we show that our update strategies for DMs and GVDs require substantially less cell visits and computation time compared to previous approaches. Furthermore, we demonstrate that our GVD algorithm deals better with non-convex structures, such as indoor areas. All our algorithms consider actual Euclidean distances rather than grid steps and are easy to implement. An open source implementation is available online.     

Conflict free coordinated path planning for multiple robots using a dynamic path modification sequence

In this paper, a practically viable approach for conflict free, coordinated motion planning of multiple robots is proposed. The presented approach is a two phase decoupled method that can provide the desired coordination among the participating robots in offline mode. In the first phase, the collision free path with respect to stationary obstacles for each robot is obtained by employing an A* algorithm. In the second phase, the coordination among multiple robots is achieved by resolving conflicts based on a path modification approach. The paths of conflicting robots are modified based on their position in a dynamically computed path modification sequence (PMS). To assess the effectiveness of the developed methodology, the coordination among robots is also achieved by different strategies such as fixed priority sequence allotment for motion of each robot, reduction in the velocities of joints of the robot, and introduction of delay in starting of each robot. The performance is assessed in terms of the length of path traversed by each robot, time taken by the robot to realize the task and computational time. The effectiveness of the proposed approach for multi-robot motion planning is demonstrated with two case studies that considered the tasks with three and four robots. The results obtained from realistic simulation of multi-robot environment demonstrate that the proposed approach assures rapid, concurrent and conflict free coordinated path planning for multiple robots.     

Coordination of multiple mobile robots with limited communication range in pursuit of single mobile target in cluttered environment

This paper addresses the problem of coordinating multiple mobile robots in searching for and capturing a mobile target, with the aim of reducing the capture time. Compared with the previous algorithms, we assume that the target can be detected by any robot and captured successfully by two or more robots. In this paper, we assume that each robot has a limited communication range. We maintain the robots within a mobile network to guarantee the successful capture. In addition, the motion of the target is modeled and incorporated into directing the motion of the robots to reduce the capture time. A coordination algorithm considering both aspects is proposed. This algorithm can greatly reduce the expected time of capturing the mobile target. Finally, we validate the algorithm by the simulations and experiments.