基于激光雷达机器人的目标人跟踪方法研究

针对机器人的目标人跟踪问题,提出一种利用目标人腿部信息自适应跟踪的方法。该方法利用激光雷达作为环境感知传感器,实时获取环境的二维激光扫描数据;然后采用设计的激光相邻点聚类算法对激光扫描数据进行分割和聚类;再利用人腿圆弧状特征设计的类圆弧人腿形状识别算法从分割的数据中识别腿部数据,获得目标人腿部相对于机器人的位置信息;最后利用Kalman滤波算法对目标人的位置和速度进行跟踪,使机器人能够平稳地跟踪目标人运动。该算法在iRobot机器人平台上进行实验,实验结果验证了算法的有效性。     

基于环境分层方法的双足机器人路径规划研究

为了实现双足机器人在障碍环境中的路径规划,提出一种将三维环境分层的方法,用两个截面将环境分为高于机器人身高障碍层、低于机器人抬脚高度障碍层和中间障碍层.首先在中间障碍层进行机器人轨迹规划,再根据机器人各种步态的不同损耗构建代价函数,把规划好的轨迹放到最底层进行规划修改,最终得到双足机器人在规划路径上代价最小的一系列连续的动作,通过计算机仿真实验验证了方法的有效性.     

机器人局部环境特征提取方法的研究

考虑到机器人导航过程中的实时性与可靠性要求,充分利用激光雷达信息的快速性与精确性,针对结构化环境中诸如墙壁、拐角、通道等这些典型环境特征分别设计了一套快速有效地特征提取的算法;另外算法还考虑到机器人建立环境地图的需要,在环境特征提取时对某一些密集的障碍物进行了合并,并注意保留了环境特征的一些拓扑信息,为建图作了一些前期准备,拿到移动机器人MORCS上进行实验获得了满意的结果,验证了算法的实时性与可靠性.     

基于激光雷达的移动机器人位姿估计方法综述

位姿估计是移动机器人研究的一个核心问题．本文综述了国内外基于激光雷达的移 动机器人位姿估计的最新进展，并对各种方法进行分类、比较和分析，从中归纳出应用中值 得注意的问题和发展趋势．     

基于改进蚁群算法的机器人路径规划

提出了一种复杂静态环境下的移动机器人避碰路径规划的改进蚁群算法,基于栅格法的工作空间模型,模拟蚂蚁的觅食行为;针对路径规划的需要,搜索过程采用了蚂蚁回退策略、目标吸引策略、参数自适应调整和路径优化策略;利用蚂蚁回退策略和惩罚函数使得蚂蚁能够顺利跳出陷阱,并且在下一次搜索中不再选择此路径,从而避免了遇到陷阱时形成的路径死锁情况,同时也提高了最优路径的搜索效率;仿真试验结果表明,该算法能迅速规划出最优路径。     

基于激光雷达的移动机器人障碍测距研究

障碍距离检测是移动机器人导航的关键问题之一。为了实现精确实时的障碍检测,针对某二维TOF激光雷达,对其数据标定、物体表面的属性、混合像素等因素进行试验,评估了其测距性能。同时,通过移动机器人运行过程中激光雷达的测距数据分析,设计了动态自适应滤波器以消除障碍检测中的测距噪声干扰。运行过程中的障碍检测试验表明:该方法可以实现可靠的障碍检测,并为移动机器人导航中环境建模、自定位及路径规划提供支持。     

基于模糊控制的自主机器人路径规划策略研究

结合人类的经验及模糊控制理论研究移动机器人的局部路径规划问题,采用步长的转向角控制的方法模拟驾车减速的自然过程,同时采用了虚拟目标点的方法处理局部路径规划中经常出现的陷阱问题．仿真结果验证了所提方法的有效性和可行性．     

基于双多线激光雷达的非结构化环境负障碍感知技术

负障碍感知是非结构化环境下的难点问题,本文针对该问题提出一种新的基于双多线激光雷达（Light detection and ranging,LiDAR）的感知方法.采用分布嵌入式架构对双激光雷达数据进行同步采集与实时处理,将雷达点云映射到多尺度栅格,统计栅格的点云密度与相对高度等特征并标记,从点云数据提取负障碍几何特征,通过将栅格的统计特征与负障碍的几何特征做多特征关联找到关键特征点对,将特征点对聚类并过滤,识别出负障碍.方法不受地面平整度影响,已成功应用在无人驾驶车上.使用表明该方法具有较高的实时性和可靠性,在非结构化环境下具有良好的感知效果.     

基于分层搜索的移动机器人路径优化

分析比较了经典的全局路径规划算法,针对移动机器人运动路径规划的优化问题,将A*、人工势场、栅格等多种算法的优势加以综合考虑,提出一种基于栅格的分层搜索概念。该方法采用栅格法进行建模,以分层搜索为核心思想,外层采用A*算法将复杂的图元模糊化以简化环境,内层则采用人工势场算法将具体的栅格还原以解模糊,具有一定的理论意义与应用前景。其仿真实验表明,该方法能达到较好的路径规划效率和实时性。     

Smooth transition for collision avoidance of redundant robots: An on-line polynomial approach

The presence of cooperation between robots and machines in the industrial environment improved the solution for several manufacturing problems. With cooperation, new challenges emerged, and among these stands out the collision avoidance between such robots and machines. Collision avoidance can be dealt with in several ways, taking into account the computational effort to make a decision and the quality of the calculated trajectory for the robots, evaluated, for instance, by smooth profiles avoiding sudden variations in joints’ velocities or acceleration. In these circumstances, the involved robots need to be redundant since new movements are necessary for avoiding collisions. The strategies for collision avoidance are offline (i.e., based on pre-programming the task), or online (i.e., implemented while the robot performs the main task). In online collision avoidance strategies, numerical performance must ensure the time requirements of the main task performed by the robot; so, numerically efficient solutions are the most appropriate. This paper presents a proposal for the collision avoidance treatment from fixed obstacles for redundant robots, based on polynomial functions. The proposed solution allows achieving smooth trajectories according to criteria based on the continuity of derivatives in trajectory curve transitions. When the robot is out of the imminent collision, it is proposed to solve the inverse kinematics through the Adaptive Extended Jacobians. Throughout the text, the mathematical developments based on polynomials are presented, and in the end, a case study graphically shows comparative results.     

Cooperative collision avoidance between multiple mobile robots

This paper presents a new collision avoidance technique, called cooperative collision avoidance, for multiple mobile robots. The detection of the danger of collision between two mobile robots is discussed with respect to the geometric aspects of their paths as are cooperative collision avoidance behaviors. The direction control command and the velocity control command for the cooperative collision avoidance are then proposed. The avoidance technique is extended to cases in which the number of mobile robots is more than two. Furthermore, the conditions for collision avoidance are considered with respect to the navigation parameters and guidelines of designing the navigation parameters are obtained. The effectiveness of the proposed technique is demonstrated by means of numerical simulation and navigation experiments using two real mobile robots named Pioneer‐1. ©2000 John Wiley & Sons, Inc.     

Multiple robots formation manoeuvring and collision avoidance strategy

This paper presents a multiple robots formation manoeuvring and its collision avoidance strategy. The direction priority sequential selection algorithm is employed to achieve the raw path, and a new algorithm is then proposed to calculate the turningcompliant waypoints supporting the multi-robot formation manoeuvre. The collision avoidance strategy based on the formation control is presented to translate the collision avoidance problem into the stability problem of the formation. The extension-decompositionaggregation scheme is next applied to solve the formation control problem and subsequently achieve the collision avoidance during the formation manoeuvre. Simulation study finally shows that the collision avoidance problem can be conveniently solved if the stability of the constructed formation including unidentified objects can be satisfied.     

基于改进速度障碍法的多机器人避碰规划算法

针对多移动机器人运动协调中的动态安全避碰问题,在分析速度障碍法原理的基础上,设计用于机器人之间相互避让的互动速度法则,并通过制定机器人的碰撞时间、碰撞距离因子对构型障碍的大小进行实时调整,把运动障碍物、动力学约束下的多步可达窗口、目标点都映射到一种速度变化空间当中,使多机器人的动态避碰问题转化为一种最优化问题,并构造了新的优化评价函数;设计了基于改进速度障碍法的机器人动态避碰规划算法。仿真实验表明,该方法有效地克服了碰撞冲突,实现了多机器人之间的运动协调控制,提高了机器人追踪运动目标的快速性。     

The dynamic window approach to collision avoidance

This approach, designed for mobile robots equipped with synchro-drives, is derived directly from the motion dynamics of the robot. In experiments, the dynamic window approach safely controlled the mobile robot RHINO at speeds of up to 95 cm/sec, in populated and dynamic environments     

基于预测窗的轮式移动机器人最优避障避碰算法

针对非线性轮式移动机器人的避障以及多机器人间的相互避碰问题,提出了一种基于预测窗的避障避碰算法.首先为了便于预测碰撞的发生,通过反馈线性化将非线性的机器人运动学模型转化成线性模型;然后根据线性模型预测会导致机器人发生碰撞的所有相对虚拟加速度变化量集合,称之为加速度变化障碍.基于此,为每个机器人构造既能躲避障碍物又能相互避碰的可行加速度变化集合.然后通过优化指标函数求得最优虚拟加速度变化量,最后将其转换成机器人的实际控制量.这种算法与现有的相比,可使机器人在避障或避碰过程中的行驶方向角、线速度的变化幅值更小,角速度和线加速度的变化更为平顺,而且运行所用的平均时间更短.仿真结果演示了所提出算法的有效性和相对于已有方法的优势.     

Distributed robust MPC for nonholonomic robots with obstacle and collision avoidance

Considering that the inevitable disturbances and coupled constraints pose an ongoing challenge to distributed control algorithms, this paper proposes a distributed robust model predictive control (MPC) algorithm for a multi-agent system with additive external disturbances and obstacle and collision avoidance constraints. In particular, all the agents are allowed to solve optimization problems simultaneously at each time step to obtain their control inputs, and the obstacle and collision avoidance are accomplished in the context of full-dimensional controlled objects and obstacles. To achieve the collision avoidance between agents in the distributed framework, an assumed state trajectory is introduced for each agent which is transmitted to its neighbors to construct the polyhedral over-approximations of it. Then the polyhedral over-approximations of the agent and the obstacles are used to smoothly reformulate the original nonconvex obstacle and collision avoidance constraints. And a compatibility constraint is designed to restrict the deviation between the predicted and assumed trajectories. Moreover, recursive feasibility of each local MPC optimization problem with all these constraints derived and input-to-state stability of the closed-loop system can be ensured through a sufficient condition on controller parameters. Finally, simulations with four agents and two obstacles demonstrate the efficiency of the proposed algorithm.     

VCS-based motion planning for distributed mobile robots: collision avoidance and formation

This paper addresses decentralized motion planning among a homogeneous set of feedback-controlled mobile robots. It introduces the velocity obstacle, which describes the collision between robot and obstacle, and the hybrid interactive velocity obstacles are designed for collision checking between interacting robots. The (sub)goal selection algorithm is also studied for formation control, then the preferred velocity is designed for robot tracking its desired (sub)goal. Furthermore, the rules for the size regulation of obstacle are presented to avoid conservative motion planning and enhance the safety. Then, we establish a novel Velocity Change Space (VCS), map the velocity obstacles, the desired (sub)goal and the reachable velocity change window before collision in this space, and directly get the new velocity by a multi-objective optimization method. We apply VCS-based motion planning methods to distributed robots, and simulation is used to illustrate the good performances with respect to the un-conservative, foresighted and multi-objective optimal motion planning, especially the successful application in the formation control of the multi-robot system.     

Fuzzy neural networks for obstacle pattern recognition and collision avoidance of fish robots

The problems of detection and pattern recognition of obstacles are the most important concerns for fish robots’ path planning to make natural and smooth movements as well as to avoid collision. We can get better control results of fish robot trajectories if we obtain more information in detail about obstacle shapes. The method employing only simple distance measuring IR sensors without cameras and image processing is proposed. The capability of a fish robot to recognize the features of an obstacle to avoid collision is improved using neuro-fuzzy inferences. Approaching angles of the fish robot to an obstacle as well as the evident features such as obstacles’ sizes and shape angles are obtained through neural network training algorithms based on the scanned data. Experimental results show the successful path control of the fish robot without hitting on obstacles.     

A constraint-based approach for human–robot collision avoidance

In this paper, we present a software-based approach for collision avoidance that can be applied in human–robot collaboration scenarios. One of the contributions is a method for converting clustered 3D sensor data into computationally efficient convex hull representations used for robot-obstacle distance computation. Based on the computed distance vectors, we generate collision avoidance motions using a potential field approach and integrate them with other simultaneously running robot tasks in a constraint-based control framework. In order to improve control performance, we apply evolutionary techniques for parameter optimization within this framework based on selected quality criteria. Experiments are performed on a dual-arm robotic system equipped with several depth cameras. The approach is able to generate task-compliant avoidance motions in dynamic environments with high performance.