Lyapunov function-based obstacle avoidance scheme for a two-wheeled mobile robot

An obstacle avoidance scheme of a two-wheeled mobile robot is shown by selecting an appropriate Lya- punov function. When considering the obstacle, the Lyapunov function may have some local minima. A method which erases the local minima is proposed by using a function which covers the minima with a plane surface. The effectiveness of the proposed method is verified by numerical simulations.     

一种新的智能避障转向控制方法

针对智能探测车的转向系统建立精确的数学模型十分困难,且其航向受诸多因素干扰问题,研究了预测控制中的动态矩阵法,提出了一种新的智能探测车转向机构模型。该方法用障碍物的方向和距离偏差作为参数,在预测控制算法中采用动态矩阵控制,输出前轮转向角。再将转向角预测量与实际转向角之和作为反馈,对转向角变化趋势做出预测。通过仿真验证了在不同纵向速度下,加入相同干扰时的系统响应。结果表明：该算法在智能探测车障碍物避让控制中,对外界环境的干扰具有较强的鲁棒性,能够满足智能探测车转向控制要求。     

Potential-based obstacle avoidance in formation control

Based on the double integrator mathematic model, a new kind of potential function is presented in this paper by referring to the concepts of the electric field; then a new formation control method is proposed, in which the potential functions are used between agent-agent and between agent-obstacle, while state feedback control is applied for the agent and its goal. This strategy makes the whole potential field simpler and helps avoid some local minima. The stability of this combination of potential functions and state feedback control is proven. Some simulations are presented to show the rationality of this control method.     

基于非线性函数的移动机器人模糊避障算法

根据模糊逻辑的特点,利用非线性函数对传统的模糊控制算法的隶属度函数进行了改进.通过MATLAB给出的仿真证明,在采用多传感器避障过程中,机器人不仅能成功避开障碍物达到目标所在位置,并且非线性函数隶属度函数优于传统隶属度函数.新算法使机器人的行进更加平滑、稳定且具有较高的实时性.     

产生规避障碍物的导航力的解决方案*

给出了一种规避障碍物的导航力的实现方案,这种方案在进行相交检测时使用了一种高效而简单的排除法,大大提高了计算效率.在计算导航力时使用模糊逻辑,使自治角色在规避障碍物时表现得更加智能.     

非完整移动机械臂的避障运动规划

针对有空间障碍物避免的移动式操作机器人系统运动规划问题，提出了一种基于特殊的人工势函数，使用局部距离信息实现非完整移动机械臂系统实时避障运动规划方法，并且用Lyapunov定理证明了闲环系统的稳定性。用提出的方法对非完整移动机械臂系统进行仿真．仿真结果表明了它的正确有效性。     

A method for guidance and control of an autonomous vehicle in problems of border patrolling and obstacle avoidance

We present a sliding mode based method for a unicycle-like vehicle control and guidance. The proposed guidance-control law is applied to the problems of border patrolling and obstacle avoidance. A mathematically rigorous analysis of this law is provided.     

基于模糊控制的移动机器人避障研究

针对移动机器人在未知环境中的不确定性,利用Matlab构建了多传感器仿真试验移动平台,在Simulink中搭建移动机器人运动学模型,利用多传感器采集环境中的障碍物信息与目标物的方位角,设计了具有避障功能的模糊控制算法.通过模糊控制器控制移动机器人的左右轮速度实现机器人的转弯以及直走,根据机器人实时的角度反馈信息不断修正机器人的位姿以精确避障.仿真实验验证了该方法的可行性及有效性.     

采用Kinect的移动机器人目标跟踪与避障

为实现移动机器人在目标跟踪的同时进行避障,采用Kinect代替传统的测距雷达和摄像机.针对Kinect在使用中存在盲区和噪声的问题,提出一种基于统计的局部地图更新方法,利用动态更新的局部地图保存可能影响机器人运动的障碍物信息,并通过统计信息来消除测距噪声的影响,确保障碍物信息的有效性.同时使用增加安全区域的人工势场法去除对移动机器人运动无干扰的障碍物信息,改善了传统人工势场法通过狭窄通道的能力.在差动驱动移动机器人的实验证实了此系统能够很好地完成跟踪与避障任务,结果表明,使用Kinect可以代替传统测距传感器.     

双轮移动机器人安全目标追踪与自动避障算法

设计了双轮移动机器人安全目标追踪算法和双回路的追踪与避障控制方案.内层控制回路是目标追踪的控制律,用来指导机器人追踪到指定目标并保持一定的安全距离,而且兼顾了机器人在运行速度上的限制和追踪的时间效率,其控制的渐近稳定性用Lyapunov函数法进行了证明.当遇到障碍物时,外层控制回路根据超声传感器的信息和阻抗控制的概念产生阻抗虚拟力,将期望目标调整到虚拟位置,使机器人能够自动转向以避开障碍物.仿真研究和实验结果证明了追踪算法的有效性和避障方法的可行性.     

Obstacle avoidance control of redundant robots using variants of particle swarm optimization

Four variants of Particle Swarm Optimization (PSO) are proposed to solve the obstacle avoidance control problem of redundant robots. The study involved simulating the performance of a 5 degree-of-freedom (DOF) robot manipulator in an environment with static obstacle. The robot manipulator is required to move from one position to a desired goal position with minimum error while avoiding collision with obstacles in the workspace. The four variants of PSO are namely PSO-W, PSO-C, qPSO-W and qPSO-C where the latter two algorithms are hybrid version of the first two. The hybrid PSO is created by incorporating quadratic approximation operator (QA) alongside velocity update routine in updating particles' position. The computational results reveal that PSO-W yields better performance in terms of faster convergence and accuracy.     

基于Matlab的机器人避障仿真软件设计

为了提高机器人避障算法开发效率,设计了一种基于Matlab的机器人避障算法设计与试验仿真软件。该软件分为用户图形界面、避障算法系统模型两大部分。其中用户图形界面是基于Matlab 图形用户界面( GUI)实现的,能够实现参数设置、结果展示等功能,给用户提供了一个操作及管理平台;避障算法系统模型是本软件实现的关键,包括探测传感器模拟和机器人运动模拟,其通过Matlab语言编程和Simulink模块组建实现。全面阐述了仿真软件的系统建模、软件编程和用户图形界面设计,并对仿真软件实现所采用的关键技术进行了详细说明。仿真实例表明,该软件运行平稳,能清晰直观地展现机器人的避障过程。     

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.     

The SDRE control of mobile base cooperative manipulators: Collision free path planning and moving obstacle avoidance

This work proposes application of a state-dependent Riccati equation (SDRE) controller for wheeled mobile cooperative manipulators. Implementation of the SDRE on a wheeled mobile manipulator (WMM) considering holonomic and non-holonomic constraints is difficult and leads to instability of the system. The present study introduces a method of controlling the WMMs including: a general formulation, state-dependent coefficient parameterization, and control structure of the SDRE. Overcoming the problem of instability of the WMM resulted in control design for a system of cooperative manipulators mounted on a wheeled mobile platform. Optimal load distribution (OLD) was employed to distribute the load between the cooperative arms. The presence of obstacles and the probability of a collision between multiple robots in a workspace are the motivations behind employment of the artificial potential field (APF) approach. Two cooperative manipulators mounted on a mobile platform retrieved from Scout robot were modeled and simulated for situations such as controlling multiple mobile bases (collision avoidance), a cooperative system of manipulators, and moving obstacle avoidance. The OLD improved the load capacity, precision, and stability in motion of the cooperative system. Compatibility of the APF within the structure of the SDRE controller is another promising aspect of this research.     

基于滚动速度障碍法的无人机山地航测避障路径规划研究

无人机在进行山地航测时,经常遭遇鸟类等动态障碍,若不能及时规避掉障碍,极容易发生坠机事故。为此,研究一种基于滚动速度障碍法的无人机山地航测避障路径规划方法。基于山地环境模型,结合飞行路径长度、路径平滑度建立一个综合目标函数并利用改进布谷鸟搜索算法求解,得到无人机山地航测的初始路径。对图像进行预处理后,识别无人机初始路径飞行过程中遇到的障碍物,并通过超声波测量无人机与障碍物之间的距离,以此建立速度障碍模型,实现速度障碍碰撞分析,通过滚动窗口的方式确定无人机与障碍物是否存在飞行冲突。基于滚动速度障碍避障方法实现滚动角度避障和速度避障,获取最终的优化路径,完成基于滚动速度障碍法的无人机山地航测避障路径规划。测试结果表明:航测避障路径长度为571.45m,平滑度为165.52,规划的方案更具合理性。     

Neural network approach to acquiring free-gait motion of quadruped robots in obstacle avoidance

In obstacle avoidance by a legged mobile robot, it is not necessary to avoid all of the obstacles by turning only, because it can climb or stride over some of them, depending on the obstacle configuration and the state of the robot, unlike a wheel-type or a crawler-type robot. It is thought that mobility efficiency to a destination is improved by crawling over or striding over obstacles. Moreover, if robots have many legs, like 4-legged or 6-legged types, then the robot's movement range is affected by the order of the swing leg. In this article a neural network (NN) is used to determine the action of a quadruped robot in an obstacle-avoiding situation by using information about the destination, the obstacle configuration, and the robot's self-state. To acquire a free gait in static walking, the order of the swing leg is realized using an alternative NN whose inputs are the amount of movement and the robot's self-state. The design parameters of the former NN are adjusted by a genetic algorithm (GA) off-line. This work was presented in part at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004     

基于动态目标位置的智能车辆动态避障控制研究

为了真实地模拟驾驶员在动态环境中避让动态障碍物的行为方式,提出了动态目标位置概念,并采用三次样条曲线作为动态避障的路径拟合曲线。以模糊逻辑为控制策略,以T-S模糊模型为控制结构,以自适应神经网络为隶属度函数的参数调整手段,设计出一种智能车辆横向运动控制器,并通过计算机仿真实现。结果表明,基于动态目标位置概念的控制器设计具有较好的控制性能,较为理想地模拟实际交通环境中车辆动态避障的特性。     

智能移动机器人的超声避障研究

智能移动机器人是机器人研究领域的重要方向,是当前机器人领域中最活跃的研究主题之一.在分析了智能移动机器人避障常用传感器的基础上,提出了基于多超声传感器的移动机器人的超声避障系统.介绍了超声避障系统的模糊控制规则和非模糊化,并给出了实验结果.实验结果表明,模糊控制机理和策略易于接受和理解,便于应用开发,模糊避障算法对环境有很大的适应性,机器人在不同的环境条件下实现了避障.     

多移动机器人的领航-跟随编队避障控制

针对多移动机器人的编队控制问题,提出了一种结合Polar Histogram避障法的领航-跟随协调编队控制算法。该算法在领航-跟随l-φ编队控制结构的基础上引入虚拟跟随机器人,将编队控制转化为跟随机器人对虚拟跟随机器人的轨迹跟踪控制。结合移动机器人自身传感器技术,在简单甚至复杂的环境下为机器人提供相应的路径运动策略,实现实时导航的目的。以两轮差动Qbot移动机器人为研究对象,搭建半实物仿真平台,进行仿真实验。仿真结果表明:该方法可以有效地实现多移动机器人协调编队和避障控制。     

Adaptive path planning and tracking system based on model predictive control for autonomous vehicle local obstacle avoidance

Path planning and tracking control are the key technologies of autonomous vehicle. The planned path and tracking results affect driving stability and safety directly. In this paper, an improved local path planning method based on model predictive control is proposed to match the variation of vehicle speed and road adhesion coefficient. A two-layer model predictive control (MPC) path planning and tracking system is further designed to validate the method and the simulation results show that the proposed solution solves the problem of excessive avoidance and reduces the lateral deviation with the reference path.