横摆力矩和主动前轮转向结合的车辆横向稳定性模糊控制仿真

提出一种基于横摆力矩和主动前轮转向相结合的车辆横向稳定性控制方法,以横摆角速度和侧偏角为控制目标,利用前馈补偿和模糊控制产生横摆力矩和附加的前轮转角,通过控制制动力的分配以及对转向角的修正,使车辆转向行驶时的横摆角速度和侧偏角很好地跟踪参考模型.对转向轮阶跃输入和正弦输入两种工况分别进行了仿真研究,采用横摆力矩和主动前轮转向相结合控制方法,车辆转向时的瞬态及稳态响应优于单独的横摆力矩控制,表明该方法能有效地控制车辆横摆角速度和侧偏角,提高车辆转向时的横向稳定性,同时能有效地减轻驾驶员操纵负担.     

单目视觉导航智能车辆的自定位方法

提出了一种单目视觉导航智能车辆的自定位方法,采用"两步法"对摄像头进行标定;推导出智能车辆位姿的参数表达式,采用基于一点的Hough变换方法提取导航路径信息,计算出导航路径的偏转角β和横向偏移距离d;通过仿真实验进行了验证.实验结果表明,偏转角β的最大误差在1.1°之内,横向偏移距离d的最大误差不超过3.2cm,满足智能车辆的自定位要求.     

三轴转向车辆侧向稳定性控制策略分析

关于汽车转向稳定性能优化问题,根据三轴转向车辆线性二自由度动力学模型,推导了基于质心零侧偏角控制策略的三轴转向车辆各动力学参数与转向中心纵向位移的关系;结合整车侧向稳定性条件,通过分析转向中心纵向位移对整车侧向稳定性和转向灵活性的影响,得出了转向中心纵向位移临界值和前轮过渡角的表示公式;根据整车侧向稳定性条件和适度质心侧偏角条件,提出了质心零侧偏角调度控制策略;利用MATLAB对质心零侧偏角控制策略和质心零侧偏角调度控制策略进行了定量对比分析,结果证明理论分析的一致性.     

轨道车辆防全向错位前面板防爬器性能仿真

运用LS DYNA对一种新型防全向错位前面板防爬器抑制横向滑移和垂向爬升的能力进行仿真,通过模拟2节编组列车车辆间水平初始偏转为2°和3°的碰撞工况,分析比较传统前面板防爬器和新型防全向错位前面板防爬器对横向运动的抑制能力。在此基础上,仿真分析车辆间初始垂向偏移分别为20 和40 mm的碰撞工况,检验新型防爬器的垂向防爬能力。仿真结果表明,在碰撞工况下,新型防全向错位前面板防爬器的横向运动和垂向运动抑制能力均优于传统面板防爬器,可在车辆设计中推广使用。     

考虑高速公路横向坡道转向行驶操纵稳定性的汽车轴距和轮距分析

为分析汽车轴距和轮距设计对操纵稳定性的影响,建立高速公路横向坡道转向行驶的汽车转向动力学模型,并在MATLAB/Simulink软件中建立相应的仿真模型.采用某型汽车设计轴距和轮距进行仿真,得到以不同速度在不同横向坡度道路上转向行驶时的横摆角速度、侧向加速度和质心侧偏角.根据该型汽车的转向特性和侧翻阈值评价其在高速公路横向坡道转向行驶时的操纵稳定性,结果表明该型汽车的设计轴距和轮距满足操纵稳定性要求.计算方法和仿真结果对整车设计具有指导意义.     

四轮线控转向系统的转向控制策略研究

为了控制汽车的质心侧偏角,同时保持汽车的转向增益不变,研究了四轮线控转向系统的后轮转向控制策略和前轮转向控制策略.首先建立了四轮转向整车二自由度模型,然后基于稳态质心侧偏角为零得到两种后轮转向控制策略:与前轮转角成比例型和横摆角速度反馈型,前者不改变系统极点,后者改变系统极点.基于转向增益不随车速改变得到二者的前轮转向控制策略.仿真表明,提出的前轮转向控制策略可以保持固定转向增益,降低驾驶员负担;后轮转向控制策略可以实现零质心侧偏角稳态值,控制车辆姿态,改善操纵稳定性.     

基于UIO的EPS系统状态反馈最优控制

准确地获知电动助力转向（Electric powering steering,EPS）系统阻力矩是提高行车安全的一个重要因素.针对车辆转向过程中,由不同附着路面上EPS 系统所需辅助力矩与转向路感之间的差别而可能导致的误操纵问题,本文基于2自由度整车动力学的EPS系统模型,结合轮胎特性,以轮胎侧偏角和理想路面附着系数为输入,通过设计非线性观测器估计当前路面的附着系数,以获取EPS系统阻力矩;进而,根据EPS 系统模型,运用未知输入观测器（Unknown input observer,UIO）估算方向盘输入转矩,并基于EPS系统状态反馈以实现对EPS系统的无传感器最优控制.最后,对基于永磁同步电机（Permanent magnet synchronous motor,PMSM）的EPS系统进行仿真实验分析.结果表明: 在以电机q轴电流闭环误差最小为指标函数情形下,本设计的方向盘回正残留角从25°降到0°,能有效抑制系统外界干扰,提高了转向时人-车系统的鲁棒性.     

线控转向系统全状态反馈控制策略的研究

为了根据车况和驾驶员喜好实现最优的操纵特性(不足转向、过度转向和中性转向),需要主动控制前轮转角.线控转向系统利用车辆全状态(横摆角速度和质心侧偏角)反馈控制策略优化驾驶员的转向输入,主动控制前轮转角来优化车辆的转向特性,高速时具有适当的不足转向特性,低速时具有适当的过度转向特性,从而在反应快速和安全性之间很好的权衡.其中通过状态观测器估计侧偏角.结果表明,在紧急操纵时可以代替驾驶员协调使车辆保持稳定,正常操纵时补偿物理参数或操纵条件的变化而保持操纵特性的一致.     

线控转向系统的主动转向控制策略研究

线控转向系统取消了转向盘和转向轮之间的机械连接,可以对前轮进行主动转向控制以增强操纵稳定性和主动安全性.通过使前轮线控转向系统的期望横摆角速度跟踪稳态质心侧偏角为0的四轮转向车辆的横摆角速度,设计线控转向系统的变传动比,主动控制前轮转角.通过时域响应、转向增益、开环总方差等指标对其进行了性能分析.结果表明:采用提出的主动转向控制策略时稳态质心侧偏角大大降低,开环总方差大大降低,从而提高了汽车的操纵稳定性.     

基于U弦长曲率的抗旋转性广义Hough变换算法

针对广义Hough变换(GHT)算法匹配发生旋转图像中的目标形状时发生误匹配的问题,提出一种基于U弦长曲率的具有抗旋转性的广义Hough变换算法。首先,对模板形状采用边缘点的U弦长曲率和偏移向量等特征构建具有旋转不变性的修改的R-表;其次,以图像中边缘点的曲率作为索引,查找构建的R-表得到偏移向量等信息;最后,根据查得的信息计算图像中目标形状的可能的参考点位置进行投票。根据投票结果即可提取出图像中目标形状的位置。当图像中目标形状分别旋转0°、2°、4°、5°、6°时,提出的算法的匹配结果均在图像中目标形状位置具有非常明显的峰值。仿真结果表明,改进的广义Hough变换(I-GHT)算法具有良好的抗旋转性和抗噪性。     

Energy-optimal trajectory planning for car-like robots

When a battery-powered robot needs to operate for a long period of time, optimizing its energy consumption becomes critical. Driving motors are a major source of power consumption for mobile robots. In this paper, we study the problem of finding optimal paths and velocity profiles for car-like robots so as to minimize the energy consumed during motion. We start with an established model for energy consumption of DC motors. We first study the problem of finding the energy optimal velocity profiles, given a path for the robot. We present closed form solutions for the unconstrained case and for the case where there is a bound on maximum velocity. We then study a general problem of finding an energy optimal path along with a velocity profile, given a starting and goal position and orientation for the robot. Along the path, the instantaneous velocity of the robot may be bounded as a function of its turning radius, which in turn affects the energy consumption. Unlike minimum length paths, minimum energy paths may contain circular segments of varying radii. We show how to efficiently construct a graph which generalizes Dubins’ paths by including segments with arbitrary radii. Our algorithm uses the closed-form solution for the optimal velocity profiles as a subroutine to find the minimum energy trajectories, up to a fine discretization. We investigate the structure of energy-optimal paths and highlight instances where these paths deviate from the minimum length Dubins’ curves. In addition, we present a calibration method to find energy model parameters. Finally, we present results from experiments conducted on a custom-built robot for following optimal velocity profiles.     

一种多模式全向移动机器人攀爬楼梯的步态

针对移动机器人在户外运动中所遇到的台阶、楼梯等复杂地形,设计了一种可攀爬楼梯的多模式全向移动机器人。通过切换运动模态,该机器人既能像传统移动机器人一样快速移动,又具备了足式机器人的越障能力。首先,分析并构建了多模式全向移动机器人的运动学模型;其次,研究了该机器人越障能力和质心位置之间的关系并计算了该机器人可以翻越台阶的...     

Planning shortest bounded-curvature paths for a class of nonholonomic vehicles among obstacles

This paper deals with the problem of planning a path for a robot vehicle amidst obstacles. The kinematics of the vehicle being considered are of the unicycle or car-like type, i.e. are subject to nonholonomic constraints. Moreover, the trajectories of the robot are supposed not to exceed a given bound on curvature, that incorporates physical limitations of the allowable minimum turning radius for the vehicle. The method presented in this paper attempts at extending Reeds and Shepp's results on shortest paths of bounded curvature in absence of obstacles, to the case where obstacles are present in the workspace. The method does not require explicit construction of the configuration space, nor employs a preliminary phase of holonomic trajectory planning. Successfull outcomes of the proposed technique are paths consisting of a simple composition of Reeds/Shepp paths that solve the problem. For a particular vehicle shape, the path provided by the method, if regular, is also the shortest feasible path. In its original version, however, the method may fail to find a path, even though one may exist. Most such empasses can be overcome by use of a few simple heuristics, discussed in the paper. Applications to both unicycle and car-like (bicycle) mobile robots of general shape are described and their performance and practicality discussed.The Authors are with the Centro E. Plaggio and Dipartimento di Sistemi Elettrici e Automazione, Università di Pisa, Italia. This work was supported in part by the C. N. R.-Progetto Finalizzato Robotica Grants 93.01079.PF67 and 93.00880.PF67.     

基于改进快速行进平方法的多移动机器人聚集路径规划与控制

针对多移动机器人聚集的路径规划与控制问题,本文提出了基于改进快速行进平方法的路径规划策略.首先,运用分段函数改进了速度图,实现了更安全、更高效的路径规划,可以将快速行进网格地图上的速度映射到真实机器人速度上,并且减少传统快速行进平方法在回溯路径过程中产生的冗余路径；接着,针对多移动机器人聚集过程总能耗最小、聚集点附近空间最大、聚集队形约束下的聚集过程总能耗最小三种任务需求,分析设计不同的目标函数,给出多移动机器人的聚集点和对应规划路径,展示本文方法的有效性以及在不同场景下的适用性.最后,在车辆动力学模型基础上,使用模型预测控制以改进后的快速行进网格地图上的速度作为机器人参考速度进行了轨迹跟踪仿真实验,实现结果显示跟踪误差减小,验证了本文改进速度场的有效性,可适用于真实环境下多移动机器人聚集路径规划与控制.     

Planning the motions of a mobile robot in a sensory uncertaintyfield

Failures in mobile robot navigation are often caused by errors in localizing the robot relative to its environment. This paper explores the idea that these errors can be considerably reduced by planning paths taking the robot through positions where pertinent features of the environment can be sensed. It introduces the notion of a “sensory uncertainty field” (SUF). For every possible robot configuration q, this field estimates the distribution of possible errors in the robot configuration that would be computed by a localization function matching the data given by the sensors against an environment model, if the robot was at q. A planner is proposed which uses a precomputed SUF to generate paths that minimize expected errors or any other criterion combining, say, path length and errors. This paper describes in detail the computation of a specific SUF for a mobile robot equipped with a classical line-striping camera/laser range sensor. It presents an implemented SUF-based motion planner for this robot and shows paths generated by this planner. Navigation experiments were conducted with mobile robots using paths generated by the SUF-based planner and other paths. The former paths were tracked with greater precision than the others. The final section of the paper discusses additional research issues related to SUF-based planning     

An Optimal Control-Based Formulation to Determine Natural Locomotor Paths for Humanoid Robots

In this paper we explore the underlying principles of natural locomotion path generation of human beings. The knowledge of these principles is useful to implement biologically inspired path planning algorithms on a humanoid robot. By 'locomotion path' we denote the motion of the robot as a whole in the plane. The key to our approach is to formulate the path planning problem as an optimal control problem. We propose a single dynamic model valid for all situations, which includes both non-holonomic and holonomic modes of locomotion, as well as an appropriately designed unified objective function. The choice between holonomic and non-holonomic behavior is not accomplished by a switching model, but it appears in a smooth way, along with the optimal path, as a result of the optimization by efficient numerical techniques. The proposed model and objective function are successfully tested in six different locomotion scenarios. The resulting paths are implemented on the HRP-2 robot in the simulation environment OpenHRP as well as in the experiment on the real robot.     

Effects of turning gait parameters on energy consumption and stability of a six-legged walking robot

Minimization of energy consumption plays a key role in the locomotion of a multi-legged robot used for various purposes. Turning gaits are the most general and important factors for omni-directional walking of a six-legged robot. This paper presents an analysis on energy consumption of a six-legged robot during its turning motion over a flat terrain. An energy consumption model is developed for statically stable wave gaits in order to minimize dissipating energy for optimal feet forces distributions. The effects of gait parameters, namely angular velocity, angular stroke and duty factors are studied on energy consumption, as the six-legged robot walks along a circular path of constant radius with wave gait. The variations of average power consumption and energy consumption per unit weight per unit traveled length with the angular velocity and angular stroke are compared for the turning gaits of a robot with four different duty factors. Computer simulations show that wave gait with a low duty factor is more energy-efficient compared to that with a high duty factor at the highest possible angular velocity. A stability analysis based on normalized energy stability margin is performed for turning motion of the robot with four duty factors for different angular strokes.     

Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning

Robotic Path planning is one of the most studied problems in the field of robotics. The problem has been solved using numerous statistical, soft computing and other approaches. In this paper we solve the problem of robotic path planning using a combination of A* algorithm and Fuzzy Inference. The A* algorithm does the higher level planning by working on a lower detail map. The algorithm finds the shortest path at the same time generating the result in a finite time. The A* algorithm is used on a probability based map. The lower level planning is done by the Fuzzy Inference System (FIS). The FIS works on the detailed graph where the occurrence of obstacles is precisely known. The FIS generates smoother paths catering to the non-holonomic constraints. The results of A* algorithm serve as a guide for FIS planner. The FIS system was initially generated using heuristic rules. Once this model was ready, the fuzzy parameters were optimized using a Genetic Algorithm. Three sample problems were created and the quality of solutions generated by FIS was used as the fitness function of the GA. The GA tried to optimize the distance from the closest obstacle, total path length and the sharpest turn at any time in the journey of the robot. The resulting FIS was easily able to plan the path of the robot. We tested the algorithm on various complex and simple paths. All paths generated were optimal in terms of path length and smoothness. The robot was easily able to escape a variety of obstacles and reach the goal in an optimal manner.     

自然地形环境下移动机器人的一种路径规划方法

本文给出了一种规划移动机器人在自然地形中运动的新方法，该方法利用ＮＵＲＢＳ曲面模拟自然地形地貌，以ＴｒｉｍｍｅｄＮＵＲＢＳ曲面描述带有障碍物或不可逾越区域的地形，在综合考虑机器人动力学、地形及障碍描述和曲面特性等各方面因素的情形下，运用测地线的概念和计算方法以及Ａ＊搜索算法，获得了在自然地形环境下任意两点间的距离最短路径和时间最优路径，所有的路径均由ＮＵＲＢＳ曲线表示，实验结果表明，该方法在性能与效率上均十分令人满意．     

Near Optimal Robust Path Planning for Mobile Robots: the Viscous Fluid Method with Friction

A new approach is proposed to robot path planning that consists of using the viscous fluid equations including external forces. Unlike the majority of potential field techniques, the method is able to cope not only with 2-dimensional binary environments made of obstacles and free space, but also with so-called weighted regions, as well as uneven natural terrain where slope and ground characteristics influence the robot performance. It shows how the viscosity coefficient can be used to control the corridors of navigation, and how the external forces acting on the fluid particles can model the forces due to gravity and to friction between the ground and the vehicle. The planner automatically constructs several routes of equivalent costs, that makes the solutions more robust than those obtained by the search of optimal paths, by allowing reactivity in case of an unexpected local disturbance. Comparisons with the scent diffusion method for a binary universe and with a genetic algorithm for a real natural terrain are presented.