多障碍物环境下机器人的连续避障策略

本文针对多障碍物环境下,机器人避障过程中又出现障碍物这一情况,设计了一种避障策略,来实现机器人的连续避障并到达指定目标点的功能。在该策略中,设计了一套基于避障状态的方向选择规则,并提出了一种象限法,来决定机器人的当前行为。实验证明,该策略实现简单,并可较大限度地降低避障行为的盲目性和重复性。     

一种复杂环境下移动机器人避障新方法

为解决基于传统人工势场法的机器人避障算法在陷阱区域或在障碍物前会产生震荡而导致机器人不能到达目标点的问题,提出一种带记忆功能的沿边法;通过记录和分析障碍物边缘人工势场法的局部最小点来判断目标点是否被障碍物包围,从而避免机器人来回震荡以及围着目标点旋转的现象;首先进行沿边行为激活和退出条件研究,然后进行局部最小点记录条件分析,最后给出了目标点被障碍物包围的判断准则;仿真实验,结果表明该方法解决了复杂环境下机器人的避障问题;该方法用于复杂环境下的机器人避障是可行的、有效的.     

基于模糊力控制算法的移动机器人避障控制

根据机器人的末端执行器和外界环境表面接触与移动机器人避障控制的相似点,将力/位置控制成功应用到移动机器人的避障控制领域内.对新颖的移动机器人避障控制算法是通过在移动机器人和障碍物之间形成虚拟力场,且对其进行整定以使两者之间能保持期望的距离.因为机器人动力学模型和障碍物的不确定性会对避障控制性能造成影响,为避免碰撞,采用模糊PD的智能混合力/位置控制来整定机器人和障碍物精确距离的力场.通过仿真研究证明了算法的有效性,可为机器人设计提出可靠依据.     

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。     

基于虚拟力引导的人机协同目标抓取方法

针对力反馈遥操作中传统人工势场法无法适应于机械臂整体的避障以及在作业过程中操作者难以控制机械臂到达所需位姿的问题,提出了一种基于虚拟力引导的人机协同目标抓取方法。力反馈设备向操作者提供力觉交互。通过结合人工势场法和虚拟夹具,构建管道形虚拟力场,生成实时虚拟力引导,实现协助操作者完成从端机器人的整体避障任务并在完成避障后引导机器人返回预定义路径并趋近目标点。当进行抓取任务时,构建锥形虚拟力场,实现协助操作者操作机械臂到达目标位置和姿态。此外,提出了一种机器人运动限制方法以降低操作者的操作失误对抓取任务的影响。实验证明,该方法能有效提高目标抓取操作的成功率和操作效率。     

基于多数据融合的智能定位传感器避障算法研究

在智能定位传感器内增加避障算法，可使机器人拥有自动躲避障碍的能力，该文基于多数据融合设计智能定位传感器避障算法。设置激光雷达测距和超声波测距作为多传感器障碍检测的方法，获取机器人当前位置与障碍点坐标的相对几何关系，计算机器人与障碍点位置的距离，定位路面障碍点，对2种传感器收集到的数据进行多元障碍定位信息的加权融合。设置智能机器人避障轨迹目标函数以及约束条件，设计机器人避障算法，得到基于定位传感器的机器人避障方法。实验结果表明，在简单环境及复杂环境下机器人均未与障碍物相撞。在运行轨迹中随机放置障碍物，机器人能够及时完成运行轨迹的变化。由此可见，该避障算法具备较好的应用前景，可应用于各种智能机器人中。     

基于改进虚拟力场的机器人路径规划方法

未知环境下机器人路径规划是机器人控制领域的研究热点问题之一,针对传统方法存在的不足,提出一种基于改进虚拟力场的未知环境下机器人路径规划方法。在虚拟力场方法中,通过引入一种面积比参数,将机器人和障碍物的大小考虑进算法当中,从而可以使机器人的运动轨迹更加安全,到达目标的时间更短。最后通过仿真实验证明了所提算法的有效性。     

基于改进ND+算法的机器人避障方法设计

针对室内结构化环境下服务机器人避障过程中传统ND+算法生成的决策速度不平滑问题,提出了一种对ND+算法生成的决策方向进行修正,使得机器人决策速度变得平滑的设计方法,该方法能够使得机器人更快速平稳地避开障碍物到达目标点。首先,根据ND+算法获取机器人的初始决策方向,以此方向把地图划分为两部分,获取左右两侧的近距离的障碍物点集,各个障碍物点共同对机器人作用,修正机器人的决策方向;实验结果表明,相比ND+算法,该方法能够提前规避前进方向潜在障碍物,机器人更加平缓地行走于充斥拥挤障碍物的室内环境中到达指定目标点。     

未知环境下基于Elman网络力控制的移动机器人避障研究

避障控制一直是移动机器人路径规划的难点.提出了一种未知环境下基于神经网络的机器人动态避障方法,同时把混合力/位置控制结构应用到移动机器人的避障控制中.力控制算法是通过在移动机器人和障碍物之间形成虚拟力场,并对其整定,以使它们两者之间能保持期望距离.由于移动机器人的动力学模型和障碍物的不确定性也会对避障控制的性能造成影响,因此采用Elman神经网络来补偿不确定性,同时整定移动机器人和障碍物之间的精确距离.仿真实验表明该动态避障算法是有效的.     

感知范围受限的群机器人自主围捕算法

针对在有障碍物场地中感知范围受限的群机器人协同围捕问题,本文首先给出了机器人个体、障碍物、目标的模型,并用数学形式对围捕任务进行描述,在此基础上提出了机器人个体基于简化虚拟速度和基于航向避障的自主围捕控制律.基于简化虚拟速度模型的控制律使得机器人能自主地围捕目标同时保持与同伴的距离避免互撞;基于航向的避障方法提升了个体的避障效率,避免斥力避障方法导致的死锁问题.其次本文证明了在该控制律下系统的稳定性.仿真结果表明,该算法在有效围捕目标的同时能够高效地避开障碍物,具有对复杂环境的适应性.最后本文分析了与其他方法相比该算法的优点.     

Fuzzy integral-based gaze control architecture incorporated with modified-univector field-based navigation for humanoid robots

When a humanoid robot moves in a dynamic environment, a simple process of planning and following a path may not guarantee competent performance for dynamic obstacle avoidance because the robot acquires limited information from the environment using a local vision sensor. Thus, it is essential to update its local map as frequently as possible to obtain more information through gaze control while walking. This paper proposes a fuzzy integral-based gaze control architecture incorporated with the modified-univector field-based navigation for humanoid robots. To determine the gaze direction, four criteria based on local map confidence, waypoint, self-localization, and obstacles, are defined along with their corresponding partial evaluation functions. Using the partial evaluation values and the degree of consideration for criteria, fuzzy integral is applied to each candidate gaze direction for global evaluation. For the effective dynamic obstacle avoidance, partial evaluation functions about self-localization error and surrounding obstacles are also used for generating virtual dynamic obstacle for the modified-univector field method which generates the path and velocity of robot toward the next waypoint. The proposed architecture is verified through the comparison with the conventional weighted sum-based approach with the simulations using a developed simulator for HanSaRam-IX (HSR-IX).     

Reactive navigation in dynamic environment using a multisensorpredictor

A reactive navigation system for an autonomous mobile robot in unstructured dynamic environments is presented. The motion of moving obstacles is estimated for robot motion planning and obstacle avoidance. A multisensor-based obstacle predictor is utilized to obtain obstacle-motion information. Sensory data from a CCD camera and multiple ultrasonic range finders are combined to predict obstacle positions at the next sampling instant. A neural network, which is trained off-line, provides the desired prediction on-line in real time. The predicted obstacle configuration is employed by the proposed virtual force based navigation method to prevent collision with moving obstacles. Simulation results are presented to verify the effectiveness of the proposed navigation system in an environment with multiple mobile robots or moving objects. This system was implemented and tested on an experimental mobile robot at our laboratory. Navigation results in real environment are presented and analyzed.     

Velocity planning for a mobile robot to track a moving target — a potential field approach

Potential field method has been widely used for mobile robot path planning, but mostly in a static environment where the target and the obstacles are stationary. The path planning result is normally the direction of the robot motion. In this paper, the potential field method is applied for both path and speed planning, or the velocity planning, for a mobile robot in a dynamic environment where the target and the obstacles are moving. The robot’s planned velocity is determined by relative velocities as well as relative positions among robot, obstacles and targets. The implementation factors such as maximum linear and angular speed of the robot are also considered. The proposed approach guarantees that the robot tracks the moving target while avoiding moving obstacles. Simulation studies are provided to verify the effectiveness of the proposed approach.     

Mobile Robot Navigation for Moving Obstacles with Unpredictable Direction Changes,Including Humans

In many service applications, mobile robots need to share their work areas with obstacles. Avoiding moving obstacles with unpredictable direction changes, such as humans, is more challenging than avoiding moving obstacles whose motion can be predicted. Precise information on the future moving directions of humans is unobtainable for use in navigation algorithms. Furthermore, humans should be able to pursue their activities unhindered and without worrying about the robots around them. An enhanced virtual force field-based mobile robot navigation algorithm (termed EVFF) is presented for avoiding moving obstacles with unpredictable direction changes. This algorithm may be used with both holonomic and nonholonomic robots. It incorporates improved virtual force functions and an improved method for selecting the sense of the detour force to better avoid moving obstacles. For several challenging obstacle configurations, the EVFF algorithm is compared with five state-of-the-art navigation algorithms for moving obstacles. The navigation system with the new algorithm generated collision-free paths consistently. Methods for solving local minima conditions are proposed. Experimental results are also presented to further verify the avoidance performance of this algorithm.     

快速连续反应-避障作业环境下的七自由度灵巧臂轨迹规划

人类经长期学习训练后能对高速物体 (如棒球、乒乓球等)具有快速连续反应作业的运动技能, 从深层次上揭示是由于人体在其训练过程中不断学习优选了相应手臂的动作轨迹, 并储存了丰富的经验和知识. 受人体手臂动作此行为机制启发, 本文提出一种 7-DOF灵巧臂快速连续反应-避障作业的轨迹规划方法. 该方法将灵巧臂对高速物体目标作业的轨迹规划问题转化为动作轨迹参数化优选问题, 考虑作业过程中灵巧臂的机构物理约束和障碍约束条件, 以灵巧臂目标可作业度指标构建适应度函数, 采用粒子群优化 (Particle swarm optimization, PSO)方法优选作业轨迹中的冗余参数; 在此基础上 利用灵巧臂动作轨迹参数化优选方法构建相应作业环境下的知识数据库, 实现灵巧臂对高速物体目标的快速连续反应作业. 以仿人机器人乒乓球对弈作业为例, 将该方法应用于 7-DOF灵巧臂乒乓球作业的轨迹规划中. 数值实验及实际对弈试验结果表明, 该方法不仅能使灵巧臂所规划的轨迹 满足灵巧臂机构物理约束与障碍约束条件, 同时能实现灵巧臂对乒乓球体的快速连续反应作业, 验证了该方法的有效性.     

基于主动式全景视觉的移动机器人障碍物检测

针对现有的移动机器人视觉系统计算资源消耗大、实时性能欠佳、检测范围受限等问题,提出一种基于主动式全景视觉传感器(AODVS)的移动机器人障碍物检测方法。首先,将单视点的全方位视觉传感器(ODVS)和由配置在1个平面上的4个红色线激光组合而成的面激光发生器进行集成,通过主动全景视觉对移动机器人周边障碍物进行检测;其次,移动机器人中的全景智能感知模块根据面激光发生器投射到周边障碍物上的激光信息,通过视觉处理方法解析出移动机器人周边障碍物的距离和方位等信息;最后,基于上述信息采用一种全方位避障策略,实现移动机器人的快速避障。实验结果表明,基于AODVS的障碍物检测方法能在实现快速高效避障的同时,降低对移动机器人的计算资源的要求。     

仿人机器人实时路径规划方法研究

为了使仿人机器人在人类生活环境中自由行走,将仿人机器人的动作离散化为指定的动作,将状态空间离散化为网格,利用立体视觉和平面提取方法建立环境地图,将仿人机器人的轮廓简化为双圆柱模型进行避障检测,最终在环境地图中搜寻代价最小的一系列可行的动作作为路径,通过仿真实验验证了方法的有效性。     

融合安全A*算法与动态窗口法的机器人路径规划

A^*算法通过启发信息指引搜索方向,被广泛应用于移动机器人的路径规划,但其规划出的搜索路径存在冗余节点且与障碍物相近,无法满足动态避障需求。对标准A^*算法进行改进,设计安全A^*算法并融合动态窗口法进行路径规划。定义安全距离因子引入A^*算法的启发函数中,提高算法规划路径的安全性,同时采用平面结构法对算法规划得到的路径进行优化,根据相邻节点与障碍物之间的位置关系判断该相邻节点间是否存在障碍物,由此减少路径拐点数,提高路径平滑度。由于当移动机器人处于未知环境时,仅靠A^*算法不能避开障碍物到达目标点,因此借助动态窗口法的局部避障功能。通过安全A^*算法规划全局最优路径节点坐标,设计融合子函数改进动态窗口法的评价函数,解决动态窗口法易陷入局部最优的问题。实验结果表明,在复杂环境中,该方法通过融合安全A^*算法和动态窗口法,能够确保在安全路径基础上实时随机避障,使机器人安全到达终点。     

Stepping over obstacles with humanoid robots

The wide potential applications of humanoid robots require that the robots can walk in complex environments and overcome various obstacles. To this end, we address the problem of humanoid robots stepping over obstacles in this paper. We focus on two aspects, which are feasibility analysis and motion planning. The former determines whether a robot can step over a given obstacle, and the latter discusses how to step over, if feasible, by planning appropriate motions for the robot. We systematically examine both of these aspects. In the feasibility analysis, using an optimization technique, we cast the problem into global optimization models with nonlinear constraints, including collision-free and balance constraints. The solutions to the optimization models yield answers to the possibility of stepping over obstacles under some assumptions. The presented approach for feasibility provides not only a priori knowledge and a database to implement stepping over obstacles, but also a tool to evaluate and compare the mobility of humanoid robots. In motion planning, we present an algorithm to generate suitable trajectories of the feet and the waist of the robot using heuristic methodology, based on the results of the feasibility analysis. We decompose the body motion of the robot into two parts, corresponding to the lower body and upper body of the robot, to meet the collision-free and balance constraints. This novel planning method is adaptive to obstacle sizes, and is, hence, oriented to autonomous stepping over by humanoid robots guided by vision or other range finders. Its effectiveness is verified by simulations and experiments on our humanoid platform HRP-2.