移动机器人四杆地形感知机构的设计

根据四杆机构的运动特性,结合城区和建筑内地形环境的特点设计了一种移动机器人四杆地形感知机构——TTSMS。通过对凸形地形的定义,描述了TTSMS对地形感知的原理和方法。它能够精确、可靠的感知地形,基本不受环境的干扰,能够直接获取障碍的特征,解决了移动机器人在三维地形环境中运动时障碍的实时感知问题。     

未知环境中移动机器人基于行为的自主导航与环境构建

将模糊控制应用于基于行为的移动机器人控制结构中，使机器人具备了避障、避险和障碍物识别等基本的自主导航能力。针对未知环境中移动机器人的地图构建问题，提出了基于虚拟势场的全局搜索方法，采用基于栅格的地图表示方法，利用声纳传感器收集信息，实现对障碍物的识别和定位。通过试验验证了该方法的可行性。     

六轮移动机器人包容地形研究

分析了六轮机器人的四种常见地形：台阶、凸台、壕沟、楼梯的几何参数与机器人自身参数的关系,得出了这些地形能够成为机器人的包容地形的限定条件。在机器人的行为决策中,为其选择越障还是避障提供了依据。     

混响环境下移动机器人语音控制方法及系统实现

为了满足移动机器人交互控制多样性需求,提升移动机器人的语音控制性能,设计基于语音的移动机器人控制系统。通过对控制信号传递过程和机器人使用环境中语音信号噪音源分析,制定出移动机器人语音控制系统组成方案,给出前端语音识别部分实现的主要流程,重点对语音增强的解混响算法进行设计。充分利用语音潜在的谱特征,将非负矩阵分解和深度神经网络结合提出一种解混响算法,先通过矩阵分解得到语音信号特征,再生成特征矢量来训练激活函数,降低深度神经网络模型的训练复杂度,经过对比分析表明该算法对解决语音混响问题具有优势。编写控制软件并嵌入语音识别算法,搭建工业移动机器人语音控制平台来验证语音控制系统有效性,在混响环境下不同人对机器人多个动作进行语音控制实验,结果表明该系统能够实现移动机器人语音控制,所提出的语音识别方法可使机器人在0.3、0.6和0.9 s的混响条件下动作正确平均执行率分别能达到96%、95%和93%以上。     

遥控移动机器人触觉感知接口的实现

传感器技术在力反馈方面的发展促进了遥操作移动机器人技术的发展,在原有遥控技术基础上,增加了力反馈传感器,这样提供给远方操作者的除了图像和导航信息外,还有触觉信息,因此改进了遥控性能,文章描述了用力反馈操作杆实现的移动机器人触觉感知接口的软硬件实施方法,及通过网络实现的遥操作。     

未知环境中基于强化学习的移动机器人路径规划

为解决未知环境中移动机器人的自适应路径规划问题,提出了一种基于Q学习算法的自主学习方法。首先设计了未知环境中基于传感器信息的移动机器人自主路径规划的学习框架,并建立了学习算法中各要素的数学模型;然后利用模糊逻辑方法解决了连续状态空间的泛化问题,有效地降低了Q值表的维数,加快了算法的学习速度;最后在不同障碍环境中对基于Q学习算法的自主学习方法进行了仿真实验,仿真实验中移动机器人通过自主学习较好地完成了自适应路径规划。研究结果证明了该自主学习方法的有效性。     

未知环境的移动机器人路径规划研究

为了提高移动机器人在未知环境中的路径规划的性能,提出了改进人工势场法.首先分析了传统人工势场法的优缺点,据此,提出了一种由模糊智能控制与改进人工势场法相结合算法.由模糊算法对静态障碍物进行避障,并改进引力函数将目标点速度、加速度信息加入其中对目标点进行跟踪,从而实现自动避障和路径规划.通过实验仿真对比可知,此算法在存在...     

全地形轮式移动机器人运动学建模与分析

提出全地形轮式移动机器人的正逆运动学问题.将机器人看成一个混合串-并联多刚体系统,从每个轮-地接触点到机器人车体分别构成一个串联子系统,抛弃车轮纯滚动假设,在轮-地接触点处建立瞬时坐标系,考虑车轮的平面滑移,从而对每个串联子系统形成一个封闭的速度链.对于每个速度闭链,可直接在驱动轮轮心处写出从机器人各驱动轮到机器人本体之间的运动方程,将每个速度闭链的运动方程合并即可得到机器人的整体运动学模型.以一个具有被动柔顺机构的六轮全地形移动机器人为对象进行推导,该方法既考虑了地形不平的影响,又考虑了车轮的前向、侧向及转向滑移,已知机构参数后就可以直接写出机器人的速度方程,且便于运动学求解.该方法对于轮式移动机器人的运动学建模具有一般性,且具有物理意义明确、推导过程简洁等特点.     

全地形移动机器人悬架机构设计及特性分析

针对全地形移动机器人多地形行走需要,满足其路面的通过性与减振需求,设计了一种双叉臂悬架减振机构,建立了基于动力学参数分析的数学模型;通过ADMAS-Simulink联合仿真,分析了多路面谱函数激励下移动机器人悬架机构的振动情况,优化了所选取的阻尼器阻尼比、刚度参数和摆臂角等悬架特性参数,确定了悬架特性参数和驱动力矩等主要设计参数范围;进一步地,开展了户外多地形行走试验,包括鹅卵石、青石板、土壤、草地等较为代表性路面,对比了两种阻尼器参数测试,结果表明,所设计的悬架机构及特性参数优化,显著改善了减振效果和越障性能,提升了车轮驱动力矩转化输出为牵引力的效果,验证了悬架减振机构动力学建模描述与仿真分析的正确性,有效改善了全地形移动机器人路面适应性、通过性和行走稳定性。     

动态环境下的移动机器人运动规划系统的研究

研究了动态环境下移动机器人的路径规划问题,建立了一套完整的运动规划系统。该系统可为移动机器人找到从起始点到目标点的可行路径,通过摄像头和图像处理方法解决了障碍物和目标的定位问题,利用遗传算法的强大优化能力自动获取和指导优化的搜索空间,自适应地调整搜索方向,获得了产生最优路径的方法,并专门设计了用户图形界面允许用户和机器人系统进行交互及环境观察。仿真结果表明,本系统能为移动机器人迅速规划出一条优化路径,且能安全避碰,效果令人满意。     

A compliance control strategy for robot manipulators under unknown environment

In this paper, a compliance control strategy for robot manipulators that employs a self-adjusting stiffness function is proposed. Based on the contact force, each entry of the diagonal stiffness matrix corresponding to a task coordinate in the operational space is adaptively adjusted during contact along the corresponding axis. The proposed method can be used for both the unconstrained and constrained motions without any switching mechanism which often causes undesirable instability and/or vibrational motion of the end-effector. The experimental results involving a two-link direct drive manipulator interacting with an unknown environment demonstrates the effectiveness of the proposed method.     

Sonar based position estimation system for an autonomous mobile robot operating in an unknown environment

The paper presents a new method of position estimation for robot navigation in a completely unknown environment. The method is different from conventional ones in that it does not need any kind of a priori reference model or man-made landmarks. A series of local maps is built and updated from sonar data while the robot is exploring the unknown environment. Among the constructed local maps, the robot autonomously selects the ones with distinctive features and memorizes them as reference landmarks. An orientation clustering method is developed which enables the robot to extract the features of the map. The maps selected in such a way are then used to estimate the position and orientation of the robot while undertaking the given task. In doing so correspondence indices are defined to determine the corresponding reference map to the current local one among the numerous stored maps. The two maps are matched so as to minimize the discrepancy between them, thus enabling one to estimate the position and orientation of the robot. The usefulness of all these approaches is illustrated with the results produced by a real robot equipped with ultrasonic sensors.     

Development of autonomous mobile robot with manipulator for manufacturing environment

This paper describes the developmental effort involved in prototyping the first indigenous autonomous mobile robot, AMR, with a manipulator for carrying out tasks related to manufacturing. The objective is to design and develop a vehicle that can navigate autonomously and transport jobs and tools in a manufacturing environment. Proprioceptive and exteroceptive sensors are mounted on AMR for navigation. Among the exteroceptive sensors, a stereovision camera is mounted in front of AMR for mobile robot perception of the environment. Using the widely supported JPEG image file format, full high-resolution color images are transmitted frame by frame from the mobile robot to multiple viewers located within the robot work area, where fast reconstruction of these images enables remote viewing. A CMOS camera mounted on the manipulator identifies jobs for pick-and-place operation. A variation of correlation based adaptive predictive search (CAPS) method, a fast search algorithm in template matching, is used for job identification. The CAPS method justifiably selects a set of search steps rather than consecutive point-to-point search for faster job identification. Search steps, i.e., either coarse search or fine search, are selected by calculating the correlation coefficient between template and the image. Adaptive thresholding is used for image segmentation for parametric calculations required for proper gripping of the object. Communication with the external world allowing remote operation is maintained through wireless connectivity. It is shown that autonomous navigation requires synchronization of different processes in a distributed architecture, while concurrently maintaining the integrity of the network.     

未知环境下机器人力/位自适应模糊控制

机器人力控制是机器人研究的一个热点和难点。在未知环境中为实现精确的接触力控制,需要力控制器能够适应环境的变化。提出一种基于神经网络训练模糊控制规则的自适应模糊控制器应用于机器人的力控制中来适应机器人末端接触环境的变化,首先用递推最小二乘算法根据机器人对未知环境的动态响应来在线估计环境参数,然后选择一个合适的模糊控制规则调整因子对力控向量进行控制。仿真试验研究表明所设计的控制器是可行和有效的。     

未知环境下移动机器人实时模糊路径规划

针对机器人在未知环境下避障的要求,设计了一个结合目标点位置信息和局部障碍物信息的评价函数实时导航算法,使机器人可以在无碰撞的情况下较快地到达目标点。该算法以二维激光雷达作为探测环境的主要传感器,采用基于行为的控制策略及模糊控制器技术,通过添加自由出口提高其路径搜寻的效率,最后为了验证此模糊路径规划方法进行了仿真实验。仿真实验结果表明,该算法具有良好的效果,机器人可以有效避障,且轨迹平滑、实时性好。     

Fuzzy force control of constrained robot manipulators based on impedance model in an unknown environment

To precisely implement the force control of robot manipulators in an unknown environment, a control strategy based on fuzzy prediction of the reference trajectory in the impedance model is developed. The force tracking experiments are executed in an open-architecture control system with different tracking velocities, different desired forces, different contact stiffnesses and different surface figurations. The corresponding force control results are compared and analyzed. The influences of unknown parameters of the environment on the contact force are analyzed based on experimental data, and the tunings of predictive scale factors are illustrated. The experimental results show that the desired trajectory in the impedance model is predicted exactly and rapidly in the cases that the contact surface is unknown, the contact stiffness changes, and the fuzzy force control algorithm has high adaptability to the unknown environment. Translated from Journal of Northeastern University (Natural Science), 2005, 26(8): 766–769 [译自: 东北大学学报 (自然科学版)]     

全自主清扫机器人智能结构设计

详细的阐述了一种全自主清扫机器人的智能结构设计,机器人以“迂回推进”的方式进行多次不同方向的清扫,并根据结果得出障碍物和环境信息,再利用边缘跟踪对障碍物及整个环境进行边缘清扫,根据仿真实验,对所得信息进行校正,证实该机器人能够实现全区域的覆盖。整个过程路径段简单,能源利用率高。     

Visual feedback control of a robot in an unknown environment (learning control using neural networks)

In this paper, a visual feedback control approach based on neural networks is presented for a robot with a camera installed on its end-effector to trace an object in an unknown environment. First, the one-to-one mapping relations between the image feature domain of the object to the joint angle domain of the robot are derived. Second, a method is proposed to generate a desired trajectory of the robot by measuring the image feature parameters of the object. Third, a multilayer neural network is used for off-line learning of the mapping relations so as to produce on-line the reference inputs for the robot. Fourth, a learning controller based on a multilayer neural network is designed for realizing the visual feedback control of the robot. Last, the effectiveness of the present approach is verified by tracing a curved line using a 6-degrees-of-freedom robot with a CCD camera installed on its end-effector. The present approach does not necessitate the tedious calibration of the CCD camera and the complicated coordinate transformations. This revised version was published online in October 2004 with a correction to the issue number.