仿真机器鱼双鱼协作过孔策略的研究

水中机器人受到水波动力及水下复杂环境等相关因素的影响,导致行为控制和多机器鱼之间协作完成任务成为难点。针对水中机器人大赛中的双鱼协作过孔项目,从仿真机器鱼控制及协作策略两方面对水中机器人的控制方法进行了研究。水中机器人控制采用基于目标区域的路径规划方法,克服了仿真机器鱼由于水波动干扰而无法准确到达目标点的问题。仿真机器鱼通过不断感知外界环境和自身位置,合理进行角色变换,从而有效地实现了协作带球过孔的任务。该算法在2011中国机器人大赛暨RoboCup公开赛水中机器人双鱼协作过孔项目中获得冠军。对仿真机器鱼控制及协作策略的研究,为日后实体水中机器人的控制研究提供了参考,具有重要意义。     

基于模糊人工势场法的机器鱼路径规划

路径规划是机器人控制领域中的一个热点方向。本文考虑到机器鱼运动特点以及水环境的特点,结合人工势场法的优势,进行机器鱼路径规划。机器鱼受到两种力的作用,分别为吸引力和排斥力。吸引力的作用是为了实现机器人向目标点靠近,排斥力为了更好地防止机器鱼碰撞到运动着的障碍物,而且将障碍物相对于机器人的速度考虑到斥力函数里面。在系统中,机器鱼的控制采用的是离散控制方式,为了最大限度地避免碰撞,我们采用一种简单的预测方法来预测下一个时刻障碍物相对于机器鱼的位置。机器鱼受力后,采用模糊逻辑算法得到机器鱼的控制指令,并在动态障碍物的环境下实验证明该方法的有效性。     

基于Fast Marching方法的多目标点路径规划的研究

目前,水下自主机器鱼已经被应用于对水域多个目标点依次进行水质监测,因此有必要研究多个目标点的路径规划。针对遍历多个目标点的路径规划问题,提出一种Multi-Direction Fast Marching（MDFM）方法和遗传算法相结合的路径规划方法。该方法首先使用MDFM方法对工作站和多个目标点两两之间进行路径规划,然后使用遗传算法规划出遍历所有点的最短路径,最后通过仿真实验验证算法的可行性。     

基于八叉树建模的人工蜂群动态路径规划算法

针对群体动画路径规划过程中存在收敛速度慢、与环境的交互性差等问题,提出一种基于八叉树建模的人工蜂群路径规划算法.将环境以八叉树模型进行分解并存储,引入群体自适应机制,通过粒子适应度和环境编码信息寻找目标点,采用分层方式实现路径的动态规划.仿真实验结果表明,该算法能进行群体路径动态规划,在寻优性和收敛性上均有较好的性能.     

水中机器鱼仿真系统中的伪3D绘制

关于水中机器鱼仿真系统设计问题,在二维仿真平台上,绘制具有碰撞特性和三维动态视觉效果的物体的技术实现比较难.因此,可结合仿真机器鱼比赛采用的2D仿真平台,提出鱼体关节的柔性体线建模和尾鳍相位变换等设计思想,采用GDI+技术,修复了平台原有的刚体建模导致的碰撞处理漏洞,为机器鱼仿真搭设了物理层映射到界面层的桥梁,有效减少了鱼体碰撞的穿越现象.实验证明,改进方法在实现三维效果方面可为水中机器鱼仿真系统优化提供支持.     

蚁群算法在移动机器人路径规划中的仿真研究

研究移动机器人路径规划问题.移动机器人路径规划是一个多目标优化问题,由于避障定位要求,传统机器人路径规划优化方法存在算法复杂、搜索空间大和效率低等难题,难以获得最优解.为了提高机器路径规划的效率和定位准确性,提出了一种蚁群算法的移动机器人路径规划方法.蚁群算法的路径规划方法首先采用栅格法对机器人工作环境进行建模,然后将机器人出发点作为蚁巢位置,路径规划最终目标点作为蚁群食物源,通过蚂蚁间相互协作找到-条避开障碍物的最优机器人移动路径.仿真实验结果证明,蚁群算法的路径规划方法提高了机器人路径规划的效率,能在最短时间找到机器人路径规划最优解,且能安全避开障碍物,为优化设计提供了依据.     

用于避障研究的微型仿生机器鱼3维仿真系统

基于VC++6.0开发环境和OpenGL（open graphics library）国际图形标准,在Windows系统下开发了微型仿生机器鱼3维仿真系统。该系统可以降低用实体机器鱼进行机器鱼避障能力研究的成本和减少在研究过程中对实体机器鱼造成的损害。采用多边形建模的方法构建了虚拟微型仿生机器鱼模型,模拟了鱼类尾鳍的摆动。提出了一种模拟红外传感器探测障碍物的虚拟射线方法。并采用实时模糊决策算法设计了基于多传感器信息的复合模糊控制器,决策微型仿生机器鱼的避障行为。仿真实验表明,复合模糊控制器实时性好、效率高;无论是单个任意形状的障碍物还是多个连续障碍物,复合模糊控制器都能有效地引导仿生机器鱼避开障碍物,到达目标点。微型仿生机器鱼3维仿真系统为研究仿生机器鱼的自主避障能力提供了可靠、逼真、便利的平台。     

基于神经网络的机器人路径规划算法

机器人路径规划技术是机器人研究的一个重要领域.针对未知的全局环境,使机器路径最优化,利用机器人传感器网络建立可视区域,将整体任务分解为环境信息已知的一系列子任务,利用神经网络高速并行计算的优点,建立神经网络罚函数,提出一种实时性较高的变参数方法离散化求取罚函数的负梯度方向,控制机器人快速高效地完成子任务,从而驱使机器人到达目标点并进行仿真.仿真结果证明了复杂环境静态和动态目标指引下方法的有效性和实用性,特别适用于实时性要求高的场合.     

基于目标状态估计的UAV路径重规划决策模型

变结构离散动态贝叶斯网络能感知突发固定威胁,但难以应用于状态未知的突发机动威胁.针对此问题,提出一种新的无人机路径重规划决策模型.该模型以变结构离散动态贝叶斯网络为基础,在机动威胁目标状态未知情况下,结合Kalman滤波理论,得到基于动态贝叶斯网络的目标状态估计模型,并将其作为一个模块加入路径重规划模型中,实现路径重规划决策.仿真结果证明了所提出的无人机路径重规划决策模型的正确性.     

计算机动画中的虚拟角色路径规划研究

动画或游戏中的虚拟角色的路径规划与机器人路径规划不同,它除了需要在运动空间找出一条无碰撞路径之外,更重要的是要体现出虚拟角色的生物行为特性。以人工鱼(晓媛鱼)为对象,在其活动的虚拟海洋环境中对其进行路径规划：将人工鱼抽象为一个有限状态机,采用人工势场方法。为体现人工鱼的生物行为特性,引入基于视觉和嗅觉的模糊判断。经动画仿真,得到了满意的效果。     

Machine vision based path planning for a robotic golf club head welding system

A path planning method based on machine vision techniques is constructed for a golf-club head robotic welding system. This system uses 3D machine vision techniques to recognize the weldseam and generates a welding path for the robot. The location of the weldseam is discovered by applying a Sobel mask to the captured data. A Laplace mask is also useful to filter out the noise points due to the scatter light refraction of tack-welding spots. The weldseam is then replenished and smoothed out by a B-spline curve fitting. The task frame of the weldseam is computed by finding the tangent, normal, and bi-normal of the curve. The robotic welding path is obtained by further rotations and translation along the axes of the task frame according to the requirement of the welding attitude. The developed machine vision technique and the mathematic framework pertaining to the generation of a welding task frame can readily be used for various three-dimensional welding tasks.     

Time-optimal tool motion planning with tool-tip kinematic constraints for robotic machining of sculptured surfaces

A time-optimal motion planning method for robotic machining of sculptured surfaces is reported in this paper. Compared with the general time-optimal robot motion planning, a surface machining process provides extra constraints such as tool-tip kinematic limits and complexity of the curved tool path that also need to be taken into account. In the proposed method, joint space and tool-tip kinematic constraints are considered. As there are high requirements for tool path following accuracy, an efficient numerical integration method based on the Pontryagin maximum principle is adopted as the solver for the time-optimal tool motion planning problem in robotic machining. Nonetheless, coupled and multi-dimensional constraints make it difficult to solve the problem by numerical integration directly. Therefore, a new method is provided to simplify the constraints in this work. The algorithm is implemented on the ROS (robot operating system) platform. The geometry tool path is generated by the CAM software firstly. And then the whole machine moving process, i.e. the feedrate of machining process, is scheduled by the proposed method. As a case study, a sculptured surface is machined by the developed method with a 6-DOF robot driven by the ROS controller. The experimental results validate the developed algorithm and reveal its advantages over other conventional motion planning algorithms for robotic machining.     

Comparison of intelligent control planning algorithms for robot's part micro-assembly task

Two intelligent control (path or motion) planning algorithms, based on a neural network and a fuzzy set theory, related to a robotic quasi-static part micro-assembly task are introduced. The part micro-assembly considered in this paper consists in a mating a part with an assembly hole or a receptacle (target) without a jamming. These algorithms are then compared through the utilization of experimentally measured data as well as simulations and a set of criteria. An entropy function, which is a useful measure of the variability and the information in terms of uncertainty, is introduced to measure its overall performance of a task execution related to the part micro-assembly task. Fuzzy set theory is introduced to address the uncertainty associated with the part micro-assembly procedure. The degree of uncertainty associated with the part micro-assembly is used as an optimality criterion, e.g. minimum fuzzy entropy, for a specific task execution. It is shown that the machine organizer using a sensor system can intelligently determine an optimal control value, based on explicit performance criteria. The algorithms utilize knowledge processing functions such as machine reasoning, planning, inferencing, learning, and decision-making. The results show the effectiveness of the proposed approaches. The proposed techniques are applicable to a wide range of robotic tasks including motion planning, pick and place operations, and part mating with various shaped parts.     

Coverage for robotics – A survey of recent results

This paper surveys recent results in coverage path planning, a new path planning approach that determines a path for a robot to pass over all points in its free space. Unlike conventional point-to-point path planning, coverage path planning enables applications such as robotic de-mining, snow removal, lawn mowing, car-body painting, machine milling, etc. This paper will focus on coverage path planning algorithms for mobile robots constrained to operate in the plane. These algorithms can be classified as either heuristic or complete. It is our conjecture that most complete algorithms use an exact cellular decomposition, either explicitly or implicitly, to achieve coverage. Therefore, this paper organizes the coverage algorithms into four categories: heuristic, approximate, partial-approximate and exact cellular decompositions. The final section describes some provably complete multi-robot coverage algorithms.     

Influence of Data Clouds Fusion From 3D Real-Time Vision System on Robotic Group Dead Reckoning in Unknown Terrain

This paper proposes the solution of tasks set required for autonomous robotic group behavior optimization during the mission on a distributed area in a cluttered hazardous terrain.The navigation scheme uses the benefits of the original real-time technical vision system(TVS)based on a dynamic triangulation principle.The method uses TVS output data with fuzzy logic rules processing for resolution stabilization.Based on previous researches,the dynamic communication network model is modified to implement the propagation of information with a feedback method for more stable data exchange inside the robotic group.According to the comparative analysis of approximation methods,in this paper authors are proposing to use two-steps post-processing path planning aiming to get a smooth and energy-saving trajectory.The article provides a wide range of studies and computational experiment results for different scenarios for evaluation of common cloud point influence on robotic motion planning.     

A novel seam extraction and path planning method for robotic welding of medium-thickness plate structural parts based on 3D vision

Currently, the robotic welding of medium-thickness plate structural parts has become a common welding application. With the rapid development of automation technology and robotics, the traditional teaching-playback mode and the off-line programming mode cannot meet the automation demand of welding robots. To realize automatic seam extraction and path planning for robotic welding of medium-thickness plate structural parts without programming and teaching, we use three models of medium-thickness plate structural parts as the research objects to propose a novel seam extraction and path planning method for robotic welding of medium-thickness plate structural parts based on 3D vision. Firstly, a set of improved RANSAC multiplanes fitting algorithms is proposed to accurately obtain the position of the intersection lines between the intersecting planes of the point cloud model. On this basis, we combine the geometric features of three models to propose the specific welding seam extraction methods respectively. Then, according to the spatial structure of the welding seams and the welding process, we carry out the welding path planning. Finally, a welding pose planning method based on the dihedral structure is proposed. Experiment results show that the proposed method can well realize the welding seam extraction, welding path and posture planning of medium-thickness plate structural parts without programming and teaching.     

A neural network approach to complete coverage path planning.

Complete coverage path planning requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications such as vacuum robots, painter robots, land mine detectors, lawn mowers, automated harvesters, and window cleaners. In this paper, a novel neural network approach is proposed for complete coverage path planning with obstacle avoidance of cleaning robots in nonstationary environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally simple. Simulation results show that the proposed model is capable of planning collision-free complete coverage robot paths.     

Neural reactive path planning with Riemannian motion policies for robotic silicone sealing

Due to its excellent chemical and mechanical properties, silicone sealing has been widely used in many industries. Currently, the majority of these sealing tasks are performed by human workers. Hence, they are susceptible to labor shortage problems. The use of vision-guided robotic systems is a feasible alternative to automate these types of repetitive and tedious manipulation tasks. In this paper, we present the development of a new method to automate silicone sealing with robotic manipulators. To this end, we propose a novel neural path planning framework that leverages fractional-order differentiation for robust seam detection with vision and a Riemannian motion policy for effectively learning the manipulation of a sealing gun. Optimal control commands can be computed analytically by designing a deep neural network that predicts the acceleration and associated Riemannian metric of the sealing gun from feedback signals. The performance of our new methodology is experimentally validated with a robotic platform conducting multiple silicone sealing tasks in unstructured situations. The reported results demonstrate that compared with directly predicting the control commands, our neural path planner achieves a more generalizable performance on unseen workpieces and is more robust to human/environment disturbances.     

Enhancement and evaluation in path accuracy of industrial robot for complex surface grinding

Lower path accuracy is an obstacle to the application of industrial robots in intelligent and precision grinding complex surfaces. This paper proposes a novel path accuracy enhancement strategy and different evaluation methods for a six-degree-of-freedom industrial robot FANUC M710ic/50 used for grinding an aero-engine blade. Six groups of theoretical tool paths individually planned on this complex surface were obtained using the iso-parametric method and the constant chord height method. Then the actual paths of the robot were dynamically recorded by a laser tracker with a high frequency. A revised Levenberg-Marquardt and Differential Evolution hybrid algorithm was proposed to improve the absolute robotic positioning accuracy by considering the average curvature variation rate, the arc length and the number of cutter contact points on planning paths. The results showed that the maximum positioning error had been drastically reduced from 0.792 mm to 0.027 mm. Based on the redefinition of robotic path accuracy, including position accuracy and shape accuracy in this work, the methods MP-TLD, BP-TPD and MP-TID were proposed to evaluate the enhanced path accuracy. The evaluation results showed that the different path planning methods have almost little effect on path accuracy. Furthermore, the maximum path deviation evaluated by the MP-TLD method was reduced from 0.378 mm to 0.044 mm, evaluated by the BP-TPD method was reduced from 0.374 mm to 0.029 mm, and evaluated by the MP-TID method was reduced from 0.205 mm to 0.026 mm. It is concluded that these evaluation methods are basically valid and the average path accuracy value is about 0.035 mm, for present complex surface grinding with this typical industrial robot. Finally, the robotic grinding experiments of titanium alloy blades are conducted to further validate the effectiveness of the proposed method.