Optimized sensor placement for active visual inspection

This article presents an optimized sensor planning system for active visual inspection of three‐dimensional manufacturing computer‐aided design (CAD) models. Quantization errors and displacement errors are inevitable in active visual inspection. To obtain high accuracy for dimensioning the entities of three‐dimensional CAD models, minimization of these errors is essential. Spatial quantization errors result in digitization. The errors are serious when the size of the pixel is significant compared to the allowable tolerance in the object dimension on the image. In placing the active sensor to perform inspection, displacement of the sensors in orientation and location is common. The difference between observed dimensions obtained by the displaced sensor and the actual dimensions is defined as displacement errors. The density functions of quantization errors and displacement errors depend on camera resolution and camera locations and orientations. The sensor constraints, such as resolution, focus, field‐of‐view, and visibility constraints, restrict sensor placement. To obtain a satisfactory view of the targeted entities of the CAD models, these constraints have to be satisfied. In this article, we focus on the edge line segments as the inspected entities. We use a genetic algorithm to minimize the probabilistic magnitude of the errors subject to the sensor constraints. Since the objective functions and constraint functions are both complicated and nonlinear, traditional nonlinear programming may not be efficient and it may trap at a local minimum. Using crossover operations, mutation operations, and the stochastic selection in the genetic algorithm, trapping can be avoided. Experiments are conducted and the performance of the genetic algorithm is presented. Given the CAD model and the entities to be inspected, the active visual inspection planning system obtains the sensor setting that maximizes the probabilities of a required accuracy for each entity. © 2001 John Wiley & Sons, Inc.     

Robot perception of impedance

This paper proposes an algorithm for robot perception of impedance, which can be used as a fundamental technique for real‐time and qualitative perception of physical constraints imposed on the robot's end‐effector. This algorithm (1) estimates the impedance that represents the motion‐force relation of the end‐effector; (2) calculates the uncertainties of the estimates; and (3) detects discontinuous changes in the impedance. The estimated impedance can be used to recognize local dynamic properties of the environment and temporary constraint conditions imposed on the end‐effector. The detected discontinuities can be used to segment the manipulated tasks and to recognize the geometric structure of the environment. This method can be implemented in both autonomous and remote‐controlled robots because it is designed separately from control methodologies. Results of preliminary experiments are presented. © 2005 Wiley Periodicals, Inc.     

Robust contorl of a mamipulator pefoming constrained motion with uncertainties in the constraint

This article deals with the control of a robot manipulator performing constrained motion in an environment where the constraint function contains uncertainties. The uncertainties are appropriately modeled, and a controller consisting two components, one for positional and velocity tracking, and the other for force regulation, is proposed. The controller contains a nonlinear term that takes the form of a polynomial in the norms of the trajectory and force errors. The coefficients of the nonlinear function are chosen in relation to the bounds on the dynamic parameters of the robot system and an estimate of the bound on the uncertainty in the constraint function. Simulation results show that the modeling is appropriate and the controller is robust against parameter uncertainties © 1995 John Wiley & Sons, Inc.     

Acquisition and propagation of spatial constraints based onqualitative information

In robot navigation, one of the important and fundamental issues is to find positions of landmarks or vision sensors located around the robot. This paper proposes a method for reconstructing qualitative positions of multiple vision sensors from qualitative information observed by the vision sensors, i.e., motion directions of moving objects. In order to directly acquire the qualitative positions of points, the method proposed in this paper iterates the following steps: 1) observing motion directions (left or right) of moving objects with the vision sensors, 2) classifying the vision sensors into spatially classified pairs based on the motion directions, 3) acquiring three point constraints, and 4) propagating the constraints. Compared with the previous methods, which reconstruct the environment structure from quantitative measurements and acquire qualitative representations by abstracting it, this paper focuses on how to acquire qualitative positions of landmarks from low-level, simple, and reliable information (that is, “qualitative”). The method has been evaluated with simulations and also verified with observation errors     

Adaptive robust motion/force control of holonomic-constrained nonholonomic mobile manipulators.

In this paper, adaptive robust force/motion control strategies are presented for mobile manipulators under both holonomic and nonholonomic constraints in the presence of uncertainties and disturbances. The proposed control is robust not only to parameter uncertainties such as mass variations but also to external ones such as disturbances. The stability of the closed-loop system and the boundedness of tracking errors are proved using Lyapunov stability synthesis. The proposed control strategies guarantee that the system motion converges to the desired manifold with prescribed performance and the bounded constraint force. Simulation results validate that the motion of the system converges to the desired trajectory, and the constraint force converges to the desired force.     

A point and distance constraint based 6R robot calibration method through machine vision

This paper proposes a 6R robot closed-loop kinematic calibration method to improve absolute position accuracy with point and distance constraints though machine vision. In the calibration process, a camera attached to the mounting plate of the robot is used to capture a fixed reference sphere as a point constraint and to record robot joint angles and gauge block lengths that are used as a distance constraint. A first-order difference quotient is used to calculate the Jacobian matrix in the joint parameter identification process. The Staübli TX60 robot is successfully calibrated using the proposed method. After calibration, the average distance error of robot motion is decreased from 2.05 mm to 0.24 mm.     

A constrained assumed modes method for dynamics of a flexible planar serial robot with prismatic joints

In the present study, for the first time, flexible multibody dynamics for a three-link serial robot with two flexible links having active prismatic joints is presented using an approximate analytical method. Transverse vibrations of flexible links/beams with prismatic joints have complicated differential equations. This complexity is mostly due to axial motion of the links. In this study, first, vibration analysis of a flexible link sliding through an active prismatic joint having translational motion is considered. A rigid-body coordinate system is used, which aids in obtaining a new and rather simple form of the kinematic differential equation without the loss of generality. Next, the analysis is extended to include dynamic forces for a three-link planar serial robot called PPP (Prismatic, Prismatic, Prismatic), in which all joints are prismatic and active. The robot has a rigid first link but flexible second and third links. To model the prismatic joint, time-variant constraints are written, and a motion equation in a form of virtual displacement and virtual work of forces/moments is obtained. Finally, an approximate analytical method called the “constrained assumed modes method” is presented for solving the motion equations. For a numerical case study, approximate analytical results are compared with finite element results, which show that the two solutions closely follow each other.     

Vsc motion and force control of robot manipulators in the presence of environmental constraint uncertainties

Due to task kinematic modelling inaccuracy, constraint functions imposed on robot manipulators may not be known exactly. In this article, a variable structure control (VSC) method is developed for robust motion and constrained force control of robot manipulators in the presence of parametric uncertainties, external disturbances, and constraint function uncertainties. The method is based on a particular structure of the constrained robot, in which motion control and force control are treated together. The proposed VSC controller provides the sliding mode and reaching transient response with prescribed qualities. A sufficient condition to guarantee the robot does not lose contact with the constraint surface is given. Detailed simulation results illustrate the proposed method. © 1994 John Wiley & Sons, Inc.     

Robotic milling posture adjustment under composite constraints: A weight-sequence identification and optimization strategy

Industrial robots are widely used for milling complex parts in restricted spaces owing to their multiple degrees of freedom and flexible postures. To plan posture trajectory for robot machining with high precision under multiple constraints, this study establishes composite constraint models with constraint boundary solutions. An improved gray relation analysis model is adopted to identify the weight-sequences among the composite constraints. The correlation degrees of the postures of the robot can be dynamically quantified between arbitrary cutter locations by applying weight sequence identification, which is conducive to fulfilling attractive orientations in artificial potential fields. In addition, this study proposes an initial posture determination strategy based on the optimization principle of minimizing the rotated energy in global postures. Consequently, an artificial potential planning model is applied to the implement posture adjustment of the robot end effector. During simulation and experimental validation, the proposed posture adjustment strategies with optimized initial postures and identified weight-sequences achieve a significant improvement in both the six-joint motion performance and machining precision quality in robotic milling.     

Tolerance design of a 2-DOF overconstrained translational parallel robot

This paper deals with the tolerance design of a two-degree-of-freedom translational parallel robot module for high-speed pick-and-place operations. The module is an overconstrained parallel mechanism using two sets of parallelograms in each limb. A probabilistic model of the uncompensatable pose error is formulated, together with a compatibility condition to ensure the mobility of the robot. Based upon this model, optimization of the tolerances of the geometric source errors and joint clearances is conducted, subject to a set of appropriate constraints. Simulation and experimental results are given to demonstrate the effectiveness of this approach.     

基于hp自适应伪谱法的空间机器人路径规划

针对空间机器人运动过程中基座姿态产生较大扰动的问题,基于hp自适应高斯伪谱法提出了一种以基座所受反作用力矩最小为目标函数的空间机器人路径规划方法.首先,综合考虑空间机器人运动过程中存在的关节角度约束、关节角速度约束、控制力矩约束及初始状态和终端状态约束等约束条件,将空间机器人路径规划问题看成满足一系列约束条件和边界条件并实现特定性能指标最优的最优控制问题.其次,结合hp自适应高斯伪谱法（hp-AGPM）与非线性规划技术,求解带有边界约束和路径约束的优化控制问题,得到满足约束且性能指标最优的空间机器人运动轨迹.最后,以平面2自由度空间机械臂为例对所设计方法进行仿真验证,并与其他伪谱法进行对比分析.仿真结果表明：本文算法能在10.6 s的时间内规划出满足各约束条件且容许偏差低于10^-6的最优运动轨迹,并且在计算速度和配点数量上都优于其他伪谱法.     

基于光视觉技术的海底管道自主巡检系统研究

针对海底管道受水中污垢、海水腐蚀、运行磨损等影响而产生缺陷和损伤问题,设计了一种基于光视觉技术的自主巡检机器人系统并制作样机进行实验验证。应用光视觉技术,设计运动控制器,实现以前端摄像头为运动圆心的寻迹偏角和位移偏差校正,保证了摄像头视野的全程有效性;基于YOLOv4-tiny模型,提出了一种异常综合评估算法,以动态评估运动过程的图像数据,有效避免了由于模型精度有限造成的误识别、数据利用不充分、同步性差等问题。经连续多次水下实验,表明自主巡检机器人能够在水平方向、竖直方向准确地沿水下管道进行循迹运动,运动准确率达100%;能够准确识别水下管道吸附物的位置和形状,识别准确度达到96%。     

约束条件下的巡线机器人逆运动学求解

高压输电线路巡检机器人是一种多关节悬挂运动机构,要实现其运动控制就需要根据机器人的本身机构特点和悬挂系统的运动约束条件进行运动学分析.本文利用微分扭转法对巡线机器人的正向运动学进行了求解,并通过对机器人悬挂系统的力学分析,得到了机器人运动学的约束条件,并在这种约束条件下,采用了一种可用来进行实时控制的迭代循环坐标下降(CCD)算法,来进行机器人的逆运动学求解.这种迭代算法对于有运动约束系统的逆运动学求解具有较强的适用性,而且它具有较好的收敛性和有效性,适合于在线计算,便于巡线机器人的实时运动控制.     

基于双目视觉导航的仿生机器人鲁棒控制算法

仿生机器人在定姿过程中受到空间扰动因素的影响容易产生控制误差,需要对机器人进行精确标定,提高仿生机器人的定位控制精度,因此提出一种基于双目视觉导航的仿生机器人鲁棒控制算法。利用光学CCD双目视觉动态跟踪系统进行仿生机器人的末端位姿参量测量,建立被控对象的运动学模型;以机器人的转动关节的6自由度参量为控制约束参量,建立机器人的分层子维空间运动规划模型;采用双目视觉跟踪方法实现仿生机器人的位姿自适应修正,实现鲁棒性控制。仿真结果表明,采用该方法进行仿生机器人控制的姿态定位时对机器人末端位姿参量的拟合误差较低,动态跟踪性能较好。     

Observability of 3D Motion

This paper examines the inherent difficulties in observing 3D rigid motion from image sequences. It does so without considering a particular estimator. Instead, it presents a statistical analysis of all the possible computational models which can be used for estimating 3D motion from an image sequence. These computational models are classified according to the mathematical constraints that they employ and the characteristics of the imaging sensor (restricted field of view and full field of view). Regarding the mathematical constraints, there exist two principles relating a sequence of images taken by a moving camera. One is the epipolar constraint, applied to motion fields, and the other the positive depth constraint, applied to normal flow fields. 3D motion estimation amounts to optimizing these constraints over the image. A statistical modeling of these constraints leads to functions which are studied with regard to their topographic structure, specifically as regards the errors in the 3D motion parameters at the places representing the minima of the functions. For conventional video cameras possessing a restricted field of view, the analysis shows that for algorithms in both classes which estimate all motion parameters simultaneously, the obtained solution has an error such that the projections of the translational and rotational errors on the image plane are perpendicular to each other. Furthermore, the estimated projection of the translation on the image lies on a line through the origin and the projection of the real translation. The situation is different for a camera with a full (360 degree) field of view (achieved by a panoramic sensor or by a system of conventional cameras). In this case, at the locations of the minima of the above two functions, either the translational or the rotational error becomes zero, while in the case of a restricted field of view both errors are non-zero. Although some ambiguities still remain in the full field of view case, the implication is that visual navigation tasks, such as visual servoing, involving 3D motion estimation are easier to solve by employing panoramic vision. Also, the analysis makes it possible to compare properties of algorithms that first estimate the translation and on the basis of the translational result estimate the rotation, algorithms that do the opposite, and algorithms that estimate all motion parameters simultaneously, thus providing a sound framework for the observability of 3D motion. Finally, the introduced framework points to new avenues for studying the stability of image-based servoing schemes.     

基于免疫优化的平面Acrobot线性自抗扰鲁棒镇定

针对受不确定性影响的平面Acrobot机器人,提出一种基于免疫优化的线性自抗扰鲁棒控制设计方法,实现机器人末端点从任意初始位置到达并镇定在目标位置.首先,借助驱动关节与被动关节角度之间的状态约束获取机器人末端点位置与驱动关节角度的对应关系,使末端点的位置控制转换为驱动关节的角度控制;其次,为缩短运动路径加入最小角度位移限制条件,设计免疫算法求解目标位置所对应的驱动关节角度的最小期望值;再次,引入线性自抗扰控制技术,把机器人的模型不确定性、未知干扰等因素视为一个新的扩张状态变量,设计线性扩张状态观测器和基于状态误差的反馈控制器,在仅驱动关节角度可测的情况下实现Acrobot的鲁棒镇定;最后,通过仿真实验验证所提出方法具有更好的鲁棒控制性能.     

Quasi-Omnidirectional Fuzzy Control of a Climbing Robot for Inspection Tasks

This work presents a novel method to quasi-omnidirectional control of an intelligent inspection robot designed to work inside and outside spherical storage tanks. The main objective is to promote a stable and smooth navigation during inspection tasks, ensuring the safety motion under adhesion and kinematic constraints. The robot is designed with four independent steerable magnetic wheels and a mechanical topology that allows the correct adjustment of adhesion system. A scheduled Fuzzy control is developed to achieve an optimal behavior and maximize the robot’s maneuverability, considering the magnetic restrictions of adhesion system and kinematic constraints of the inspection robot. The high adaptability of its mechanical topology (i.e., wheel misalignment, magnetic adhesion system, wheel camber and flexibilities in mechanical structure) and gravitational disturbance introduce many nonlinear characteristics in dynamic behavior that cannot be neglected, making the determination of its dynamic model a complex task. The Fuzzy approach allows to project a control system without a depth knowledge of its dynamic properties, to minimize the dynamic disturbances found in robot structure. Thus, the proposed motion control works according to the robot specific characteristics to ensure the quasi-omnidirectional motion over a reliable adhesion to tank surface and to minimize the effects of wheels kinematic constraints.     

Fault-tolerant control for a class of quantised networked control of nonlinear systems with unknown time-varying sensor faults

ABSTRACT

In this paper, fault-tolerant control problems in quantised networked control of nonlinear systems (NCSs) with sensor uncertainties are considered. The measured output equation of the given NCS system has unknown external time-varying uncertainties that may be unbounded in addition to the effects of network-induced constraints; network-induced delays, network-induced data packet losses and network quantisation errors. A simultaneous observer-controller is constructed to stabilise the overall NCS. A flexible-joint robot link example is simulated to illustrate the significant improvement on system tracking performance.     

Formation control of multiple mecanum-wheeled mobile robots with physical constraints and uncertainties

Aiming at the formation control of multiple Mecanum-wheeled mobile robots (MWMRs) with physical constraints and model uncertainties, a novel robust control scheme that combines model predictive control (MPC) and extended state observer-based adaptive sliding mode control (ESO-ASMC) is proposed in this paper. First, a linear MPC strategy is proposed to address the motion constraints of MWMRs, which can transform the robot formation model based on leader-follower into a constrained quadratic programming (QP) problem. The QP problem can be solved iteratively online by a delay neural network (DNN) to obtain the optimal control velocity of the follower robot. Then, to address the input saturation constraints, model uncertainties and unknown disturbances in the dynamic model, an improved ESO-ASMC is proposed and compared with the robust adaptive terminal sliding mode control (RATSMC) and the conventional sliding mode control (SMC) to prove the effectiveness. The proposed scheme, considering the optimal control velocity obtained by the kinematics controller as the given desired velocity of the dynamics controller, can implement precise formation control, while solving various physical constraints of the robot, and eliminating the effects of model uncertainties and disturbances. Finally, through a comparative simulation case, the effectiveness and robustness of the proposed method are verified.

     

Path planning in GPS-denied environments via collective intelligence of distributed sensor networks

This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin’s car-like robot.