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1.
为研究管道机器人清淤装置在旋转条件下的工作稳定性,对其自适应系统进行冲击与振动的研究。将简化的清淤装置导入到ADAMS中,分别在最低转速20r/min和最高转速100r/min时,选取不同刚度系数的弹簧,在有弹簧预压缩量和弹簧预压力的条件下进行振动动力学仿真,提取不同条件下滑块与螺塞的冲击曲线、滑块对弹簧的冲击曲线,验证了刚性冲击系数远大于柔性冲击系数;对自适应系统添加柔性元件,得到自适应系统的冲击和振动曲线,并对结果进行对比分析。结果表明:低转速时选择较大弹簧刚度,高转速时选择较低弹簧刚度;比较系统冲击力和冲击系数,应在高速和弹簧预压力为(35~100)N条件下工作更稳定;系统需添加柔性缓冲元件,可极大减小冲击力和系统振动。  相似文献   
2.
为了减少机器人导航路径长度和路径规划时间,提出了基于自主选择搜索策略蜂群算法的规划方法。分析了人工蜂群算法原理,依据蜜蜂从自身认知、种群认知和其他个体认知等多种环境认知方式,对应给出了多种蜜源搜索方式;通过建立不同蜜源搜索方式的即时价值和后效价值模型,计算了蜜蜂选择不同蜜源搜索方式的概率,从而给出了蜜蜂对蜜源搜索方式的自主选择策略,在以上基础上提出了自主选择搜索策略蜂群算法。使用坐标旋转法将二维路径规划问题转化为一维,设计了两种环境下的导航路径规划仿真实验,在两种环境下自主选择搜索策略蜂群算法规划的路径长度均远远小于人工蜂群算法,且搜索到最优值的迭代次数也远远小于人工蜂群算法,充分证明了自主选择搜索策略蜂群算法在导航路径规划中的有效性。  相似文献   
3.
为提高快递分拣效率,设计了一种基于OpenMV机器视觉模块的快递分拣机器人控制系统。在这一控制系统中,通过机器视觉技术实现快递件的轮廓识别、定位、扫码及分类,将STM32单片机作为运动控制核心,外接传感器感知机器人的状态,基于串口与机器视觉模块双向通信,实现对放置在地面的快递件进行自动分拣。通过制作样机验证了设计的合理性与可行性。  相似文献   
4.
提出了一种无传感器零力控制算法,来实现机器人的牵引示教功能。首先,根据机器人的结构特点建立机器人动力学模型;其次,将采集的混杂有高频噪声的驱动器数据进行滤波得到了可供辨识使用的有效信息数据;再次,结合摩擦力数据分布不规律的特性,采用切比雪夫拟合法对摩擦力模型进行辨识,模拟牵引到停止过程中完整的加减速过程中的摩擦力补偿变化,并分析了其相对于传统的最小二乘法的优势;最后,通过拟合实验证明本算法的准确性,验证机器人本体在工程上的有效性,并根据操作的实际情况分析了拟合次数对于摩擦模型准确度的影响。  相似文献   
5.
Human–Robot Collaboration (HRC) is a term used to describe tasks in which robots and humans work together to achieve a goal. Unlike traditional industrial robots, collaborative robots need to be adaptive; able to alter their approach to better suit the situation and the needs of the human partner. As traditional programming techniques can struggle with the complexity required, an emerging approach is to learn a skill by observing human demonstration and imitating the motions; commonly known as Learning from Demonstration (LfD). In this work, we present a LfD methodology that combines an ensemble machine learning algorithm (i.e. Random Forest (RF)) with stochastic regression, using haptic information captured from human demonstration. The capabilities of the proposed method are evaluated using two collaborative tasks; co-manipulation of an object (where the human provides the guidance but the robot handles the objects weight) and collaborative assembly of simple interlocking parts. The proposed method is shown to be capable of imitation learning; interpreting human actions and producing equivalent robot motion across a diverse range of initial and final conditions. After verifying that ensemble machine learning can be utilised for real robotics problems, we propose a further extension utilising Weighted Random Forest (WRF) that attaches weights to each tree based on its performance. It is then shown that the WRF approach outperforms RF in HRC tasks.  相似文献   
6.
Key areas of robot agility include methods that increase capability and flexibility of industrial robots and facilitate robot re-tasking. Manual guidance can achieve robot agility effectively, provided that a safe and smooth interaction is guaranteed when the user exerts an external force on the end effector. We approach this by designing an adaptive admittance law that can adjust its parameters to modify the robot compliance in critical areas of the workspace, such as near and on configuration singularities, joint limits, and workspace limits, for a smooth and safe operation. Experimental validation was done with two tests: a constraint activation test and a 3D shape tracing task. In the first one, we validate the proper response to constraints and in the second one, we compare the proposed approach with different admittance parameter tuning strategies using a drawing task where the user is asked to guide the robot to trace a 3D profile with an accuracy or speed directive and evaluate performance considering path length error and execution time as metrics, and a questionnaire for user perception. Results show that appropriate response to individual and simultaneous activation of the aforementioned constraints for a safe and intuitive manual guidance interaction is achieved and that the proposed parameter tuning strategy has better performance in terms of accuracy, execution time, and subjective evaluation of users.  相似文献   
7.
In the robotic eye-in-hand measurement system, a hand-eye calibration method is essential. From the perspective of 3D reconstruction, this paper first analyzes the influence of the line laser sensor hand-eye calibration error on the 3D reconstructed point clouds error. Based on this, considering the influence of line laser sensor measurement errors and the need for high efficiency and convenience in robotic manufacturing systems, this paper proposes a 3D reconstruction-based robot line laser hand-eye calibration method. In this method, combined with the point cloud registration technique, the newly defined error-index more intuitively reflects the calibration result than traditional methods. To raise the performance of the calibration algorithm, a Particle Swarm Optimization - Gaussian Process (PSO-GP) method is adopted to improve the efficiency of the calibration. The experiments show that the Root Mean Square Error (RMSE) of the reconstructed point cloud can reach 0.1256 mm when using the proposed method, and the reprojection error is superior to those using traditional hand-eye calibration methods.  相似文献   
8.
With the number of hospital stays increasing, nurses require more training to handle a variety of patients. However, time for training in nursing schools is limited, and students lack the opportunity to practice on a diverse variety of patients. Using a robot to simulate actual patients, this study observes the learning transfer effect of practice on practice-similar and practice-dissimilar skills from one patient to another, and investigates which types of practice suit which kinds of training. An experiment was conducted by administering a pre-test, practice, a post-test, and a transfer test to two groups (N?=?8), each with different practice-related skills. The evaluation used a checklist covering required skills that were either similar or dissimilar across groups, depending on their practice. The effect of practice can be observed through a comparison of skills similar to one group but dissimilar to the other. The results show that practice facilitates learning transfer on similar skills but not, or to a lesser degree, on dissimilar skills. Furthermore, if skills needed to handle given symptoms are unfamiliar or inaccessible to students, practice related to those symptoms should be emphasized through simulated training with robots.  相似文献   
9.
This study considers a flowshop type production system consisting of m machines. A material handling robot transports the parts between the machines and loads and unloads the machines. We consider the sequencing of the robot moves and determining the speeds of these moves simultaneously. These decisions affect both the robot’s energy consumption and the production speed of the system. In this study, these two objectives are considered simultaneously. We propose a second order cone programming formulation to find Pareto efficient solutions. We also develop a heuristic algorithm that finds a set of approximate Pareto efficient solutions. The conic formulation can find robot schedules for small cells with less number of machines in reasonable computation times. Our heuristic algorithm can generate a large set of approximate Pareto efficient solutions in a very short computational time. Proposed solution approaches help the decision-maker to achieve the best trade-off between the throughput of a cell and the energy efficiency of a material handling robot.  相似文献   
10.
This study focuses on the accurate tracking control and sensorless estimation of external force disturbances on robot manipulators. The proposed approach is based on an adaptive Wavelet Neural Network (WNN), named Adaptive Force-Environment Estimator (WNN-AFEE). Unlike disturbance observers, WNN_AFEE does not require the inverse of the Jacobian transpose for computing the force, thus, it has no computational problem near singular points. In this scheme, WNN estimates the external force disturbance to attenuate its effects on the control system performance by estimating the environment model. A Lyapunov based design is presented to determine adaptive laws for tuning WNN parameters. Another advantage of the proposed approach is that it can estimate the force even when there are some parametric uncertainties in the robot model, because an additional adaptive law is designed to estimate the robot parameters. In a theorem, the stability of the closed loop system is proved and a general condition is presented for identifying the force and robot parameters. Some suggestions are provided for improving the estimation and control performance. Then, a WNN-AFEE is designed for a planar manipulator as an example, and some simulations are performed for different conditions. WNN_AFEE results are compared attentively with the results of an adaptive force estimator and a disturbance estimator. These comparisons show the efficiency of the proposed controller in dealing with different conditions.  相似文献   
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