FUZZY COORDINATION AND FORCE/POSITION CONTROL OF ROBOTIC MANIPULATOR

It is crucial for implementing force/position control of robotic manipulator under the constraint of unknown environment to determine the force control and the position control directions. This paper presents an on line algorithm to real timely estimate the tangent and the normal vectors of the constraint surface based on the measured contact force under the consideration of frictional force. A fuzzy synthesis policy is proposed to coordinate the conflict between the compliant force control and the stiff position control. An experimental study on an AdeptThree, a SCARA type robotic manipulator, is conducted. The experimental results show that the policy presented in the paper is effective.

Adaptive hybrid force/position control for an industrial robot： theory and experiments

This paper proposes a feasible force/position control method for industrial robots utilized for such tasks as grinding, polishing, deburring, and so on. Specifically, an adaptive force/position control strategy is designed in this paper which regulates the contact force between a robot and a workpiece to reach any given set-point exponentially fast, and enables the robot to follow a chosen trajectory simultaneously without requiring prior knowledge of the system parameters. The stability of the closed-loop system is analyzed by Lyapunov techniques. To test the validity of the force/position control method, some simulation results are first collected for the closed-loop system. Furthermore, some experiments are implemented on a 5DOF （degree of freedom） industrial robot for the constructed adaptive force controller. Both simulation and experiment results demonstrate the superior performance of the designed adaptive force/position control strategy.

Impedance force control for position controlled robotic manipulators under the constraint of unknown environments

A force control strategy for position controlled robotic manipulators is presented. On line force feedback data are employed to estimate the local shape of the unknown constraint. The estimated vectors are used to generate the virtual reference trajectory for the target impedance model that is driven by the force error to produce command position. By following the command position trajectory the robotic manipulator can follow the unknown constraint surface while keeping an acceptable force error in a manner depicted by the target impedance model. Computer simulation on a 3 linked planar manipulator and experimental studies on an Adept 3, an SCARA type robotic manipulator, are conducted to verify the force tracking capability of the proposed control strategy.

Decentralized adaptive neural network sliding mode position/force control of constrained reconfigurable manipulators

A decentralized adaptive neural network sliding mode position/force control scheme is proposed for constrained reconfigurable manipulators. Different from the decentralized control strategy in multi-manipulator cooperation, the proposed decentralized position/force control scheme can be applied to series constrained reconfigurable manipulators. By multiplying each row of Jacobian matrix in the dynamics by contact force vector, the converted joint torque is obtained. Furthermore, using desired information of other joints instead of their actual values, the dynamics can be represented as a set of interconnected subsystems by model decomposition technique. An adaptive neural network controller is introduced to approximate the unknown dynamics of subsystem. The interconnection and the whole error term are removed by employing an adaptive sliding mode term. And then, the Lyapunov stability theory guarantees the stability of the closed-loop system. Finally, two reconfigurable manipulators with different configurations are employed to show the effectiveness of the proposed decentralized position/force control scheme.

Design,analysis and control for an antarctic modular manipulator

Antarctic scientific expedition has important strategic significance. It is an inevitable trend to apply robots to assist researchers during the Antarctic expedition. However, the robot manipula- tors at present have a series of problems and unable to meet the requirements of the Antarctic expe- dition. In this paper, a novel Antarctic modular robot manipulator is proposed, which has a compact structure with modular joints. The robot manipulator has high reliability, and quick assembling-and- disassembling ability. Through well wires arranging and thermal controlling, the manipulator can better adapt to the Antarctic environment. In addition, the work space of the manipulator is serious- ly analyzed, and a new hybrid position/force control method is adopted to make the manipulator per- form better. Simulation results validate the control method and show that the robot manipulator has a good performance to meet the requirements of Antarctic expedition.

Mechanical Design and Drive Control of a Novel Dexterous Hand for On-Orbit Servicing

In order to meet the requirements of on-orbit servicing outside the cabin, a flexible, dexterous hand with easy grasping ability and strong loading capacity is designed. The dexterous hand is comprised of three fingers. Each finger is driven by a set of four linkages. Furthermore, two fingers have a set of axial rotational degrees of freedom. In order to achieve the position control and keep griping stability, the dexterous hand adopts a mechanism of hybrid force/position control. In the end, experimental results demonstrates that the on-orbit servicing dexterous hand has great adaptability and operational capability.

Hybrid H_2/H_∞ force/position control based on neural networks

A neural network control scheme with mixed H2/H∞performance was proposed for robot force/position control under parameter uncertainties and external disturbances. The mixed H2/H∞tracking performance ensures both robust stability under a prescribed attenuation level for external disturbance and H2optimal tracking. The neural network was introduced to adaptively estimate nonlinear uncertainties, improving the system’s performance under parameter uncertainties as well as obtaining the H2/H∞tracking performance. The simulation shows that the control method performs better even when the system is under large modeling uncertainties and external disturbances.

Application of Artificial Neural Network in Robotic Hybrid Position/Force Control

A hybrid position/force controller is designed for the joint 2 and the joint 3 of thePUMA 560 robot.The hybrid controller includes a multilayered neural network,which canidentify the dynamics of the contacted environment and can optimize the parameters of PIDcontroller.The experimental results show that after having been trained,the robot has sta-ble response to the training patterns and strong adaptive ability to the situation between thepatterns.

Robot Position Control Using Force Information for Cooperative Work in Remote Robot Systems with Force Feedback

This paper proposes robot position control using force information for cooperative work between two remote robot systems with force feedback in each of which a user operates a remote robot by using a haptic interface device while observing work of the robot with a video camera. We also investigate the effect of the proposed control by experiment. As cooperative work, we deal with work in which two robots carry an object together. The robot position control using force information finely adjusts the position of the robot arm to reduce the force applied to the object. Thus, the purpose of the control is to avoid large force so that the object is not broken. In our experiment, we make a comparison among the following three cases in order to clarify how to carry out the control effectively. In the first case, the two robots are operated manually by a user with his/her both hands. In the second case, one robot is operated manually by a user, and the other robot is moved automatically under the proposed control. In the last case, the object is carried directly by a human instead of the robot which is operated by the user in the second case. As a result, experimental results demonstrate that the control can help each system operated manually by the user to carry the object smoothly.

Intelligent Control of Multi-fingered Robot Hand

The dynamics properties of a kind of multi-fingered robot hand is analyzed. It is pointed out that the dynamics property of this kind of multifingered robot hand in the approaching process is quite different from that in the grasping process and,different control algorithm should be taken in the two process. A position-force hybrid control algorithm is proposed which is applied to the control system of the University of Science and Technology Beijing double-thumb robot hand successfully.

A Review of Robotic Force/Position Control

This paper reviews robotic position and force control techniques. The existing fundamental force control algorithms are compared and discussed, including explicit/implicit control, stiffness control, impedance/admit- tance control, and hybrid position/force control. This work is intended to give a basic guidance for understanding and utilization of the fundamental robotic control algorithm.

Robust scheme of global parallel force/position regulators for robot manipulators under environment uncertainty

A simple robust scheme of parallel force/position control is proposed in this paper to deal with two problems for non-planar constraint surface and nonlinear mechanical feature of environment： i） uncertainties in environment that are usually not available or difficult to be determined in most practical situations; ii） stability problem or/and integrator windup due to the integration of force error in the force dominance rule in parallel force/position control. It shows that this robust scheme is a good alternative for anti-windup. In the presence of environment uncertainties, global asymptotic stability of the resulting closed-loop system is guaranteed; it environment with complex characteristics. Finally, numerical robot manipulator. also shows robustness of the proposed controller to uncertain simulation verifies results via contact task of a two rigid-links

Collision avoidance for a mobile robot based on radial basis function hybrid force control technique

Collision avoidance is always difficult in the planning path for a mobile robot. In this paper, the virtual force field between a mobile robot and an obstacle is formed and regulated to maintain a desired distance by hybrid force control algorithm. Since uncertainties from robot dynamics and obstacle degrade the performance of a collision avoidance task, intelligent control is used to compensate for the uncertainties. A radial basis function （RBF） neural network is used to regulate the force field of an accurate distance between a robot and an obstacle in this paper and then simulation studies are conducted to confirm that the proposed algorithm is effective.

Design and performance analysis of position-based impedance control for an electrohydrostatic actuation system

Electrohydrostatic actuator（EHA） is a type of power-by-wire actuator that is widely implemented in the aerospace industry for flight control, landing gears, thrust reversers, thrust vector control, and space robots. This paper presents the development and evaluation of positionbased impedance control（PBIC） for an EHA. Impedance control provides the actuator with compliance and facilitates the interaction with the environment. Most impedance control applications utilize electrical or valve-controlled hydraulic actuators, whereas this work realizes impedance control via a compact and efficient EHA. The structures of the EHA and PBIC are firstly introduced. A mathematical model of the actuation system is established, and values of its coefficients are identified by particle swarm optimization. This model facilitates the development of a position controller and the selection of target impedance parameters. A nonlinear proportional-integral position controller is developed for the EHA to achieve the accurate positioning requirement of PBIC. The controller compensates for the adverse effect of stiction, and a position accuracy of 0.08 mm is attained.Various experimental results are presented to verify the applicability of PBIC to the EHA. The compliance of the actuator is demonstrated in an impact test.

A buffering method for quadruped robots based on active impedance control

Compared with wheeled or tracked robots,legged robots exhibit advantages on agile locomotion and higher survival chance for deadly impacts. A buffering strategy is proposed for quadruped robots with non-extreme initial attitudes from the end of air-righting to the steady standing on the ground.This approach consists of landing phase,buffering phase and recovering phase. The variable stiffness control,proportional-derivative( PD) force control and foot trajectory planning are applied to the joints of quadruped robots until the end of the recovering phase. The PD parameters are tuned according to the desired performance of each phase. The above approach is verified on a virtual platform.

Decoupling Control Research on Test System of Hydraulic Drive Unit of Quadruped Robot Based on Diagonal Matrix Method

The mathematical model of hydraulic drive unit of quadruped robot was built in this paper. According to the coupling characteristics between position control system and force control system, the decoupling control strategy was realized based on diagonal matrix method in AMESim?. The results of simulation show that using diagonal matrix method can achieve the decoupling control effectively and it can achieve the decoupling control more effectively with the method of not offset pole-zero in the S coordinate. This research can provide theoretical basis for the application of test system of hydraulic drive unit.

Research on the Coordinated Control of the Multi-point Array Support Force of Complex Structural Parts

Multi-point array flexible tooling based on multilateration is widely used in the processing and manufacturing of complex curved surface parts. However, during the positioning of workpieces, the force exerted on each flexible support point is not uniform, and there exists force coupling between the support units. In response to the force coupling problem in the multi-point array positioning support process, a coordinated control method for the support force of multi-point array positioning combining correlation coefficient and regression analysis was proposed in this paper. The Spearman correlation coefficient was adopted in this method to study the force coupling correlation between positioning points, and a mathematical model of force coupling was established between positioning units through regression analysis, which can quickly and accurately perform coordinated control of the multilateration support system, and effectively improve the force interference of the multi-point array positioning support scene.

Study on the Robot Robust Adaptive Control Based on Neural Networks

Force control based on neural networks is presented. Under the framework of hybrid control, an RBF neural network is used to compensate for all the uncertainties from robot dynamics and unknown environment first. The technique will improve the adaptability to environment stiffness when the end-effector is in contact with the environment, and does not require any a priori knowledge on the upper bound of syste uncertainties. Moreover, it need not compute the inverse of inertia matrix. Learning algorithms for neural networks to minimize the force error directly are designed. Simulation results have shown a better force/position tracking when neural network is used.

煤矿掘进机自定位截割控制方法及试验研究

开展了掘进机自定位截割控制方法及试验研究,提出了掘进机自定位截割控制策略,使用基于单目视觉与深度学习的掘进机机体六自由度位姿检测方法,以及具有规定性能的双层模糊自适应反步控制方法实现了掘进机自定位截割,并将其用于机体任意位姿下的截割头循迹跟踪控制。在掘进机两种不同的机体位姿下进行了截割头循迹跟踪控制试验,试验结果显示最大轮廓误差分别在48 mm和52 mm(约2.14%与2.32%)以内,验证了提出的掘进机自定位截割控制方法的有效性。

基于强化学习的多段连续体机器人轨迹规划

针对多段连续体机器人的轨迹规划问题,提出了一种基于深度确定性策略梯度强化学习的轨迹规划算法。首先,基于分段常曲率假设方法,建立连续体机器人的关节角速度和末端位姿的正向运动学模型。然后,采用强化学习算法,将机械臂的当前位姿和目标位姿等信息作为状态输入,将机械臂的关节角速度作为智能体的输出动作,设置合理的奖励函数,引导机器人从初始位姿向目标位姿移动。最后,在MATLAB中搭建仿真系统,仿真结果表明,强化学习算法成功对多段连续体机器人进行轨迹规划,控制连续体机器人的末端平稳运动到目标位姿。