三连杆移动机械臂模型与运动规划

采用拉格朗日动力学方法和非完整动力学罗兹方程建立了三连杆移动机器臂运动学和动力学模型,并且利用该模型采用了人工势函数方法来驱动移动机械臂系统绕过障碍物到达目标位置.仿真的结果证明了该模型的正确性及其规划方法是有效的.     

Real-time collision-free motion planning of a mobile robot using a Neural Dynamics-based approach

A neural dynamics based approach is proposed for real-time motion planning with obstacle avoidance of a mobile robot in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation or an additive equation. The real-time collision-free robot motion is planned through the dynamic neural activity landscape of the neural network without any learning procedures and without any local collision-checking procedures at each step of the robot movement. Therefore the model algorithm is computationally simple. There are only local connections among neurons. The computational complexity linearly depends on the neural network size. The stability of the proposed neural network system is proved by qualitative analysis and a Lyapunov stability theory. The effectiveness and efficiency of the proposed approach are demonstrated through simulation studies.     

Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field

A new fuzzy-based potential field method is presented in this paper for autonomous mobile robot motion planning with dynamic environments including static or moving target and obstacles. Two fuzzy Mamdani and TSK models have been used to develop the total attractive and repulsive forces acting on the mobile robot. The attractive and repulsive forces were estimated using four inputs representing the relative position and velocity between the target and the robot in the x and y directions, in one hand, and between the obstacle and the robot, on the other hand. The proposed fuzzy potential field motion planning was investigated based on several conducted MATLAB simulation scenarios for robot motion planning within realistic dynamic environments. As it was noticed from these simulations that the proposed approach was able to provide the robot with collision-free path to softly land on the moving target and solve the local minimum problem within any stationary or dynamic environment compared to other potential field-based approaches.     

Real-time localization of an UAV using Kalman filter and a Wireless Sensor Network

A real-time localization algorithm is presented in this paper. The algorithm presented here uses an Extended Kalman Filter and is based on time difference of arrivals (TDOA) measurements of radio signal. The position and velocity of an Unmanned Aerial Vehicle (UAV) are successfully estimated in closed-loop in real-time in both hover and path following flights. Relatively small position errors obtained from the experiments, proves a good performance of the proposed algorithm.     

自主移动机器人定位系统中Kalman滤波算法改进*

为了解决常规Kalman滤波算法在移动机器人定位过程中运算量大、精度不高的问题,在分析传统Kalman滤波器缺点的基础上,提出了一种基于UT参数变换的方法对常规Kalman滤波算法进行了改进。改进后的Kalman滤波算法消减了传统Kalman滤波器高阶项无法忽略而带来的误差。实验结果表明,改进型的Kalman滤波算法使机器人的最大位置偏差得到减小,对移动机器人的定位精度有明显改善,误差仿真曲线表明,改进后的定位结果误差波动不明显,使定位系统的稳定性得到了较大提高。     

Intelligent mobile manipulator navigation using adaptive neuro-fuzzy systems

The work presented in this paper deals with the problem of autonomous and intelligent navigation of mobile manipulator, where the unavailability of a complete mathematical model of robot systems and uncertainties of sensor data make the used of approximate reasoning to the design of autonomous motion control very attractive.A modular fuzzy navigation method in changing and dynamic unstructured environments has been developed. For a manipulator arm, we apply the robust adaptive fuzzy reactive motion planning developed in [J.B. Mbede, X. Huang, M. Wang, Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators, IEEE Transactions on Fuzzy Systems 11 (2) (2003) 249-261]. But for the vehicle platform, we combine the advantages of probabilistic roadmap as global planner and fuzzy reactive based on idea of elastic band. This fuzzy local planner based on a computational efficient processing scheme maintains a permanent flexible path between two nodes in network generated by a probabilistic roadmap approach. In order to consider the compatibility of stabilization, mobilization and manipulation, we add the input of system stability in vehicle fuzzy navigation so that the mobile manipulator can avoid stably unknown and/or dynamic obstacles. The purpose of an integration of robust controller and modified Elman neural network (MENN) is to deal with uncertainties, which can be translated in the output membership functions of fuzzy systems.     

Observer Kalman filter identification of an autonomous underwater vehicle

This paper deals with the identification of linear discrete-time multivariable models of an autonomous underwater vehicle (AUV). The observer Kalman filter identification (OKID) method is applied with the main objective of evaluating its effectiveness to the experimental identification of the dynamic behaviour of an AUV. After presenting the mathematical background of the OKID algorithm, the proposed method is first validated on the basis of simulated data of both the linearized and nonlinear yaw dynamics of an AUV. Subsequently, the identification algorithm is applied to a set of experimental data. Results suggest that the method can be an efficient tool for the experimental identification of AUV dynamics.     

基于模糊自适应Kalman滤波的GPS/DR数据融合

针对标准Kalman滤波器对系统模型依赖性强、鲁棒性差,而GPS/DR系统的准确数学模型难以建立的问题,提出了一种模糊自适应联邦卡尔曼滤波器(FAFKF).首先通过模糊自适应滤波控制器监控观测量的残差理论值和实际值,并通过实时增强它们的一致性来调整各子系统观测噪声方差阵,使之更符合真实的模型,有效提高了Kalman滤波器对模型变化的适应能力.然后通过模糊自适应信息融合控制器对各子系统可信度进行模糊评判,并根据可信度自适应地计算信息分配系数来实现数据的融合.理论分析和实验数据表明该滤波器在滤波精度、容错性能上都有了很大的提高.     

Trajectory tracking for an autonomous airship using fuzzy adaptive sliding mode control

We present a novel control approach for trajectory tracking of an autonomous airship.First,the dynamics model and the trajectory control problem of an airship are formulated.Second,the sliding mode control law is designed to track a time-varying reference trajectory.To achieve better control performance,fuzzy adaptive sliding mode control is proposed in which the control gains are tuned according to fuzzy rules,and an adaptation law is used to guarantee that the control gains can compensate for model uncertainties of the airship.The stability of the closed-loop control system is proven via the Lyapunov theorem.Finally,simulation results illustrate the effectiveness and robustness of the proposed control scheme.     

A learning fuzzy algorithm for motion planning of mobile robots

A sensor-based fuzzy algorithm is proposed to navigate a mobile robot in a 2-dimensional unknown environment filled with stationary polygonal obstacles. When the robot is at the starting point, vertices of the obstacles that are visible from the robot are scanned by the sensors and the one with the highest priority is chosen. Here, priority is an output fuzzy variable whose value is determined by fuzzy rules. The robot is then navigated from the starting point to the chosen vertex along the line segment connecting these two points. Taking the chosen vertex as the new starting point, the next navigation decision is made. The navigation process will be repeated until the goal point is reached.In implementation of fuzzy rules, the ranges of fuzzy variables are parameters to be determined. In order to evaluate the effect of different range parameters on the navigation algorithm, the total traveling distance of the robot is defined as the performance index first. Then a learning mechanism, which is similar to the simulated annealing method in the neural network theory, is presented to find the optimal range parameters which minimize the performance index. Several simulation examples are included for illustration.     

Compliant motion using a mobile manipulator: an operational space formulation approach to aircraft canopy polishing

The operational space formulation provides a framework for the analysis and control of robotic systems with respect to interactions with their environments. In this paper, we discuss its implementation on a mobile manipulator programmed to polish an aircraft canopy with a curved surface of unknown geometry. The polishing task requires the robot to apply a specified normal force on the canopy surface while simultaneously performing a compliant motion keeping the surface of the grinding tool tangentially in contact with the workpiece. A human operator controls the mobile base via a joystick to guide the polishing tool to desired areas on the canopy surface, effectively increasing the mobile manipulator's reachable workspace. The results demonstrate the efficacy of compliant motion and force regulation based on the operational space formulation for robots performing tasks in unknown environments with robustness towards base motion disturbances. The mobile manipulator consists of a PUMA 560 arm mounted on top of a Nomad XR4000 mobile base. Implementation issues are discussed and experimental results are shown.     

Contact force and torque sensing for serial manipulator based on an adaptive Kalman filter with variable time period

Contact force and torque sensing approaches enable manipulators to cooperate with humans and to interact appropriately with unexpected collisions. In this paper, a mode-switching moving average with variable time period is proposed to reduce the effects of measured motor current noise and thus provide improved confidence in joint output torque estimation. The time period of the filter adapts continuously to achieve optimal tradeoff between response time and precision of estimation in real-time. An adaptive Kalman filter that consists of the proposed moving average and the classical Kalman filter is proposed. Calibration routines for the adaptive Kalman filter take the measured motor current noise and errors in the speed data from the individual joints into account. The combination of the proposed adaptive Kalman filter with variable time period and its calibration method facilitates force and torque estimation without force/torque sensors. Contact force/torque sensing and response time assessments from the proposed approach were performed on the Universal Robot 5 manipulator with differing unexpected end effector loads. The combined force and torque sensing method led to a reduction of the estimation errors and response time in comparison with the pioneering method, and the effect is further improved as the payload rises. The proposed method can be applied to any robotic manipulators as long as the motor information (current, joint position, and joint velocities) are available and consequently the cost will be reduced dramatically from methods that require load cells.     

Door-opening behavior of an autonomous mobile manipulator by sequence of action primitives

A goal of this research is to accomplish a long distance navigation task by an autonomous mobile manipulator, including a behavior of “Passing through a doorway.” In our approach to this problem, we apply the concept of action primitives to the mobile manipulator control system. Action primitives are defined as unit elements of a complex behavior (such as door opening behavior), which control a robot according to a sequence of planned motion primitives. An important feature of the concept is that each action primitive is designed to include an error adjustment mechanism to cope with the accumulated position error of the mobile base. In this article, we report on the design and implementation of action primitives for a door opening task, and show experimental results for “Passing through a doorway” by an autonomous mobile manipulator using sequences of action primitives. © 1996 John Wiley & Sons, Inc.     

Task scheduling and motion planning for an industrial manipulator

In many robotic industrial applications, a manipulator should move among obstacles and reach a set of task-points in order to perform a pre-defined task. It is quite important as well as very complicated to determine the time-optimum sequence of the task-points visited by the end-effector's tip only once assuring that the manipulator's motion through the successive task-points is collision-free.     

Control of an omnidirectional spherical mobile robot using an adaptive Mamdani-type fuzzy control strategy

Neural Computing and Applications - In this study, a supervisory control system with an adaptive Mamdani-type fuzzy controller (AMFC) is used to control an omnidirectional spherical mobile robot...     

Construction of self-learning fuzzy controllers using autonomous adaptive control methodology

A method of constructing adaptive fuzzy controllers using autonomous adaptive control methodology is considered. Knowledge in the system is represented in the form of fuzzy production rules. New rules are automatically generated by clustering empirical data obtained using substractive method in the course of system operation. The system is adapted with application of a special quantity calculated for each rule, the “adequacy degree”, which specifies the weight of the rule in the course of control. The method developed can be used for constructing applied control systems of dynamic objects. This opportunity is shown experimentally using the problems of balancing an inverted pendulum and stabilizing the angular motion of a spacecraft.     

Online tuning fuzzy PID controller using robust extended Kalman filter

Fuzzy PID controllers have been developed and applied to many fields for over a period of 30 years. However, there is no systematic method to design membership functions (MFs) for inputs and outputs of a fuzzy system. Then optimizing the MFs is considered as a system identification problem for a nonlinear dynamic system which makes control challenges. This paper presents a novel online method using a robust extended Kalman filter to optimize a Mamdani fuzzy PID controller. The robust extended Kalman filter (REKF) is used to adjust the controller parameters automatically during the operation process of any system applying the controller to minimize the control error. The fuzzy PID controller is tuned about the shape of MFs and rules to adapt with the working conditions and the control performance is improved significantly. The proposed method in this research is verified by its application to the force control problem of an electro-hydraulic actuator. Simulations and experimental results show that proposed method is effective for the online optimization of the fuzzy PID controller.     

Sensor-based fuzzy navigation of an autonomous mobile robot in an indoor environment

The work presented in this paper deals with the problem of navigating a mobile robot either in an unknown indoor environment or in a partially known one. A navigation method based on the combination of elementary behaviors has been developed for an unknown environment. Most of these behaviors are achieved by means of fuzzy inference systems. The proposed navigator combines two types of obstacle avoidance behaviors, one for the convex obstacles and one for the concave ones. In the case of a partially known environment, a hybrid method is used to exploit the advantages of global and local navigation strategies. The coordination of these strategies is based on a fuzzy inference system that involves an on-line comparison between the real scene and a memorized one. Both methods have been implemented on the miniature mobile robot Khepera^® which is equipped with rough sensors. The good results obtained illustrate the robustness of a fuzzy logic approach with regard to sensor imperfections.     

Integrated planning and execution control for an autonomous mobile robot

We present a distributed hierarchical planning and execution monitoring system and its implementation on an actual mobile robot. The planning system is a distributed hierarchical domain independent system called FPS for Flexible Planning System. It is a rule based plan generation system with planning specific and domain specific rules. A planning solution to the ‘Boxes and Wedge’ Problem is presented.The Robot Control System (RCS) operates and monitors the robot in the real world. In order to allow real-time responses to asynchronous events (both internal and external), RCS consists of a rule-based decision kernel and a distributed set of sensor/effector monitors. RCS contains an execution model and may authorize local corrective actions, e.g., unexpected obstacle avoidance during execution of a trajectory. RCS also generates status and failure reports through which the PMs inform the different decision subsystems as to the robot's state and current capacities. The failure reports help the RCS and planners in correcting/replanning a plan that has aborted. An illustrative example of system behaviour is to be presented.     

采用反馈路径规划的航天器近程安全交会对接

航天器近距离交会对接过程中需要满足禁飞区,对接走廊等约束,否则会有航天器相碰或者损毁的风险.本文研究了一种基于线性二次型调节器树(linear quadratic regulator trees, LQR-Trees)反馈路径规划的安全交会对接的路径规划与控制方法.首先采用非线性规划算法(nonlinear programming, NLP)计算开环标称轨迹,并应用局部线性反馈控制算法生成闭环控制律使得系统状态保持在标称轨迹附近;然后采用平方和(sums-of-squares, SOS)凸优化方法,沿着标称轨迹附近计算反向可达集;最后采用相对稀疏的多个稳定域生成轨迹库覆盖大范围的状态空间,扩展稳定区域.本文首次提出采用6维的LQR-Trees算法的安全对接轨迹规划与控制方法,并重新设计了控制饱和约束.仿真结果验证了该算法在交会对接安全轨迹规划与控制中的有效性,通过设计5个稳定域实现了满足对接走廊约束的安全交会对接任务.