Projection operator-based robust adaptive control of an aerial robot with a manipulator

This paper describes a quadcopter manipulator system, an aerial robot with an extended workspace, its controller design, and experimental validation. The aerial robot is based on a quadcopter with a three degree of freedom robotic arm connected to the base of the vehicle. The work aims to create a stable airborne robot with a robotic arm that can work above and below the airframe, regardless of where the arm is attached. Integrating a robotic arm into an underactuated, unstable system like a quadcopter can enhance the vehicle's functionality while increasing instability. To execute a mission with accuracy and reliability during a real-time task, the system must overcome the inter-coupling effects and external disturbances. This work presents a novel design for a robust adaptive feedback linearization controller with a model reference adaptive controller and hardware implementation of the quadcopter manipulator system with plant uncertainties. The closed-loop stability of the aerial robot and the tracking error convergence with the robust controller is analyzed using Lyapunov stability analysis. The quadcopter manipulator system is custom developed in the lab with an off-the-shelf quadcopter and a 3D-printed robotic arm. The robotic system architecture is implemented using a Jetson Nano companion computer for autonomous onboard flight. Experiments were conducted on quadcopter manipulator system to evaluate the autonomous aerial robot's stability and trajectory tracking with the proposed controller.     

A Practical Iterative Learning Path‐Following Control Of An Omni‐Directional Vehicle

A new practical iterative learning control (ILC) updating law is proposed to improve the path following accuracy for an omni‐directional autonomous mobile robot. The ILC scheme is applied as a feedforward controller to the existing feedback controller. By using the local symmetrical double‐integral of the feedback control signal of the previous iteration, the ILC updating law takes a simple form with only two design parameters: the learning gain and the range of local integration. Convergence analysis is presented together with a design procedure. Simulation results on a difficult maneuver are presented to illustrate the effectiveness of the proposed simple and yet practical scheme. The simulation is based on the model of a novel robotic platform, the Utah State University (USU) Omni‐Directional Vehicle (ODV), which uses multiple “smart wheels,” whose speed and direction can be independently controlled through dedicated processors for each wheel.     

Experimental investigation on coverage performance of a chaotic autonomous mobile robot

In many autonomous mobile robotic missions the complete and fast coverage of the workspace, scanned by an unpredictable trajectory, plays a crucial role. To satisfy these special demands in the design of an autonomous mobile robot, a motion controller, based on the dynamical behavior of a known discrete chaotic system, the Logistic map, is presented in this paper. The proposed method is based on a microcontroller for realizing a chaotic random bit generator and converting the produced chaotic bit sequence, to the robot’s trajectory of motion. The experimental results confirm that this approach, with an appropriate sensor for obstacle avoidance, can obtain very satisfactory results in regard to the fast scanning of the robot’s workspace with unpredictable way.     

T-S 模糊模型变结构的机器臂轨迹跟踪控制

针对不确定性的机械臂轨迹跟踪问题,结合滑模变结构和T-S模糊模型的优点,给出一种基于T-S模糊模型的变结构轨迹跟踪的方法。首先采用T-S模型建模,得到机械臂的模糊模型;然后设计出保证机械臂全局渐近稳定的滑模控制器。仿真结果表明,所设计的模糊变结构控制器与普通变结构控制器相比,可使机械臂无论在计算时间、误差上都具有更大的优势和更强的鲁棒性。     

基于ARM的机械臂控制系统分析

以钻孔机械臂为研究对象,进行了以LPC2138为主控制器的嵌入式系统硬件平台设计,以及LPC2138与伺服驱动电路、液晶显示电路、键盘扫描电路等的接口设计,且着重对机械臂执行机构交流伺服电机的驱动进行了设计研究。     

Neuro-fuzzy and model-based motion control for mobile manipulator among dynamic obstacles

This paper focuses on autonomous motion control of a nonholonomic platform with a robotic arm, which is called mobile manipulator. It serves in transportation of loads in imperfectly known industrial environments with unknown dynamic obstacles. A union of both procedures is used to solve the general problems of collision-free motion. The problem of collision-free motion for mobile manipulators has been approached from two directions, Planning and Reactive Control. The dynamic path planning can be used to solve the problem of locomotion of mobile platform, and reactive approaches can be employed to solve the motion planning of the arm. The execution can generate the commands for the servo-systems of the robot so as to follow a given nominal trajectory while reacting in real-time to unexpected events. The execution can be designed as an Adaptive Fuzzy Neural Controller. In real world systems, sensor-based motion control becomes essential to deal with model uncertainties and unexpected obstacles.     

Robotic sanding system for new designed furniture with free-formed surface

In this paper, a sanding system based on an industrial robot with a surface following controller is proposed for the sanding process of wooden materials constructing furniture. Handy air-driven tools can be easily attached to the tip of the robot arm via a compact force sensor. The robotic sanding system is called the 3D robot sander. The robot sander has two novel features. One is that the polishing force acting between the tool and wooden workpiece is delicately controlled to track a desired value, e.g., 2 kgf. The polishing force is defined as the resultant force of the contact force and kinetic friction force. The other is that no complicated teaching operation is required to obtain a desired trajectory of the tool. Cutter location (CL) data, which are tool paths generated by a CAD/CAM system, are directly used for the basic trajectory of the handy tool attached to the robot arm. The robot sander can be applied to the sanding task of free-formed curved surface with which conventional sanding machines have not been able to cope. The effectiveness and promise are shown and discussed through a few experiments.     

A precise robust fuzzy control of robots using voltage control strategy

Fuzzy control of robot manipulators with a decentralized structure is facing a serious challenge. The state-space model of a robotic system including the robot manipulator and motors is in non-companion form, multivariable, highly nonlinear, and heavily coupled with a variable input gain matrix. Considering the problem, causes and solutions, we use voltage control strategy and convergence analysis to design a novel precise robust fuzzy control (PRFC) approach for electrically driven robot manipulators. The proposed fuzzy controller is Mamdani type and has a decentralized structure with guaranteed stability. In order to obtain a precise response, we regulate a fuzzy rule which governs the origin of the tracking space. The proposed design is verified by stability analysis. Simulations illustrate the superiority of the PRFC over a proprotional derivative like (PD-like) fuzzy controller applied on a selective compliant assembly robot arm (SCARA) driven by permanent magnet DC motors.     

A path following algorithm for mobile robots

This paper considers path following control for a robotic platform. The vehicle used for the experiments is a specially designed robotic platform for performing autonomous weed control. The platform is four-wheel steered and four-wheel driven. A diesel engine powers the wheels via a hydraulic transmission. The robot uses a Real Time Kinematic Differential Global Positioning System to determine both position and orientation relative to the path. The deviation of the robot to the desired path is supplied to two high level controllers minimizing the orthogonal distance and orientation to the path. Wheel angle setpoints are determined from inversion of the kinematic model. At low level each wheel angle is controlled by a proportional controller combined with a Smith predictor. Results show the controller performance following different paths shapes including a step, a ramp, and a typical headland path. A refined tuning method calculates controller settings that let the robot drive as much as possible along the same path to its setpoint, but also limit the gains at higher speeds to prevent the closed loop system to become unstable due to the time delay in the system. Mean, minimum and maximum orthogonal distance errors while following a straight path on a paving at a speed of 0.5 m/s are 0.0, −2.4 and 3.0 cm respectively and the standard deviation is 1.2 cm. The control method for four wheel steered vehicles presented in this paper has the unique feature that it enables control of a user definable position relative to the robot frame and can deal with limitations on the wheel angles. The method is very well practical applicable for a manufacturer: all parameters needed are known by the manufacturer or can be determined easily, user settings have an easy interpretation and the only complex part can be supplied as a generic software module.     

Mobile robot design in education

This paper describes the course on robot design that electronics systems engineers take in their last semester at the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM) in Mexico. In this course, the use of project oriented learning (POL) and collaborative learning are proposed. The paper also describes the design and implementation of a modular, low-cost, three-wheeled autonomous robotic platform, to serve as a base platform from which different applications, educational and research, could be mounted.     

Nonsingular Terminal Sliding Mode Control of Robot Manipulators Using Fuzzy Wavelet Networks

This paper presents an adaptive nonsingular terminal sliding mode (NTSM) tracking control design for robotic systems using fuzzy wavelet networks. Compared with linear hyperplane-based sliding control, terminal sliding mode controller can provide faster convergence and higher precision control. Therefore, a terminal sliding controller combined with the fuzzy wavelet network, which can accurately approximate unknown dynamics of robotic systems by using an adaptive learning algorithm, is an attractive control approach for robots. In addition, the proposed learning algorithm can on-line tune parameters of dilation and translation of fuzzy wavelet basis functions and hidden-to-output weights. Therefore, a robust control law is used to eliminate uncertainties including the inevitable approximation errors resulted from the finite number of fuzzy wavelet basis functions. The proposed controller requires no prior knowledge about the dynamics of the robot and no off-line learning phase. Moreover, both tracking performance and stability of the closed-loop robotic system can be guaranteed by Lyapunov theory. Finally, the effectiveness of the fuzzy wavelet network-based control approach is illustrated through comparative simulations on a six-link robot manipulator     

空间机器人控制系统硬件仿真平台的研究

建立了空间机器人控制系统的硬件仿真平台。研究了空间机器人基于手眼视觉的控制问题,建立了系统关键部件的模拟设备。仿真平台由中央控制器、关节模拟器、手眼模拟器、动力学/运动学仿真计算机和三维动画显示计算机组成。基于该平台,对空间机器人控制特性和仿真过程中的延时环节进行了研究。系统自主捕获仿真试验结果表明,所采用的运动控制算法能够稳定收敛于目标,仿真平台能够较好地完成对实际机器人系统控制过程的模拟测试及系统控制算法的验证。     

A Non-Time Based Controller for Load Swing Damping and Path-Tracking in Robotic Cranes

This paper proposes the use of the non-time based control strategy named Delayed Reference Control (DRC) to the control of industrial robotic cranes. Such a control scheme has been developed to achieve two relevant objectives in the control of autonomous operated cranes: the active damping of undesired load swing, and the accurate tracking of the planned path through space, with the preservation of the coordinated Cartesian motion of the crane. A paramount advantage of the proposed scheme over traditional ones is its ease of implementation on industrial devices: it can be implemented by just adding an outer control loop (incorporating path planning) to standard position controllers. Experimental performance assessment of the proposed control strategy is provided by applying the DRC to the control of the oscillation of a cable-suspended load moved by a parallel robot mimicking a robotic crane. In order to implement the DRC scheme on such an industrial robot it has been just necessary to manage path planning and the DRC algorithm on a separate real-time hardware computing the delay in the execution of the desired trajectory suitable to reduce load swing. Load swing has been detected by processing the images from two off-the-shelf cameras with a dedicated vision system. No customization of the robot industrial controller has been necessary.     

OTTER: The design and development of an intelligent underwater robot

Recent advances in sensing and intelligent control technologies open a whole new dimension in underwater autonomy. However, before truly-capable, autonomous underwater robots can be created for subsea intervention and exploration, many research issues must be first investigated and developed experimentally on testbed platforms.OTTER is an underwater robot designed to be used as a testbed for autonomous technologies. Both OTTER's hardware and software systems are configured to support simultaneous development and testing of different concepts for underwater robotic by independent researchers. A general control-software framework enables common access to all subsystems and avoids the duplication of basic robotic functionality jointly required by all projects. Additionally, the new autonomous technologies enabled by the results of individual research are mutually compatible and can be easily integrated into a single robotic system. Examples of new technologies demonstrated on the OTTER underwater robot include control from a real-time vision-sensing system, coordinated arm/vehicle control, and control from 3D graphical user interfaces.     

室内机器人及其路径跟踪网络设计

近年来,室内移动机器人的研究和设计成为关注的焦点。我们采用单片机作为机器人的核心控制器,利用超声波传感器、碰撞传感器、步进电机及其控制芯片Ta8435联合制作开发了机器人实验平台。最后介绍了模糊控制、模糊神经网络,并利用模糊控制和模糊神经网络技术对室内机器人导航中的模糊控制避障和模糊神经网络路径跟踪作了MATLAB仿真研究,达到了预期的目的。     

Fuzzy and Recurrent Neural Network Motion Control among Dynamic Obstacles for Robot Manipulators

An integration of fuzzy controller and modified Elman neural networks (NN) approximation-based computed-torque controller is proposed for motion control of autonomous manipulators in dynamic and partially known environments containing moving obstacles. The fuzzy controller is based on artificial potential fields using analytic harmonic functions, a navigation technique common used in robot control. The NN controller can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the robot arm. The NN weights are tuned on-line, with no off-line learning phase required. The stability of the closed-loop system is guaranteed by the Lyapunov theory. The purpose of the controller, which is designed as a neuro-fuzzy controller, is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics. Sensor-based motion control is an essential feature for dealing with model uncertainties and unexpected obstacles in real-time world systems.     

A Compact/Open Network-Based Controller Incorporating Modular Software Architecture for a Humanoid Robot

This paper presents the design of a compact/open network-based controller incorporating modular software architecture for various kinds of robot applications. Within the proposed controller scheme, a standardized real-time network like CAN connects the central motion control part and the servo control part. Thus, the size of the servo controller becomes small enough to be attached inside the robot body and the control software can be designed with an open and modular concept. The open/compact controller incorporating a modular software architecture offers benefits of reduced engineering costs. The proposed architecture has been implemented on a KIST humanoid robot controller platform and its performance has been verified through experimental tests.     

Tracking control of a closed-chain five-bar robot with two degrees of freedom by integration of an approximation-based approach and mechanical design

The trajectory tracking problem of a closed-chain five-bar robot is studied in this paper. Based on an error transformation function and the backstepping technique, an approximation-based tracking algorithm is proposed, which can guarantee the control performance of the robotic system in both the stable and transient phases. In particular, the overshoot, settling time, and final tracking error of the robotic system can be all adjusted by properly setting the parameters in the error transformation function. The radial basis function neural network (RBFNN) is used to compensate the complicated nonlinear terms in the closed-loop dynamics of the robotic system. The approximation error of the RBFNN is only required to be bounded, which simplifies the initial "trail-and-error" configuration of the neural network. Illustrative examples are given to verify the theoretical analysis and illustrate the effectiveness of the proposed algorithm. Finally, it is also shown that the proposed approximation-based controller can be simplified by a smart mechanical design of the closed-chain robot, which demonstrates the promise of the integrated design and control philosophy.     

Hybrid position–admittance realization of an adaptive output super-twisting controller for a robotic scalpel

This study proposes the design and implementation of a hybrid robust automatic controller based on the application of a high order sliding mode algorithm for a robotic scalpel prototype (RS). Two fully actuated arms with three degrees of freedom constitute the RS, one arm holds the sample and the second one has the scalpel to exert the cutting task. Each arm is attached to its corresponding cartesian robotic platform. The available measurements are the angular displacements, the linear displacement and the force vector describing the interaction between the scalpel and the biological sample. A hybrid position–admittance controller implements an output-based adaptive distributed super-twisting algorithm to mobilize the RS. A high order sliding mode observer estimates the unknown angular and linear velocities that were used in the hybrid controller. Once the end-effector of each arm reaches the desired cutting position, the designed controller switches to the admittance controller to avoid damaging the surrounding tissue. Numerical simulations show the advantages of the suggested controller in comparison with classical algorithms. The hybrid sliding mode admittance controller has been successfully evaluated on an self-constructed platform. The experimental results show a precise cut and efficient mobilization of the RS compared to other classical controllers such as proportional-differentiator, proportional-integral and first order sliding mode controllers.     

Controlling gaze with an embodied interactive control architecture

Human-Robot Interaction (HRI) is a growing field of research that targets the development of robots which are easy to operate, more engaging and more entertaining. Natural human-like behavior is considered by many researchers as an important target of HRI. Research in Human-Human communications revealed that gaze control is one of the major interactive behaviors used by humans in close encounters. Human-like gaze control is then one of the important behaviors that a robot should have in order to provide natural interactions with human partners. To develop human-like natural gaze control that can integrate easily with other behaviors of the robot, a flexible robotic architecture is needed. Most robotic architectures available were developed with autonomous robots in mind. Although robots developed for HRI are usually autonomous, their autonomy is combined with interactivity, which adds more challenges on the design of the robotic architectures supporting them. This paper reports the development and evaluation of two gaze controllers using a new cross-platform robotic architecture for HRI applications called EICA (The Embodied Interactive Control Architecture), that was designed to meet those challenges emphasizing how low level attention focusing and action integration are implemented. Evaluation of the gaze controllers revealed human-like behavior in terms of mutual attention, gaze toward partner, and mutual gaze. The paper also reports a novel Floating Point Genetic Algorithm (FPGA) for learning the parameters of various processes of the gaze controller.