There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

This paper presents a vision guidance approach using an image-based visual servo (IBVS) for an aerial manipulator combining a multirotor with a multidegree of freedom robotic arm. To take into account the dynamic characteristics of the combined manipulation platform, the kinematic and dynamic models of the combined system are derived. Based on the combined model, a passivity-based adaptive controller which can be applied on both position and velocity control is designed. The position control is utilized for waypoint tracking such as taking off and landing, and the velocity control is engaged when the platform is guided by visual information. In addition, a guidance law utilizing IBVS is employed with modifications. To secure the view of an object with an eye-in-hand camera, IBVS is utilized with images taken from a fisheye camera. Also, to compensate underactuation of the multirotor, an image adjustment method is developed. With the proposed control and guidance laws, autonomous flight experiments involving grabbing and transporting an object are carried out. Successful experimental results demonstrate that the proposed approaches can be applied in various types of manipulation missions.

Vision-Guided Aerial Manipulation Using a Multirotor With a Robotic Arm

Agricultural production must double by 2050 in order to meet the expected food demand due to population growth. Precision agriculture is the key to improve productivity and efficiency in the use of resources, thus helping to achieve this goal under the diverse challenges currently faced by agriculture mainly due to climate changes, land degradation, availability of farmable land, labor force shortage, and increasing costs. To face these challenges, precision agriculture uses and develops sensing methodologies that provide information about crop growth and health indicators. This paper presents a survey of the state-of-the-art in optical visible and near-visible spectrum sensors and techniques to estimate phenotyping variables from intensity, spectral, and volumetric measurements. The sensing methodologies are classified into three areas according to the purpose of the measurements: 1) plant structural characterization; 2) plant/fruit detection; and 3) plant physiology assessment. This paper also discusses the progress in data processing methods and the current open challenges in agricultural tasks in which the development of innovative sensing methodologies is required, such as pruning, fertilizer and pesticide management, crop monitoring, and automated harvesting.

A Survey of Ranging and Imaging Techniques for Precision Agriculture Phenotyping

This paper presents a teleoperation control for an exoskeleton robotic system based on the brain–machine interface and vision feedback. Vision compressive sensing, brain–machine reference commands, and adaptive fuzzy controllers in joint-space have been effectively integrated to enable the robot performing manipulation tasks guided by human operator's mind. First, a visual-feedback link is implemented by a video captured by a camera, allowing him/her to visualize the manipulator's workspace and movements being executed. Then, the compressed images are used as feedback errors in a nonvector space for producing steady-state visual evoked potentials electroencephalography (EEG) signals, and it requires no prior information on features in contrast to the traditional visual servoing. The proposed EEG decoding algorithm generates control signals for the exoskeleton robot using features extracted from neural activity. Considering coupled dynamics and actuator input constraints during the robot manipulation, a local adaptive fuzzy controller has been designed to drive the exoskeleton tracking the intended trajectories in human operator's mind and to provide a convenient way of dynamics compensation with minimal knowledge of the dynamics parameters of the exoskeleton robot. Extensive experiment studies employing three subjects have been performed to verify the validity of the proposed method.

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BrainźMachine Interface and Visual Compressive Sensing-Based Teleoperation Control of an Exoskeleton Robot

This paper focuses on a problem of adaptive visual tracking control for an uncalibrated image-based visual servoing manipulator system with actuator fuzzy dead-zone constrain and unknown dynamic. Without a prior knowledge of the system, fuzzy logic systems are employed to approximate the unmodeled nonlinear manipulator dynamics and external disturbances. By using the recursive Newton–Euler method, the total number of fuzzy rules can be reduced significantly as compared with the traditional fuzzy logic system. By defuzzifying the fuzzy slope $\tilde {k}$ of the fuzzy dead-zone model to a deterministic value $\bar {k}$ , a novel fuzzy adaptive controller is constructed to eliminate the harmful effect of fuzzy dead-zone constrain. Lyapunov functions are presented for stability analysis of visual feedback control problem with unknown dynamics and actuator fuzzy dead-zone constrains. Experimental results are carried out to test the visual tracking performance of the proposed controller and the boundedness of the closed-loop system.

Adaptive Visual Tracking Control for Manipulator With Actuator Fuzzy Dead-Zone Constraint and Unmodeled Dynamic

This paper is concerned with the design and implementation of a distributed proportional-derivative (PD) controller of a 7-degrees of freedom (DOF) robot manipulator using the Takagi-Sugeno (T-S) fuzzy framework. Existing machine learning approaches to visual servoing involve system identification of image and kinematic Jacobians. In contrast, the proposed approach actuates a control signal primarily as a function of the error and derivative of the error in the desired visual feature space. This approach leads to a significant reduction in the computational burden as compared to model-based approaches, as well as existing learning approaches to model inverse kinematics. The simplicity of the controller structure will make it attractive in industrial implementations where PD/PID type schemes are in common use. While the initial values of PD gain are learned with the help of model-based controller, an online adaptation scheme has been proposed that is capable of compensating for local uncertainties associated with the system and its environment. Rigorous experiments have been performed to show that visual servoing tasks such as reaching a static target and tracking of a moving target can be achieved using the proposed distributed PD controller. It is shown that the proposed adaptive scheme can dynamically tune the controller parameters during visual servoing, so as to improve its initial performance based on parameters obtained while mimicking the model-based controller. The proposed control scheme is applied and assessed in real-time experiments using an uncalibrated eye-in-hand robotic system with a 7-DOF PowerCube robot manipulator.

Image-Based Visual Servoing of a 7-DOF Robot Manipulator Using an Adaptive Distributed Fuzzy PD Controller

This paper presents a position based visual tracking system of a redundant manipulator using a Kinect camera. Kinect camera provides 3-D information of a target object, therefore the control algorithm of the position-based visual servoing (PBVS) can be simplified, as there is no requirement to estimate a 3-D feature point position from the extracted image and the camera model. The Kalman filter is used to predict the target position and velocity. This control method is applied to a calibrated robotic system with eye-to-hand configuration. The stability analysis has been derived and real-time experiments have been carried out using a 7 DOF PowerCube manipulator from Amtec Robotic. The experimental results of both static and moving targets are presented to demonstrate and to verify the proposed position based visual tracking system performance.

A position based visual tracking system for a 7 DOF robot manipulator using a Kinect camera

This paper presents a distributed fuzzy proportional control system for a vision guided redundant robot manipulator. Firstly, the Takagi Sugeno (TS) fuzzy algorithm is used to model analytical Image Based Visual Servoing (IBVS) using shape moments by offline learning. This control method is applied to an uncalibrated robotic system with eye-in-hand visual feedback. The system is able to track a moving object through a variety of motions and maintain the object's image features in a desired position in the image plane without a priori knowledge of the robot kinematic, camera calibration and inverse Jacobian. The experimental results of both static and moving targets using the 7 DOF PowerCube manipulator from Amtec Robotic show and verify its performance in a realtime application.

Image Based Visual Servoing of a 7 DOF robot manipulator using a distributed fuzzy proportional controller

This paper presents a Model Based Visual Servoing (MBVS) control strategy for a differential drive mobile robot navigation using single camera attached on the robot platform. Four points image features are used to compute the angular velocities of the right wheel and the left wheel. The model based visual servoing scheme was simulated using visual servoing platform (ViSP) libraries. The simulation results show the effectiveness of the designed control law. The control law performances are measured both in the image space and in the control input space. The effectiveness of the MBVS has also been verified in real-time experiments using Beaglebone Black as the main hardware controller.

A real-time Model Based Visual Servoing application for a differential drive mobile robot using Beaglebone Black embedded system

Electricity as the primary source of power for gadgets, computers, machinery, and other electronic goods is used at the home-to-industrial level. Most of the world’s Electric Grids operate on fossil fuels which are getting thinner and expected to run out soon, the best solution that is possible to do is Smart Grid. The Smart Grid realized extensive use of information sensing, transmission and processing. Today, IoT technology become a essential role in construction of power grid. This paper reviews the Integration of IoT in Smart Grids. IoT-integrated Smart Grid systems are already deployed, but the full capabilities of instant knowledge and sustainable large-scale data processing have not been exploited optimally. Two of the architectures of IoT integrated Smart Grids is four-layered architecture and Web Enabled Smart Grid. To process and manage the Big Data in Smart Grid, Supervisory Control and Data Acquisition (SCADA) system can be used and furthermore enhanced with cloud computing. The smart grid is the future of electric grid that solves the problems of information flows, rising energy consumption, energy waste, reliability and security in the conventional power grid.

Indrazno Siradjuddin

Papers

Image-Based Visual Servoing of a 7-DOF Robot Manipulator Using an Adaptive Distributed Fuzzy PD Controller

A position based visual tracking system for a 7 DOF robot manipulator using a Kinect camera

Image Based Visual Servoing of a 7 DOF robot manipulator using a distributed fuzzy proportional controller

A real-time Model Based Visual Servoing application for a differential drive mobile robot using Beaglebone Black embedded system

Internet of Things Integration in Smart Grid