Design, fabrication and control of a magnetic capsule-robot for the human esophagus

Much research on the development of a robotic capsule and micro robot for the diagnosis of gastrointestinal diseases has been carried out. The powering of these micro systems is becoming very challenging as the implementation of such systems is limited due to the existence of on-board power supplies. This paper presents a micro robotic system based on magnetic principles. The goal is to build a system in which a capsule-robot can be manipulated wirelessly inside an enclosed environment such as human??s body. A prototype of capsule-robot is built and tested, that can be remotely operated with three DOF in an enclosed environment by transferring magnetic energy and electromagnetic waves. A magnetic drive unit generates magnetic energy for the manipulation. Experimental results show the capsule-robot is manipulated and moved through a desired trajectory in a viscous fluid. The capsule-robot can be potentially used for endoscopy and colonoscopy.     

基于立体视觉的串联机器人跟踪检测系统

建立一种六自由度串联机器人视觉跟踪检测系统框架,包括图像采集、摄像机标定、机器臂跟踪检测、机器臂位姿建模与计算等。提出利用CamShift算法对机器人进行在线粗跟踪,搜寻和画定出机器臂操作器在当前窗口的区域位置。对跟踪到的机器臂按照SURF算法进行特征提取与立体匹配。该方法被用于对串联机器人位姿检测进行实验。实验结果表明,2种算法的结合适用于六自由度串联机器人在空间复杂运动的跟踪检测。     

Intelligent control based on wavelet decomposition and neural network for predicting of human trajectories with a novel vision-based robotic

In this paper, an intelligent novel vision-based robotic tracking model is developed to predict the performance of human trajectories with a novel vision-based robotic tracking system. The developed model is based on wavelet packet decomposition, entropy and neural network. We represent an implementation of a novel vision-based robotic tracking system based on wavelet decomposition and artificial neural (WD-ANN) which can track desired human trajectory pattern in real environments. The input–output data set of the novel vision-based robotic tracking system were first stored and than these data sets were used to predict the robotic tracking based on WD-ANN. In simulations, performance measures were obtained to compare the predicted and human–robot trajectories like actual values for model validation. In statistical analysis, the RMS value is 0.0729 and the R² value is 99.76% for the WD-ANN model. This study shows that the values predicted with the WD-ANN can be used to predict human trajectory by vision-based robotic tracking system quite accurately. All simulations have shown that the proposed method is more effective and controls the systems quite successful.     

Cooperative passers-by tracking with a mobile robot and external cameras

In this paper, we present a cooperative passers-by tracking system between fixed view wall mounted cameras and a mobile robot. The proposed system fuses visual detections from wall mounted cameras and detections from a mobile robot–in a centralized manner–employing a “tracking-by-detection” approach within a Particle Filtering strategy. This tracking information is then used to endow the robot with passers-by avoidance ability to facilitate its navigation in crowds during the execution of a person following mission. The multi-person tracker’s ability to track passers-by near the robot distinctively is demonstrated through qualitative and quantitative off-line experiments. Finally, the designed perceptual modalities are deployed on our robotic platform, controlling its actuators via visual servoing techniques and free space diagrams in the vicinity of the robot, to illustrate the robot’s ability to follow a given target person in human crowded areas.     

Output feedback tracking control of robot manipulators with model uncertainty via adaptive fuzzy logic

Many robot controllers require not only joint position measurements but also joint velocity measurements; however, most robotic systems are only equipped with joint position measurement devices. In this paper, a new output feedback tracking control approach is developed for the robot manipulators with model uncertainty. The approach suggested herein does not require velocity measurements and employs the adaptive fuzzy logic. The adaptive fuzzy logic allows us to approximate uncertain and nonlinear robot dynamics. Only one fuzzy system is used to implement the observer-controller structure of the output feedback robot system. It is shown in a rigorous manner that all the signals in a closed loop composed of a robot, an observer, and a controller are uniformly ultimately bounded. Finally, computer simulation results on three-link robot manipulators are presented to show the results which indicate good position tracking performance and robustness against payload uncertainty and external disturbances.     

Sparse-then-dense alignment-based 3D map reconstruction method for endoscopic capsule robots

Despite significant progress achieved in the last decade to convert passive capsule endoscopes to actively controllable robots, robotic capsule endoscopy still has some challenges. In particular, a fully dense three-dimensional (3D) map reconstruction of the explored organ remains an unsolved problem. Such a dense map would help doctors detect the locations and sizes of the diseased areas more reliably, resulting in more accurate diagnoses. In this study, we propose a comprehensive medical 3D reconstruction method for endoscopic capsule robots, which is built in a modular fashion including preprocessing, keyframe selection, sparse-then-dense alignment-based pose estimation, bundle fusion, and shading-based 3D reconstruction. A detailed quantitative analysis is performed using a non-rigid esophagus gastroduodenoscopy simulator, four different endoscopic cameras, a magnetically activated soft capsule robot, a sub-millimeter precise optical motion tracker, and a fine-scale 3D optical scanner, whereas qualitative ex-vivo experiments are performed on a porcine pig stomach. To the best of our knowledge, this study is the first complete endoscopic 3D map reconstruction approach containing all of the necessary functionalities for a therapeutically relevant 3D map reconstruction.     

Design of an artificial brain for robots

This paper proposes a new information processing and control system, which is called artificial brain (ABrain), for robotics using neurocomputers and a Von-Neumann-type microcomputer, and interfaces operating these computers. We introduce three robotic systems with ABrains developed recently in our laboratory. One is an ABrain to recognize the objects in a robotic system for recognition and tracking. The others are an ABrain for controlling the duty ratio in the robot system for recognition and tracking, and an ABrain for controlling the steering angle in an intelligent mobile vehicle. Based on our results, we present a realization of a general type of ABrain to recognize a moving pattern and track it simultaneously. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998     

Haptic feedback and control of a flexible surgical endoscopic robot

A flexible endoscope could reach the potential surgical site via a single small incision on the patient or even through natural orifices, making it a very promising platform for surgical procedures. However, endoscopic surgery has strict spatial constraints on both tool-channel size and surgical site volume. It is therefore very challenging to deploy and control dexterous robotic instruments to conduct surgical procedures endoscopically. Pioneering endoscopic surgical robots have already been introduced, but the performance is limited by the flexible neck of the robot that passes through the endoscope tool channel. In this article we present a series of new developments to improve the performance of the robot: a force transmission model to address flexibility, elongation study for precise position control, and tissue property modeling for haptic feedback. Validation experiment results are presented for each sector. An integrated control architecture of the robot system is given in the end.     

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.     

Information based indoor environment robotic exploration and modeling using 2-D images and graphs

As the autonomy of personal service robotic systems increases so has their need to interact with their environment. The most basic interaction a robotic agent may have with its environment is to sense and navigate through it. For many applications it is not usually practical to provide robots in advance with valid geometric models of their environment. The robot will need to create these models by moving around and sensing the environment, while minimizing the complexity of the required sensing hardware. Here, an information-based iterative algorithm is proposed to plan the robot's visual exploration strategy, enabling it to most efficiently build a graph model of its environment. The algorithm is based on determining the information present in sub-regions of a 2-D panoramic image of the environment from the robot's current location using a single camera fixed on the mobile robot. Using a metric based on Shannon's information theory, the algorithm determines potential locations of nodes from which to further image the environment. Using a feature tracking process, the algorithm helps navigate the robot to each new node, where the imaging process is repeated. A Mellin transform and tracking process is used to guide the robot back to a previous node. This imaging, evaluation, branching and retracing its steps continues until the robot has mapped the environment to a pre-specified level of detail. The set of nodes and the images taken at each node are combined into a graph to model the environment. By tracing its path from node to node, a service robot can navigate around its environment. This method is particularly well suited for flat-floored environments. Experimental results show the effectiveness of this algorithm.     

Vision-based robotics: a challenge to real world Artificial Intelligence

Vision is a key function not only for robotics but also for AI more generally. Today real-time visual processing is becoming possible; this means that vision based behavior can become more dynamic, opening fertile areas for applications. One aspect of this is real-time visual tracking. We have built a real-time tracking system and incorporated it in an integrated robot programming environment. Using this, we have performed experiments in vision based robot behavior and human-robot interactions. In particular, we have developed a robotic system capable of 'learning by seeing'. In general, it is important for the AI community not to lose sight of the problems and progress of robotics. After all, an AI system which acts in real-time in the real-world is no less (and no more) than an intelligent robot.     

Trajectory prediction and tracking using a multi-behaviour social particle filter

3D motion tracking is a challenging task when both the tracked object and the observer are moving. In this paper, we present a multi-behavioural social force-based particle filter to track a group of moving humans from a moving robot using a limited field-of-view monocular camera. The application is a robotic guide and while moving, the robot often loses visibility of one or more people in the group, who must still be tracked. As an example, due to limited space, when the robot takes a sharp turn to avoid an obstacle or circumvent a corner, the visibility of the people at the rear is lost for some time. Therefore, several human social behavioural aspects have been implemented to predict the human’s motion in a group. The model accounts for attraction and repulsion between the people of the group and those with the robot, to maintain a comfortable social distance with each other at equilibrium. Additionally, when any person leaves the group then the track is deleted and after joining the track is automatically re-initialized. In the literature, the time of invisibility is a criterion to detect a person who has left the system, which however cannot be used here since the invisibility may be due to a limited field of view or the robot making a sharp turn to avoid an obstacle or circumventing a corner. Social heuristics are used to accurately detect people leaving the robotic system. The tracked trajectory is compared with ground truth and our system gives a very less error when compared with several baseline approaches. False positives are reduced, and the accuracy also increased with our proposed model as compared to other baseline methods. This method has been tested on several scenarios to ensure its validity.

     

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.     

A New Mechanism for Mesoscale Legged Locomotion in Compliant Tubular Environments

We present design and experimental performance results for a novel mechanism for robotic legged locomotion at the mesoscale (from hundreds of microns to tens of centimeters). The new mechanism is compact and strikes a balance between conflicting design objectives, exhibiting high foot forces and low power consumption. It enables a small robot to traverse a compliant, slippery, tubular environment, even while climbing against gravity. This mechanism is useful for many mesoscale locomotion tasks, including endoscopic capsule robot locomotion in the gastrointestinal tract. It has enabled fabrication of the first legged endoscopic capsule robot whose mechanical components match the dimensions of commercial pill cameras (11 mm diameter by 25 mm long). A novel slot-follower mechanism driven via lead screw enables the mechanical components of the capsule robot to be as small while simultaneously generating 0.63 N average propulsive force at each leg tip. In this paper, we describe kinematic and static analyses of the lead screw and slot-follower mechanisms, optimization of design parameters, and experimental design and tuning of a gait suitable for locomotion. A series of ex vivo experiments demonstrate capsule performance and ability to traverse the intestine in a manner suitable for inspection of the colon in a time period equivalent to standard colonoscopy.     

Robust tracking enhancement of robot systems including motordynamics: a fuzzy-based dynamic game approach

A robust tracking control design of robot systems including motor dynamics with parameter perturbation and external disturbance is proposed in this study via adaptive fuzzy cancellation technique. A minimax controller equipped with a fuzzy-based scheme is used to enhance the tracking performance in spite of system uncertainties and external disturbance. The design procedure is divided into three steps. At first, a linear nominal robotic control design is obtained via model reference tracking with desired eigenvalue assignment. Next, a fuzzy logic system is constructed and then tuned to eliminate the nonlinear uncertainties as possibly as it can to enhance the tracking robustness. Finally, a minimax control scheme is specified to optimally attenuate the worst-case effect of both the residue due to fuzzy cancellation and external disturbance to achieve a minimax tracking performance. In addition, an adaptive fuzzy-based dynamic game theory is introduced to solve the minimax tracking problem. The proposed method is appropriate for the robust tracking design of robotic systems with large parameter perturbation and external disturbance. A simulation example of a two-link robotic manipulator driven by DC motors is also given to demonstrate the effectiveness of proposed design method's tracking performance     

Development of a physically behaved robot work cell in virtual reality for task teaching

Robot task teaching on a real work cell is expensive and sometimes risky. This cost and risk can be avoided by using virtual reality technology. Using the simulated environment in virtual reality (VR), the operator can practise, explore and preview the operations for possible problems that might occur during implementation. It is therefore of practical importance to build the virtual robot work cell in VR that can facilitate the study of the performance of robotic tasks such as robotic assembly. This paper describes our work in incorporating physical behaviours of virtual objects into VR for robot task teaching. To facilitate the task teaching, we developed visual and audio cues which help visualise the dynamic interactions between virtual objects. Dynamic sensing capability is incorporated in the simulated environment. A simplified force sensor is modelled and simulated. The physical behaviours of the virtual objects are simulated using physics-based approach. A virtual robot work cell is built incorporating the developed features and an example for the task teaching is given. The implementation includes view tracking using virtual camera, visual and audio rendering, and the user interface developed in the VR. The current implementation was carried out on a PC-based VR platform, with the programs developed using Watcom C++.     

Locomotion Control of a Hydraulically Actuated Hexapod Robot by Robust Adaptive Fuzzy Control with Self-Tuned Adaptation Gain and Dead Zone Fuzzy Pre-compensation

Hydraulically actuated robotic mechanisms are becoming popular for field robotic applications for their compact design and large output power. However, they exhibit nonlinearity, parameter variation and flattery delay in the response. This flattery delay, which often causes poor trajectory tracking performance of the robot, is possibly caused by the dead zone of the proportional electromagnetic control valves and the delay associated with oil flow. In this investigation, we have proposed a trajectory tracking control system for hydraulically actuated robotic mechanism that diminishes the flattery delay in the output response. The proposed controller consists of a robust adaptive fuzzy controller with self-tuned adaptation gain in the feedback loop to cope with the parameter variation and disturbances and a one-step-ahead fuzzy controller in the feed-forward loop for hydraulic dead zone pre-compensation. The adaptation law of the feedback controller has been designed by Lyapunov synthesis method and its adaptation rate is varied by fuzzy self-tuning. The variable adaptation rate helps to improve the tracking performance without sacrificing the stability. The proposed control technique has been applied for locomotion control of a hydraulically actuated hexapod robot under independent joint control framework. For tracking performance of the proposed controller has also been compared with classical PID controller, LQG state feedback controller and static fuzzy controller. The experimental results exhibit a very accurate foot trajectory tracking with very small tracking error with the proposed controller.     

Robust fuzzy tracking control for robotic manipulators

In this paper, a stable adaptive fuzzy-based tracking control is developed for robot systems with parameter uncertainties and external disturbance. First, a fuzzy logic system is introduced to approximate the unknown robotic dynamics by using adaptive algorithm. Next, the effect of system uncertainties and external disturbance is removed by employing an integral sliding mode control algorithm. Consequently, a hybrid fuzzy adaptive robust controller is developed such that the resulting closed-loop robot system is stable and the trajectory tracking performance is guaranteed. The proposed controller is appropriate for the robust tracking of robotic systems with system uncertainties. The validity of the control scheme is shown by computer simulation of a two-link robotic manipulator.     

Robust stability analysis of robot control systems

Some robust stability measures for state-space models are discussed. These measures are used for assessing the robustness of a robotic system controlled by a form of the PID control algorithm. The highly nonlinear and strongly coupled robot model is replaced by a nominal linear model involving modelling uncertainty. The amount of uncertainty under which the robotic system can maintain its stability and tracking performance is determined using the above measures. The parametric sensitivity of the robotic system is also briefly considered.