Tracking Performance Of Robot Research Articles

Accurate Robotic Posterior Capsule Polishing With Tissue Stabilization

Within cataract surgery, posterior capsule (PC) polishing is a specific procedure that has demonstrated value toward improving surgical outcomes, yet is not commonly performed by surgeons due to the risk of doing so. In this work, a robotic system is used to perform the polishing procedure through three main contributions: external stabilization of the eye using a dedicated docking device, regulating intraocular pressure through feedback control, and improving the robot-tracking performance to result in an accurate polishing procedure. To validate the methodology, polishing was performed on four ex vivo pig eyes by mimicking the residual lens on the PC with an evenly distributed layer of glue. Fast and accurate polishing results were achieved and complete glue removal was accomplished in all experiments without any PC rupture or other surgical complications.

Universal nonlinear disturbance observer for robotic manipulators

Dynamic uncertainties and unknown disturbances will degrade the tracking performance of robots. When using a disturber observer-based controller, the key to effectively compensate for these uncertainties is to measure or estimate as accurately as possible any disturbance. To relax restrictions on the design of the current nonlinear disturbance observer for the robot, a universal nonlinear disturbance observer is proposed for higher estimation performance. The stability of the proposed universal nonlinear disturbance observer is theoretically analyzed and the boundaries of estimation error are proven according to the vector differential equation. Finally, the proposed universal nonlinear disturbance observer is evaluated via simulation by comparison to the nonlinear disturbance observer. The result shows a faster estimation speed and a higher estimation accuracy of the universal nonlinear disturbance observer.

Bilateral Teleoperation With Adaptive Impedance Control for Contact Tasks

This letter presents an adaptive impedance control architecture for robotic teleoperation of contact tasks featuring continuous interaction with the environment. We use Learning from Demonstration (LfD) as a framework to learn variable stiffness control policies. Then, the learnt state-varying stiffness is used to command the remote manipulator, so as to adapt its interaction with the environment based on the sensed forces. Our system only relies on the on-board torque sensors of a commercial robotic manipulator and it does not require any additional hardware or user input for the estimation of the required stiffness. We also provide a passivity analysis of our system, where the concept of energy tanks is used to guarantee a stable behavior. Finally, the system is evaluated in a representative teleoperated cutting application. Results show that the proposed variable-stiffness approach outperforms two standard constant-stiffness approaches in terms of safety and robot tracking performance.

Improving Tracking through Human-Robot Sensory Augmentation

This letter introduces a sensory augmentation technique enabling a contact robot to understand its human user's control in real-time and integrate their reference trajectory information into its own sensory feedback to improve the tracking performance. The human's control is formulated as a feedback controller with unknown control gains and desired trajectory. An unscented Kalman filter is used to estimate first the control gains and then the desired trajectory. The estimated human's desired trajectory is used as augmented sensory information about the system and combined with the robot's measurement to estimate a reference trajectory. Simulations and an implementation on a robotic interface demonstrate that the reactive control can robustly identify the human user's control, and that the sensory augmentation improves the robot's tracking performance.

Path Tracking of a Cable-Driven Snake Robot With a Two-Level Motion Planning Method

Due to high degree of freedoms (DOFs), motion planning of the cable-driven snake robot is challenging and usually tackled by the iterative Jacobian-based method. This iterative method adopts discontinuous velocity and tolerance to determine intermediate poses, which deteriorate the tracking performance of robots. In this paper, a two-level motion planning method is proposed to schedule the motion of cable-driven snake robot owning a serial–parallel hybrid structure. First, kinematics model of the robot among actuators, joints, and end effectors are introduced. Second, in the upper level, a jerk-bounded algorithm is employed to plan the position, velocity, and acceleration profiles of the end effector for a given path, whereas in the lower level, the joint angles and cable length changes are calculated by the inverse kinematics of the robot according to the position and velocity profiles of the end effector from the upper level. Therefore, the iterative process is eliminated by the presented method, which enhances the tracking performance and motion smoothness of the robot. Finally, simulations and experiments on a 24 DOFs snake robot prototype demonstrate the effectiveness of the presented method. According to the experimental results, the two-level method improves the repeatability and accuracy of the prototype by about 60% and 6%, respectively, compared with the iterative method.

Motion prediction and delay compensation for improved teleoperation of an exoskeletal robot

Delay is a major challenge in teleoperation. To teleoperate a robot in real-time with specific human motion such as walking, prediction and compensation algorithms are needed to minimize the tracking delay. Human walking possesses the inherent features of synergy and quasi-periodicity with nonlinearity. By analyzing these features, three prediction methods were designed. A delay-coordinate phase space reconstruction method is based on the delay embedding theorem and uses the historical data. The echo prediction method uses data from the coupling joints of the other body side and takes the Euclidean distances in the delay-coordinate phase space reconstruction method for data search and match. The fused prediction method combines the delay-coordinate phase space reconstruction and echo prediction methods, uses the best matching historical data of the same joint and the corresponding joint of the other limb. These methods were implemented for teleoperation of an exoskeletal robot with inertial measurement unit measurements of human walking. The predicted trajectories were used to compensate the teleoperation delay and improve the robot tracking performance. Experimental results demonstrated that the fused method brought apparent improvement over other methods and can serve as a promising solution for time delay estimation, prediction, and compensation of human walking tracking and teleoperation.

Detecting, localizing, and tracking an unknown number of moving targets using a team of mobile robots

Target tracking is a fundamental problem in robotics research and has been the subject of detailed studies over the years. In this paper, we generate a data-driven target model from a real-world dataset of taxi motions. This model includes target motion, appearance, and disappearance from the search area. Using this target model, we introduce a new formulation of the mobile target tracking problem based on the mathematical concept of random finite sets. This formulation allows for tracking an unknown and dynamic number of mobile targets with a team of robots. We show how to employ the probability hypothesis density filter to simultaneously estimate the number of targets and their positions. Next, we present a greedy algorithm for assigning trajectories to the robots to allow them to actively track the targets. We prove that the greedy algorithm is a two-approximation for maximizing submodular tracking objective functions. We examine two such functions: the mutual information between the estimated target positions and future measurements from the robots and a new objective that maximizes the expected number of targets detected by the robot team. We provide extensive simulation evaluations to validate the performance of our data-driven motion model and to compare the behavior and tracking performance of robots using our objective functions.

Bionic tracking method by hand & eye-vergence visual servoing

Animals rotate their eyes to gaze at the target prey, enhancing the ability of measuring the distance to the target precisely for catching it. These animals, visual tracking includes the triangular eye-vergence control and their body’s motion control by visual servoing. The research aims to realize a bionic robot tracking performance, in which the body links moves together with eyes’ view orientation. This paper proposed a hand & eye-vergence dual control system which included two feedback loops: an outer loop for conventional visual servoing to direct a manipulator toward a target object and an inner loop for active motion control of binocular cameras to change the viewpoint along with the moving object to give an accurate and broad observation. This research also foused on how to compensate a fictional motion of the target seen by camera images in an eye-in-hand system, where the camera was fixed on the end-effector and moved together with the hand motion. A robust motion-feedforward (MFF) recognition method is proposed to compensate the fictional motion of the target based on the manipulator’s joint velocity, then the real motion of the target seen by camera images is extracted, which can improve the feedback image sensing unit to make the whole servoing system dynamically stable. The effectiveness of the proposed hand & eye-vergence visual servoing method is shown by tracking experiments using a 6-DoF robot manipulator and a 3-DoF binocular vision system.

Comparison of Uncalibrated Model-Free Visual Servoing Methods for Small-Amplitude Movements: A Simulation Study

The paper compares the performance of several methods used for the estimation of an image Jacobian matrix in uncalibrated model-free visual servoing. This was achieved for an eye-in-hand configuration with small-amplitude movements with several sets of system parameters. The tested methods included the Broyden algorithm, Kalman and particle filters as well as the recently proposed population-based algorithm. The algorithms were tested in a simulation environment (Peter Corke's Robotic Toolbox for MATLAB) on a PUMA 560 robot. Several application scenarios were considered, including static point and dynamic trajectory tracking, with several characteristic shapes and three different speeds. Based on the obtained results, conclusions were drawn about the strengths and weaknesses of each method both for a particular setup and in general. Algorithm-switching was introduced and explored, since it might be expected to improve overall robot tracking performance with respect to the desired trajectory. Finally, possible future research directions are suggested.

ROBUST CONTROL OF HYBRID PARALLEL-SERIAL ROBOT

A 3-DOF hybrid parallel-serial robotic manipulator which is constructed by combining a parallel mechanism and a pantograph is introduced to increase high stiffness as well as workspace, based on the fact that parallel mechanism has higher stiffness but smaller workspace, Dynamics equation is established by applying Lagrange's method and its feature is discussed. Then a robust control scheme for the robot is developed. The proposed controller which is designed in Cartesian coordinates consists of two parts. The first part is the model-based computed torque controller which is used to stabilize the nominal robot system. The second part is a robust compensating controller which is designed to handle the uncertainty in the system. The theory analysis and simulation results show that using this controller, the robot tracking performance can be improved effectively.

Nonlinear friction compensation in mechatronic servo systems

Friction, especially its nonlinear component, may degrade the tracking performance of robots. Based on Kang’s method, a novel compensation method for nonlinear friction is presented in this paper, which modified Southward’s traditional compensation method for nonlinear friction. The stability of the systems which adopt the novel compensation method is proved with Layapunov’s stability theorem, and is enhanced further. Having estimated the nonlinear friction model using an identification method, the effect caused by its nonlinear component can be compensated, and enhanced tracking performance is verified under the SCARA robot experimental platform using Windows NT and VenturCom’s real-time extension module (RTX) environment.

Learning control of mobile robots using a multiprocessor system

A real-time multiprocessor system is proposed for the solution of the tracking problem of mobile robots operating in a real context with environmental disturbances and parameter uncertainties. The proposed control scheme utilizes multiple models of the robot for its identification in an adaptive and learning control framework. Radial Basis Function Networks (RBFNs) are considered for the multiple models in order to exploit the net non-linear approximation capabilities for modeling the kinematic behavior of the vehicle and for reducing unmodeled contributions to tracking errors. The training of the nets and the tests of the achieved control performance have been done in a real experimental setup. The proposed control architecture improves the robot tracking performance achieving fast and accurate control actions in presence of large and time-varying uncertainties in dynamical environments. The experimental results are satisfactory in terms of tracking errors and computational efforts.

Kinematic redundancy and sensor redundancy for enhancement of robot tracking performance

Imprecision of robot's internal sensors and its influence on trajectory tracking tasks along with high dynamic requirements are the main issues in this paper. The solution proposes redundancy introduction (in the shape of small mechanism at the top of the robot) for resolving both problems. The inverse kinematics of the redundant robot has been solved at the tactical level of control via the Distributed Positioning (DP) concept. The redundancy applied reduces the tracking error caused by dynamic requirements and partly by imprecision of internal sensors. However, additional solutions with external position sensor could be used in order to reduce sensor imprecision influence. All solutions are very conservative considering changes in basic (non-redundant) robot. Simulation results provide clear insight in the validity of proposed solutions.