In this article, we improve the efficiency of a turbine blade inspection robotic workcell. The workcell consists of a stationary camera and a 6-axis serial robot that is holding a blade and presenting different zones of the blade to the camera for inspection. The problem at hand consists of a 6-DOF (degree of freedom) continuous optimization of the camera placement and a discrete combinatorial optimization of the sequence of inspection poses (images). For each image, all robot configurations (up to eight) are taken into consideration. A novel combined approach is introduced, called blind dynamic particle swarm optimization (BD-PSO), to simultaneously obtain the optimal design for both domains. The objective is to minimize the cycle time of the inspection process, while avoiding any collisions. Even though PSO is vastly used in engineering problems, the novelty of our combinatorial optimization method is in its ability to be used efficiently in traveling salesman problems where the distances between the cities are unknown and subject to change. This highly unpredictable environment is the case of the inspection cell where the cycle time between two images will change for different camera placements.

The various vision-based tactile sensors have been developed for robotic perception in recent years. In this paper, the novel soft robotic finger embedded with the visual sensor is proposed for perception. It consists of a colored soft inner chamber, an outer structure, and an endoscope camera. The bending perception algorithm based on image preprocessing and deep learning is proposed. The boundary of color regions and the position of marker dots are extracted from the inner chamber image and label image, respectively. Then the convolutional neural network with multi-task learning is trained to obtain bending states of the finger. Finally, the experiments are implemented to verify the effectiveness of the proposed method.

Double inverted pendulum model, stationary or on a cart, is computationally the simplest out of the range of reasonable models used for anthropomorphic robots motion synthesis. However, it is still not sufficient for describing more complex situations. The novel concept of variable double inverted pendulum (VDIP) for static postures and VDIP on cart (VDIPC) for dynamic cases is proposed. It provides a simplified but a sufficiently accurate tool for planning the human-like static and dynamic robot postures. Its variable parameters enable the description of both human static postures and motion dynamics. The variable length of the lower link is essential for the representation of postures attained by bending legs. The studies of a set of static and dynamic postures were used for deducing and verifying the locations of lower and upper joint of a double pendulum and the point masses. To justify the concept, human body and pendulum behaviors are compared taking into account a typical model of the human body. Static analysis was conducted by considering static human postures. Dynamic conditions were analyzed using the data acquired from human motion and thus the VDIPC definition was established. The zero moment point trajectories of the human and of VDIPC were compared, validating the correctness of VDIPC in dynamic situations. The formal description of VDIPC is provided together with the torques equilibrium condition needed for evaluating the dynamic postural stability, with the VDPIC representing the robot configuration. The VDPIC state equations are formulated in a form required by the predictive control method. The paper contributes to the motion synthesis methods of anthropomorphic robots taking into account postural control.

In this paper, a new approach is presented for perfect torque compensation of the robot in point-to-point motions. The proposed method is formulated as an open-loop optimal control problem. The problem is defined as optimal trajectory planning with adjustable design parameters to compensate applied torques of a planar 5R parallel robot for a given task, perfectly. To illustrate the effectiveness of the approach, the obtained optimal path is used as the reference command in the experiment. The experimental outputs show that the performance index has been reduced by over 80% compared to the typical design of the robot.

In this study, a control algorithm is proposed and evaluated for a special type of kinematically redundant manipulator. This manipulator is comprised of two mechanisms, macro and micro mechanisms, with distinct acceleration and work space characteristics. A control algorithm is devised to minimize the task completion duration and the overall actuator effort with respect to the conventional manipulator. A general framework multi-priority controller for macro-micro manipulators is introduced by utilizing virtual dynamics, which is introduced in null-space projection to achieve secondary tasks. The proposed controller is evaluated on a simulation model based on a previously constructed macro-micro manipulator for planar laser cutting. Task completion duration and the total actuator effort are investigated and the results are compared.

The purpose of this paper is to design a stabilizing controller for a car with n connected trailers. The proposed control algorithm is constructed on the Lyapunov theory. In this paper, the purpose of navigating the system toward the desired point considering the slip phenomenon as a main source of uncertainty is analyzed. First mathematical models are presented. Then, a stabilizing control approach based on the Lyapunov theory is presented. Subsequently, an uncertainty estimator is taken into account to overcome the wheel slip effects. Obtained results show the convergence properties of the proposed control algorithm against the slip phenomenon.

This paper reports on laboratory and field experimental results for controlled robotic manipulators operating on moving platforms with unmodeled dynamics. The aim is to validate theoretical predictions for the dependence on control parameters of an adaptive control strategy. In addition, the results provide insight into different discretizations of the continuous-time formulation, suggesting the most suitable discretization scheme for hardware implementation. The second set of experimental results, obtained from an implementation of the control framework for synchronization and consensus in networks of robotic manipulators, similarly validate theoretical predictions on the sensitivity to network communication delays.

Forward kinematics is essential in robot control. Its resolution remains a challenge for continuum manipulators because of their inherent flexibility. Learning-based approaches allow obtaining accurate models. However, they suffer from the explosion of the learning database that wears down the manipulator during data collection. This paper proposes an approach that combines the model and learning-based approaches. The learning database is derived from analytical equations to prevent the robot from operating for long periods. The database obtained is handled using Deep Neural Networks (DNNs). The Compact Bionic Handling robot serves as an experimental platform. The comparison with existing approaches gives satisfaction.

The usage of depth data from time-of-flight or any equivalent devices overcomes visual challenges presented under low illumination. For integrating such data from multiple sources, the paper proposes a novel tool for the calibration of sensors. The proposed tool consists of retro-reflective stripped spheres. To correctly estimate these spheres, the paper investigates the performance of spherical estimations from either infrared or depth data. The relationship between sensors is determined by calculating the poses between the calibration tool and each sensor. The paper evaluates and compares the proposed approach against other state-of-the-art approaches in terms of shape reconstruction and spatial consistency.

This paper presents a robust adaptive impedance controller for electrically driven robots using polynomials of degree N as a universal approximator. According to the universal approximation theorem, polynomials of degree N can approximate uncertainties including un-modeled dynamics and external disturbances. This fact is completely discussed and proved in this paper. The polynomial coefficients are estimated based on the adaptive law calculated in the stability analysis. A performance evaluation has been carried out to verify satisfactory performance of the controller. Simulation results on a two degree of freedom manipulator have been presented to guarantee its successful implementation.

The dynamic manipulability of a manipulator refers to the capacity to generate accelerations given the joint torques, which is an important indicator for motion planning and control. In this paper, the dynamic manipulability analysis is extended to the multi-arm space robot, and further to the closed-loop system composed of the space robot and the captured target. According to the dynamic equations, the relation between the joint torques and the end-effector accelerations in the open-loop space robot and that between the joint torques and the target accelerations in the closed-loop system are derived. On this basis, the dynamic manipulability factor and dynamic manipulability ellipsoid are proposed as two tools for the dynamic manipulability measure, where the effects of the bias acceleration are considered. The influences of dynamic parameters, link lengths, joint variables, and velocities on the dynamic manipulability measure are mainly studied.

This paper investigates the dynamics of an underwater vehicle-manipulator system (UVMS) consisting of a two-link flexible-joint manipulator affixed to an autonomous underwater vehicle. The quasi-Lagrange formulation is utilized in deriving a realistic mathematical model of the UVMS considering joints’ friction, hysteretic coupling between the joints and links, and the nonlinear hydrodynamic forces acting on the system, such as added mass, viscous damping, buoyancy, drag, and vortex-induced forces. Numerical simulations are performed to demonstrate the effects of hydrodynamic forces and system coupling between the vehicle and the manipulator and the joints and the links on the precise positioning of the end effector.

This paper presents a Chebyshev Pseudospectral (PS) method for solving the motion planning problem of nonholonomic mobile robots with kinematic and dynamic constraints. The state and control variables are expanded in the Chebyshev polynomial of order N, and Chebyshev–Gauss–Lobatto (CGL) nodes are provided for approximating the system dynamics, boundary conditions, and performance index. For the lack of enough nodes nearby the obstacles, the interpolation of trajectory may violate the obstacles and the multiple-interval strategy is proposed to deal with the violation. Numerical examples demonstrate that multiple-interval strategy yields more accurate results than the single-interval Chebyshev PS method.

In this paper, an online self-gain tuning method of a PD computed torque control (CTC) is used for a 3UPS-PS parallel robot. The CTC is applied to the 3UPS-PS parallel robot based on the robot dynamic model which is established via a virtual work principle. The control system of the robot comprises a nonlinear feed-forward loop and a PD control feedback loop. To implement real-time online self-gain tuning, an adjustment method based on the genetic algorithm (GA) is proposed. Compared with the traditional CTC, the simulation results indicate that the control algorithm proposed in this study can not only enhance the anti-interference ability of the system but also improve the trajectory tracking speed and the accuracy of the 3UPS-PS parallel robot.

In typical calibration methods (kinematic or non-kinematic) for serial industrial robot, though measurement instruments with high resolutions are adopted, measurement configurations are optimized, and redundant parameters are eliminated from identification model, calibration accuracy is still limited under measurement noise. This might be because huge gaps still exist among the singular values of typical identification Jacobians, thereby causing the identification models ill conditioned. This paper addresses such problem by using new identification models established in two steps. First, the typical models are divided into the submodels with truncated singular values. In this way, the unknown parameters corresponding to the abnormal singular values are removed, thereby reducing the condition numbers of the new submodels. However, these models might still be ill conditioned. Therefore, the second step is to further centralize the singular values of each submodel by using a matrix balance method. Afterward, all submodels are well conditioned and obtain much higher observability indices compared with those of typical models. Simulation results indicate that significant improvements in the stability of identification results and the identifiability of unknown parameters are acquired by using the new identification submodels. Experimental results indicate that the proposed calibration method increases the identification accuracy without incurring additional hardware setup costs to the typical calibration method.

Prandtl–Ishlinskii (PI) model has an excellent compromise to characterize an asymmetric saturated hysteresis behavior of shape-memory-alloy (SMA)-driven systems, but it cannot consider thermomechanical relations between components of SMA-driven systems. On the other hand, constitutive models are composed of these relations, but their precision needs to be improved. In this paper, PI model is proposed to boost constitutive models in two cases. In the first case, PI model is used to characterize martensite volume fraction (MVF) called hybrid model. In the second case, the model is applied as a regulator in the output of a constitutive model called PI-based output (PIO) regulator. Due to simplicity and ability of Liang–Rogers (LR) model in transformation phases, it is considered as an MVF in the original constitutive model. The performance of both proposed models is compared with the original LR-based constitutive model. Unknown parameters of all three models are identified using genetic algorithm in MATLAB Toolbox. The performance of the three models is investigated at three different frequencies of \[\frac{{2\pi }}{8}\], \[\frac{{2\pi }}{{15}}\], and \[\frac{{2\pi }}{{30}}\] Hz because the excitation frequency changes the hysteresis behavior. Results show that the proposed hybrid model keeps the precision of the original constitutive model at different frequencies. In addition, the proposed PIO model shows the best performance to predict hysteresis behavior at different frequencies.

This study proposes the use of a series elastic actuator (SEA) in a Segway to improve the stability of the tires during linear and curved driving, thus improving the comfort of the driver and ensuring driving stability. Recently, Segway has been developed continuously for intelligent mobile vehicles and the performance of Segway is being enhanced. Therefore, safety factors during the Segway driving have been considered seriously. In most of the developments and studies on Segway, the optimization and improvement of the controller component have been tackled and there are few studies on the safety devices and the stability of driving. The impact and vibration generated from the ground due to uneven road surfaces considerably influence the force exerted on the tire, which further affects driving stability. This research focuses on the control of the SEA based on the tire model to improve the driving stability of Segway. The performance of the proposed algorithm to improve the stability of the driver has been verified by straight and curved paths driving experiments with the tire model.

Cable-driven parallel manipulators (CDPMs) offer advantages over traditional parallel manipulators. Though their ability to accelerate is higher than the traditional motion platforms, the capabilities are often not used optimally. The issues of cable slackening (especially at higher accelerations) and the emergence of singularity poses have traditional limitations. This paper analyzes and generates manipulator configurations that reduce the effect of these two essential hindrances of deploying CDPMs. A methodology, inspired by rigid body dynamics of multiple contact problems, used to optimize the positions of attachment points, is shown to be effective.

A versatile architecture is presented to implement autonomous vehicles. The focus idea consists of a set of standalone modules, called wireless robotic components wireless robotic components (WRCs). Each component performs a particular function by means of a radio modem interface, a processing unit, and a sensor/actuator. The components interact through a coordinator that redirects asynchronous requests to the appropriate WRCs, configuring a built-in network. The WRC architecture has been tested in marine and terrestrial platforms to perform tasks of waypoint and wall following. Results show that the tested system complies with adaptability and portability that allow conforming a variety of autonomous vehicles.

In this article, we propose a nonlinear Proportional+Derivative (PD) tracking controller with adaptive Fourier series compensation. The proposed controller uses a regressor-free adaptive scheme that relies on a trigonometric polynomial with varying coefficients to solve the control problem. Asymptotic convergence of the position and velocity errors is proven via a formal stability analysis based on Lyapunov and LaSalle theory for discontinuous systems. The proposed controller is validated on a 2-degrees of freedom robot manipulator. The experimental results validate the theoretically obtained results and reflect the effect of certain parameters in the transient behavior of the error dynamics. Certain robustness properties are also observed.