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.

This paper proposes a set of novel indices for evaluating the kinematic performance of a 3-RRS (R and S denote revolute and spherical joint respectively, R denotes active joint.), parallel mechanism whose translational and rotational movements are strongly coupled. First, the indices are formulated using the decoupled overall Jacobian matrix, which is developed using coordinate transformation. Then, the influences of the homogeneous dimensionless parameters on these indices are investigated. In addition, the dimension synthesis of the 3-RRS parallel mechanism is carried out by minimizing the mean value of the kinematic performance indices and their standard deviation. The results demonstrate that the established approach facilitates good global kinematic performance of the parallel mechanism.

The optimum selection of a structure for a given application is a capital phase in typological synthesis of parallel robots. To help in this selection, this paper presents a performance evaluation of four translational parallel robots: Delta, 3-UPU, Romdhane-Affi-Fayet, and Tri-pyramid (TP). The problem is set as a multiobjective optimization using genetic algorithm methods, which uses kinematic criteria, that is, global dexterity and compactness, to ensure a prescribed workspace. The results are presented as Pareto fronts, which are used to compare the performances of the aforementioned structures. The obtained results show that the TP robot has the best kinematic performance, whereas the 3-UPU robot is the most compact for a given prescribed workspace.

Unmanned aerial vehicle (UAV) was introduced for nondeterministic traffic monitoring, and a real-time UAV cruise route planning approach was proposed for road segment surveillance. First, critical road segments are defined so as to identify the visiting and unvisited road segments. Then, a UAV cruise route optimization model is established. Next, a decomposition-based multi-objective evolutionary algorithm (DMEA) is proposed. Furthermore, a case study with two scenarios and algorithm sensitivity analysis are conducted. The analysis result shows that DMEA outperforms other two commonly used algorithms in terms of calculation time and solution quality. Finally, conclusions and recommendations on UAV-based traffic monitoring are presented.

Human–robot interaction (HRI) is becoming more important nowadays. In this paper, a low-cost communication system for HRI is designed and implemented on the Scout robot and a robotic face. A hidden Markov model-based voice command detection system is proposed and a non-native database has been collected by Persian speakers, which contains 10 desired English commands. The experimental results confirm that the proposed system is capable to recognize the voice commands, and properly performs the task or expresses the right answer. Comparing the system with a trained system on the Julius native database shows a better true detection (about 10%).

This paper proposes a fully distributed continuous region-reaching controller for multi-robot systems which can effectively eliminate the chattering issues and the negative effects caused by discontinuities. The adaptive control gain technique is employed to solve the distributed region-reaching control problem. By performing Lyapunov function-based stability analysis, it is shown that all the robots can move cohesively within the desired region under the proposed distributed control algorithm. In addition, collision avoidance and velocity matching within the moving region can be guaranteed under properly designed control gains. Simulation examples are given to verify the capabilities of the proposed control method.

In this paper we designed an optimal trajectory generation (OTG) method to generate easy and errorless continuous path motion with quick converging using Grey Wolf Optimization (GWO) method. The proposed OTG method finds the trajectory path with minimum tracking error, combined speed, joint increasing speed wrinkle, as well as joint lurching move to follow an error-free smooth continuous path.

A novel underwater vehicle configuration with an operating principle as the Sepiida animal is presented and developed in this paper. The mathematical equations describing the movements of the vehicle are obtained using the Newton–Euler approach. An analysis of the dynamic model is done for control purposes. A prototype and its embedded system are developed for validating analytically and experimentally the proposed mathematical representation. A real-time characterization of one mass is done to relate the pitch angle with the radio of displacement of the mass. In addition, first validation of the closed-loop system is done using a linear controller.

This paper presents a methodology to obtain the wrench capabilities of a kinematically redundant planar parallel manipulator using a wrench polytope approach. A methodology proposed by others for non-redundant and actuation-redundant manipulators is adapted to a kinematically redundant manipulator. Four wrench capabilities are examined: a pure force analysis, the maximum force for a prescribed moment, the maximum reachable force, and the maximum moment with a prescribed force. The proposed methodology, which finds the exact explicit solution for three of the four wrench capabilities, does not use optimization and is very efficient.

This paper presents an experimental study on new paradigms of haptic-based teleoperated navigation of underwater vehicles. Specifically, the work is focused on investigating the possibility of enhancing the user interaction by introducing haptic cues at the level of the user wrist providing a force feedback that reflects dynamic forces on the remotely operated underwater vehicle. Different typologies of haptic controllers are conceived and integrated with a real-time simulated model of an underwater robotic vehicle. An experimental test is designed to evaluate the usability of the system and to provide information on the global performance during the execution of simple tasks. Experiments are conducted with 7 candidates testing 12 different controllers. Among these, the most effective strategies have been identified and selected on the basis of minimization of errors on the vehicle trajectory and of the quality of the user’s interaction in terms of perceived comfort during operation. Overall, the results obtained with this study underline that haptic navigation control can have a positive influence on the performance of remotely controlled underwater vehicles.

The dynamical stability of the cable-driven lower-limb rehabilitation training robot (CLLRTR) is a crucial question. Based on the established dynamics model of CLLRTR, the solution to the wrench closure of the under-constrained system is presented. Secondly, the stability index of CLLRTR is proposed by the Krasovski method. Finally, in order to analyze the stability distribution of CLLRTR in the workspace, the stability evaluation index in the workspace is calculated using the eigenvalue decomposition method. The stability distribution laws of CLLRTR are further verified by the experimental study. The results provide references for studying trajectory planning and anti-pendulum control of CLLRTR.

Cable is the most important bearing structure of the cable-stayed bridges. Its safety has been of crucial public concern. Traditional manual cable inspection method has many defects such as low inspection efficiency, poor reliability and hazardous working environment. In this paper, a new wirelessly controlled cable-climbing robot enabling safe and convenient inspection of stay cables is proposed. The designed robot is composed of two modules, joined by four turnbuckles to form a closed structure that clasps the cable. The robot is controlled wirelessly by a ground-based station, and a DC power is supplied via an onboard lithium battery. The climbing principle and mechanical structure of this robot are introduced. The static model of the robot during obstacle negotiation is established. The relationships of the driving force and resistance with obstacle height to determine the obstacle-negotiation capability of the robot are obtained. The effects of cable diameter, cable inclination and preload force on obstacle climbing ability of the robot are also analyzed. The experiments verify that the robot could climb random inclined cables and overcome an obstacle of 2.42 mm in height with a mass of 5 kg payload.

A robotic manipulator’s classical mechanical capabilities are governed by the design parameters (mass, geometry, dimensions, etc.) of its kinematic pairs and its architecture (number of limbs, degrees of freedom, actuation ability, etc.). Using Clifford-Algebraic and network theoretic methods, this work presents a novel-theoretical framework which allows any two robot architectures and design parameters to be mathematically related to one another through combinations of discrete operators or ‘reassembling transformations’. Two theoretical case studies involving a 6R manipulator and Klann linkage are furnished in this work.

Aiming at planning the welding path of complex ship components, a welding path planning optimization model was constructed with the shortest welding path and using the target and the welding process and welding starting and ending points as constraints. Based on the model, an improved ant colony algorithm with dynamic adaptive parameters was proposed to complete the path planning work. Simulation results showed that, compared with other classical optimization algorithms, the proposed algorithm improved optimization speed while ensuring optimization effect and achieving better results in path planning of complex ship components.

In the current research, kinematic analysis of a humanoid NAO is attempted. Here, both Denavit–Hartenberg (DH) parameter approach and multibody formulation approach have been analyzed. In the DH parameter approach, the NAO robot is solved by separating it into five individual kinematic chains. In the multibody formulation approach, NAO is divided into 15 segments, and each segment is analyzed. Kinematic analysis holds a significant importance; as from the data obtained in the kinematic analysis, the robots can be designed for real-time path planning and navigation. The current analysis is a novel approach to analyze the NAO based on its kinematic constraints.

With the widespread development of leg exoskeletons to provide external force-based repetitive training for gait rehabilitation, the prospect of undesired movement adaptation due to applied forces and imposed constraints require adequate investigation. A cable-driven leg exoskeleton, CDLE, presents a lightweight, flexible, and redundantly actuated architecture that enables the possibility of system parameters modulation to alter human–robot interaction while applying the desired forces. In this work, multi-joint stiffness performance of CDLE is formulated to systematically analyze human–CDLE interaction. Further, potential alterations in CDLE architecture are presented to tune human–CDLE interaction that favors the desired human leg movement during a gait rehabilitation paradigm.

In mobile robot localization with multiple sensors, myriad problems arise as a result of inadequacies associated with each of the individual sensors. In such cases, methodologies built upon the concept of multisensor fusion are well-known to provide optimal solutions and overcome issues such as sensor nonlinearities and uncertainties. Artificial neural networks and fuzzy logic (FL) approaches can effectively model sensors with unknown nonlinearities and uncertainties. In this article, a robust approach for localization (positioning) of a mobile robot in indoor as well as outdoor environments is proposed. The neural network is utilized as a pseudo-sensor that models the global positioning system (GPS) and is used to predict the robot’s position in case of GPS signal loss in indoor environments. The data from proprioceptive sensors such as inertial sensors and GPS are fused using the Kalman and the complementary filter-based fusion schemes in the outdoor case. To eliminate the position inaccuracies due to wheel slippage, an expert FL system (FLS) is implemented and cascaded with the sensor fusion module. The proposed technique is tested both in simulation and in real scenarios of robot movements. The simulations and results from the experimental platform validate the efficacy of the proposed algorithm.

Visual tracking is an essential building block for target tracking and capture of the underwater vehicles. On the basis of remotely autonomous control architecture, this paper has proposed an improved kernelized correlation filter (KCF) tracker and a novel fuzzy controller. The model is trained to learn an online correlation filter from a plenty of positive and negative training samples. In order to overcome the influence from occlusion, the improved KCF tracker has been designed with an added self-discrimination mechanism based on system confidence uncertainty. The novel fuzzy logic tracking controller can automatically generate and optimize fuzzy rules. Through Q-learning algorithm, the fuzzy rules are acquired through the estimating value of each state action pairs. An S surface based fitness function has been designed for the improvement of learning based particle swarm optimization. Tank and channel experiments have been carried out to verify the proposed tracker and controller through pipe tracking and target grasp on the basis of designed open frame underwater vehicle.

Self-localization in highly dynamic environments is still a challenging problem for humanoid robots with limited computation resource. In this paper, we propose a dual-channel unscented particle filter (DC-UPF)-based localization method to address it. A key novelty of this approach is that it employs a dual-channel switch mechanism in measurement updating procedure of particle filter, solving for sparse vision feature in motion, and it leverages data from a camera, a walking odometer, and an inertial measurement unit. Extensive experiments with an NAO robot demonstrate that DC-UPF outperforms UPF and Monte–Carlo localization with regard to accuracy.

The authors summarize the main dynamic balancing methods of robotic mechanisms in this paper. The majority of dynamic balancing methods have been presented, and there may be other dynamic balancing methods that are not included in this paper. Each of the balancing methods is reviewed and discussed. The advantages and disadvantages of each method are presented and compared. The goal of this paper is to provide an overview of recent research in balancing. The authors hope that this study can provide an informative reference for future research in the direction of dynamic balancing of robotic mechanisms.