This paper deals with motion planning and control problems for a class of partially differentially flat systems. They possess a feature that the derivative of the fiber variable can be represented purely by the base variable and its derivatives. Based on this feature, a Beta function-based motion planning algorithm is proposed with less computational cost compared with the optimal control formulation while providing similar system performance. Then, an adaptive controller is constructed through a function approximation technique-based approach. Finally, the feasibility of the proposed motion planning and control algorithms is verified by simulations.

This paper proposes a novel laser beam tracking mechanism for a mobile target robot that is used in shooting ranges. Compared with other traditional tracking mechanisms and modules, the proposed laser beam tracking mechanism is more flexible and low cost in use. The mechanical design and the working principle of the tracking module are illustrated, and the complete control system of the mobile target robot is introduced in detail. The tracking control includes two main steps: localizing the mobile target robot with regards to the position of the laser beam and tracking the laser beam by the linear quadratic regulator (LQR). First of all, the state function of the control system is built for this tracking system; second, the control law is deduced according to the discretized state function; lastly, the stability of the control method is proved by the Lyapunov theory. The experimental results demonstrate that the Hue, Saturation, Value feature-extracting method is robust and is qualified to be used for localization in the laser beam tracking control. It is verified through experiments that the LQR method is of better performance than the conventional Proportional Derivative control in the aspect of converge time, lateral error control, and distance error control.

This paper formulates and solves a new problem of global practical inverse optimal exponential path-tracking control of mobile robots driven by Lévy processes with unknown characteristics. The control design is based on a new inverse optimal control design for nonlinear systems driven by Lévy processes and ensures global practical exponential stability almost surely and in the pth moment for the path-tracking errors. Moreover, it minimizes cost function that penalizes tracking errors and control torques without having to solve a Hamilton–Jacobi–Bellman or Hamilton–Jaccobi–Isaacs equation.

In this study, AA5083-reinforced multiwalled carbon nanotubes (MWCNT) nanocomposites were selected as the alternate material for a redundant articulated robot (RAR) design by varying the composition of MWCNT wt%. By assigning AA5083-reinforced MWCNT as a custom material to the parts of RAR developed by Solid Works and exported to MATLAB/SimMechanics platform to convert the model into multi-body system blocks. The dynamic parameter torque was observed utilising simulation capability in a SimMechanics second-generation environment. The simulation results inferred that AA5083 reinforced with increased wt% of MWCNT has better properties suitable for RAR design.

In this article, a novel mechanism for planar one-legged hopping robots is proposed. The robot consists of a flat foot which is pinned to the leg and a reciprocating mass which is connected to the leg via a prismatic joint. The proposed mechanism performs the hopping by transferring linear momentum between the reciprocating mass and its main body. The nonlinear equations of the motion of the robot are derived using the Euler–Lagrange equations. To accomplish a stable jump, appropriate trajectories have been planned. To guarantee a stable response for this nonlinear system, a sliding-mode controller is implemented. The performance of the hopping robot is investigated through numerical simulations. The results confirm the stability of the hopping robot through the jump cycle on a flat surface and in climbing up and down ramp and stairs.

In this paper, a new hybrid fault-tolerant control (FTC) strategy based on nonsingular fast integral-type terminal sliding mode (NFITSM) and time delay estimation (TDE) is proposed for a Schönflies parallel manipulator. In order to detect, isolate, and accommodate actuator faults, TDE is used as an online fault estimation algorithm. Stability analysis of the closed-loop system is performed using Lyapunov theory. The proposed controller performance is compared with conventional sliding mode and feedback linearization control methods. The obtained results reveal the superiority of the proposed FTC based on TDE and NFITSM.

This paper proposes an approach for the high-level programming of co-designed robots that reduces programming complexity. Particularly, the work presented focuses on the programming framework of an intelligent system, based on the IEEE Standard Ontologies for Robotics and Automation, which allows users the automatic design of robots and the automatic implementation of controllers in the Robot Operating System (ROS). In our approach, the co-designed robot functionalities are automatically translated into visual programming blocks allowing non-expert users an easy robot programming by means of a visual programming language. Several robot configurations and three case studies are provided as a proof of concept. The validation, in terms of usability, of the framework has been carried out with inexperienced users showing promising results.

This paper proposes a robust controller for the generation of stable limit cycles in multi-input mechanical systems subjected to model uncertainties. The proposed idea is based on Port-Controlled Hamiltonian (PCH) model and energy-based control by considering the Hamiltonian function as the Lyapunov function. For this purpose, first, a nominal controller is designed by shaping the energy function of the system according to the structure of the desired limit cycle. Then, an additional robustifying control term is designed based on the integral sliding mode method with the selection of an appropriate sliding surface. Finally, computer simulations for two practical case studies are provided to confirm the effectiveness of the proposed controller in the generation of stable limit cycles in the presence of uncertainties.

In order to achieve high efficient self-motion for a redundant robot manipulator, a novel quadratic programming and varying-gain recurrent neural network based varying-gain neural self-motion (VGN-SM) approach is proposed and developed. With VGN-SM, the convergence errors can be adaptively and efficiently converged to zero. For comparisons, a traditional fixed-parameter neural self-motion (FPN-SM) approach is also presented. Theoretical analysis shows that the proposed VGN-SM has higher accuracy than the traditional FPN-SM. Finally, comparative experiments between VGN-SM and FPN-SM are performed on a six degrees-of-freedom robot manipulator to verify the advantages of the novel VGN-SM.

In this paper, mathematical models of kinematics, statics and inverse dynamics are derived firstly according to the mechanical structure of leg hydraulic drive system (LHDS). Then, all the above models are integrated with MATLAB/Simulink to build the LHDS simulation model, the model not only considers influence of leg dynamic characteristics on hydraulic system but also takes into account nonlinearity, variable load characteristics and other common problems brought by hydraulic system, and solves compatibility and operation time which brought by using multiple software simultaneously. The experimental results show the simulation model built in this paper can accurately express characteristics of the system.

The design and experimental works of an autonomous robotic platform for road marks painting are presented in this paper as the first autonomous system of its kind. The whole system involves two main sub-systems, namely: an autonomous mobile robot navigation system which is used for recognizing the roads and estimating the position of road marks, and automatic road marks painting system that is attached to the mobile robot platform to control the spray of the paint on the road’s surface. The experimental results show the capability of the proposed system to perform the task of autonomous road marks painting with accuracy of ±10 cm.

The particularity of nuclear power plant environment requires that the nuclear power inspection robot must be remote control operation. The main purpose of the inspection robot is to carry out inspection, prevention, reporting, and safety emergency operation on the instruments, so as to provide guarantee for the safe operation of the nuclear power plant. Based on the representative configuration of nuclear power robot at home and abroad, this paper develops a small and lightweight nuclear power plant inspection robot, including walking mechanism, lifting mechanism, operating mechanism, image acquisition, information communication and control system, etc., to carry on the statics analysis to the key components of the inspection robot and verify that the stiffness and strength of the mechanical structure meet the requirements of lightweight design. Modal analysis is carried out to verify that the motor does not cause resonance when working. The kinematic model of the robot has been established and can provide the theoretical basis for the controller design. A hierarchical control system based on LabVIEW upper computer monitoring and control operation interface is established, which uses adaptive fuzzy Proportional Integral Derivative (PID) control to simulate the walking control, and then realizes the control of walking mechanism through software programming, and the adaptive fuzzy PID control has better effect than the conventional PID control. The S-type acceleration and deceleration algorithm is used to realize the accurate control of the position location of the lifting mechanism. Finally, combined with the experiment of 5MS robot comprehensive experimental platform, it is proved that the inspection robot can realize remote control function operation.

Over the past decade, research on human–robot collaboration has grown exponentially, motivated by appealing applications to improve the daily life of patients/operators. A primary requirement in many applications is to implement highly “transparent” control laws to reduce the robot impact on human movement. This impact may be quantified through relevant motor control indices. In this paper, we show that control laws based on careful identification procedures improve transparency compared to classical closed-loop position control laws. A new performance index based on the ratio between electromyographic activity and limb acceleration is also introduced to assess the quality of human exoskeleton interaction.

Optimal grasping points for a robotic gripper were derived, based on object and hand geometry, using deep neural networks (DNNs). The optimal grasping cost functions were derived using probability density functions for each local cost function of the normal distribution. Using the DNN, the optimum height and width were set for the robot hand to grasp objects, whose geometric and mass centre points were also considered in obtaining the optimum grasping positions for the robot fingers and the object. The proposed algorithm was tested on 10 differently shaped objects and showed improved grip performance compared to conventional methods.