This article proposes a method for incremental data dimensionality reduction in loop closure detection for robotic autonomous navigation. The approach uses dominant eigenvector concept for: (a) spectral description of visual datasets and (b) representation in low dimension. Unlike most other papers on data dimensionality reduction (which is done in batch mode), our method combines a sliding window technique and coordinate transformation to achieve dimensionality reduction in incremental data. Experiments in both simulated and real scenarios were performed and the results are suitable.

The 7 degrees of freedom (DOF) redundant manipulator greatly improves obstacle/singularity avoidance capability and operational flexibility. However, the inverse kinematics problem of this manipulator is very difficult to solve because it has an infinite number of solutions. This paper uses a new numerical sequence processing method with a closed-loop framework to solve the inverse kinematics of the 7-DOF redundant manipulator. Simulation and experiment show that this method has high commonality. No special structure of the robot is required, and this method has improved computational efficiency and reliability.

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.

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.

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.

This paper proposes a shared control scheme which aims to achieve a stable control of the speed and turn of a bipedal robot during a delayed bilateral teleoperation. The strategy allows to get a delay-dependent damping value that must be injected to assure a bounded response of the hybrid system, while simultaneously, the human operator receives a force feedback that help him to decrease the synchronism error. Furthermore, a test where a human operator handles the walking of a simulated bipedal robot, to follow a curve path in front of varying time delay, is performed and analyzed.

Rendering of rigid objects with high stiffness while guaranteeing system stability remains a major and challenging issue in haptics. Being a part of the haptic system, the behavior of human operators, represented as the mechanical impedance of arm, has an inevitable influence on system performance. This paper first verified that the human arm impedance can unconsciously be modified through imposing background forces and resist unstable motions arising from external disturbance forces. Then, a reliable impedance tuning (IT) method for improving the stability and performance of haptic systems is proposed, which tunes human arm impedance by superimposing a position-based background force over the traditional haptic workspace. Moreover, an adaptive IT algorithm, adjusting the maximum background force based on the velocity of the human arm, is proposed to achieve a reasonable trade-off between system stability and transparency. Based on a three-degrees-of-freedom haptic device, maximum achievable stiffness and transparency grading experiments are carried out with 12 subjects, which verify the efficacy and advantage of the proposed method.

An improved FastSLAM based on the robust square-root cubature Kalman filter (RSRCKF) with partial genetic resampling is proposed in this paper. In the proposed method, RSRCKF is used to design the proposal distribution of FastSLAM and to estimate environment landmarks. The proposed method does not require a priori knowledge of the noise statistics. In addition, to increase diversity, it uses the genetic operators-based strategy to further improve the particle diversity. In fact, a partial genetic resampling operation is carried out to maintain the diversity of particles. The proposed method is compared with other methods via simulation and experimental data. It can be seen from the results that the proposed method provides significantly more accurate and robust estimation results compared with other methods even with fewer particles and unknown a priori. In addition, the consistency of the proposed method is better than that of other methods.

Soft robots can perform effectively inspecting than rigid robots in some special environments such as nuclear pipelines and high-voltage cables. This article presents a versatile quadruped soft rod-climbing robot (SR-CR) that consists of four bending actuators and a telescopic actuator. The bending actuator is composed of flexible bellows with multiple folding air chambers, elastic telescopic layer (ETL), and strain-limiting layer (SLL). The telescopic actuator provides the energy for the robot to climb forward. The SR-CR is activated by a control strategy that alternates the body deformation and feet pneumatic clenched for stable climbing. The robot can climb rods at 90°, with the maximum speed of up to 2.33 mm/s (0.018 body length/s). At 0.83 HZ, the maximum moving speed of the robot in climbing horizontally parallel rods can reach 18.43 mm/s. In addition, the SR-CR can also achieve multiple impressive functions, including turning around a corner at a rate of 7 mm/s (0.054 body length/s), carrying a payload of 3.7 times its self-weight on horizontal rods at a speed of 9 mm/s (0.069 body length/s).

This work presents a multimode flight framework control scheme for a quadrotor based on the super twisting algorithm. The controller design stages for six flight control modes are presented. The stability proof for each flight mode is carried out by means of Lyapunov functions, while the stability analysis for the complete control scheme, when a transition from one flight mode to another occurs, is demonstrated using the switching nonlinear systems theory. The performance of the proposed framework is shown in a 3D simulation environment considering a forest fire detection task, which takes into account external disturbances.

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.

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.