In this paper, hybrid control of central pattern generators (CPGs), along with an adaptive supper-twisting sliding mode (ASTSM) control based on supper-twisting state observer, is proposed to guard against disturbances and uncertainties. Rhythmic and coordinated signals are generated using CPGs. In addition, to overcome the chattering of conventional sliding mode, supper-twisting sliding mode has been applied. The ASTSM method triggers sliding variables, and its derivatives tend to zero continuously in the presence of the uncertainties. Moreover, to acquire maximum stability, the desired trajectory of the upper limb based on zero moment point criterion is designed.

This paper presents a bi-level adaptive computed-current impedance controller for electrically driven robots. This study aims to reduce calculation complexities by utilizing the electrical equations of actuators, instead of the entire model of the electromechanical system. Moreover, taking the dynamical effects of mechanical parts into account through the current’s feedback, external disturbances are compensated. In order to handle uncertainties, a bi-level optimization problem is formulated to obtain guaranteed stability besides the estimation convergence. An adaptation rule and its optimal tuning gain are achieved. The proposed method is applied to control of a rehabilitation robot to evaluate its performance.

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 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.

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.

This paper introduces a new RGBD-Simultaneous Localization And Mapping (RGBD-SLAM) based on a revisited keyframe SLAM. This solution improves the localization by combining visual and depth data in a local bundle adjustment. Then, it presents an extension of this RGBD-SLAM that takes advantage of a partial knowledge of the scene. This solution allows using a prior knowledge of the 3D model of the environment when this latter is available which drastically improves the localization accuracy. The proposed solutions called RGBD-SLAM and Constrained RGBD-SLAM are evaluated on several public benchmark datasets and on real scenes acquired by a Kinect sensor. The system works in real time on a standard central processing units and it can be useful for certain applications, such as localization of lightweight robots, UAVs, and VR helmet.

Accurate torque control is a critical issue in the compliant human–robot interaction scenario, which is, however, challenging due to the ever-changing human intentions, input delay, and various disturbances. Even worse, the performances of existing control strategies are limited on account of the compromise between precision and stability. To this end, this paper presents a novel high-performance torque control scheme without compromise. In this scheme, a new nonlinear disturbance observer incorporated with equivalent control concept is proposed, where the faster convergence and stronger anti-noise capability can be obtained simultaneously. Meanwhile, a continuous fractional power control law is designed with an iteration method to address the matched/unmatched disturbance rejection and global finite-time convergence. Moreover, the finite-time stability proof and prescribed control performance are guaranteed using constructed Lyapunov function with adding power integrator technique. Both the simulation and experiments demonstrate enhanced control accuracy, faster convergence rate, perfect disturbance rejection capability, and stronger robustness of the proposed control scheme. Furthermore, the evaluated assistance effects present improved gait patterns and reduced muscle efforts during walking and upstair activity.

An autonomous motion planning framework is proposed, consisting of path planning and trajectory generation. Primarily, a spacious preferred probabilistic roadmap algorithm is utilized to search a safe and short path, considering kinematics and threats from obstacles. Subsequently, a minimum-snap and position-clearance polynomial trajectory problem is transformed into an unconstrained quadratic programming and solved in a two-step optimization. Finally, comparisons with other methods based on statistical simulations are implemented. The results show that the proposed method achieves computational efficiency and a safe trajectory.

We present a 3D reconstruction method using brightness and camera motion estimation for registering local colon structure in colonoscopy. The proposed method is based on reverse projection from 2D fold contours to 3D space, motion estimation from 3D reconstructed points between neighboring frames, and model registration to reconstruct the fold structure. On the synthetic colon, the average percentages of the reconstructed depth error and circumference error are about 14.2% and 15.2%, respectively. The accuracy is enough for the navigation and control in capsule robot. This work demonstrates that the proposed method is superior to the methods using single-frame-based brightness intensity.

The current contribution presents a new sinkage sensor specified for an unmanned ground vehicle to find the exact sinkage zone of a wheel interacting with the soil particles. This sensor will be wrapped around the wheel, and consequently, contact analog outputs will be used in soil deposition and bulldozing effect prediction. Furthermore, the new sensor will be used for a novel soil flow calculation estimating the total mass variation of the control volume of soil particles beneath the wheel. Accordingly, the spiral model simulating the displacement of the particle is implemented to calculate the soil deposition.