In this article, we propose a robust hierarchical inverse dynamics control scheme for redundant manipulators, which guarantees both predefined-time convergence of regulation tasks and robustness against model uncertainties/disturbances. Predefined-time convergence of a robotic task means that the task error is regulated to the origin at a desired preset time, independently of the initial state of the robot. The strict hierarchy of tasks is ensured by the so-called dynamic consistency, which avoids conflicts between decoupled task constraints. Thus, it enables the robot with more dexterity for reaching targets in its workspace while overcoming obstacles and joint limits. Predefined-time convergence facilitates the design of a time schedule to execute sequential and simultaneous tasks. All these features are supported by a stability analysis and experiments with two torque-controlled mobile manipulators in pick-and-place applications.

A concurrent optimum design of a manipulator is to find the best geometrical and control parameters in the same optimization process. One of the main contributions of this paper is a concurrent method for optimum design and its comparison versus a sequential one, where the later requires several optimization stages. Besides, we statistically compare three metaheuristics: the Omnioptimizer, the covariance matrix adaptation evolutionary strategy, and the Boltzmann univariate marginal distribution algorithm for the two methodologies, in order to elucidate directions about which metaheuristic performs the best for this kind of problem, and whether it must be used in a sequential or concurrent fashion. These metaheuristics are compared with reported results in the specialized literature. In addition, we perform an analysis of results to statistically determine relations between the parameters and the objective function, as a consequence, we found the most impacting parameters to the manipulator performance.

Two robust kinematic controllers for position trajectory tracking of a perturbed wheeled mobile robot are presented. We address a final objective of fixed-time pose-regulation, which means that the robot position and orientation must reach desired final values simultaneously in a user-defined time. To achieve that, we propose the robust tracking of adequate trajectories for position, which drives the robot to get a desired final orientation due to the nonholonomic motion constraint. Hence, the main contribution of the paper is a complete strategy to define adequate reference trajectories as well as robust controllers to track them in order to enforce the pose-regulation of a wheeled mobile robot in a desired time. Realistic simulations show the good performance of the proposed scheme even in the presence of strong disturbances.

This paper addresses the problem of exploring an unknown, planar, polygonal and simply connected environment. A saliency object (i.e. a landmark) is located in the environment. The collision-free subset of the robot's configuration space is simply connected or it might have several connected components. The robot is a differential drive system shaped as a disc. The robot has limited sensing, namely it is incapable of measuring any distance or angle, or performing self localization. The exploration problem consists in discovering the environment with the robot's sensor. To solve this problem, a motion policy is developed based on simple sensor feedback and a complete exploration strategy is represented as a Moore Machine. The proposed exploration strategy guarantees that the robot will discover the largest possible region of the environment. Consequently, the robot will find the landmark or declare that an exploration strategy to find it does not exist.

In this paper, we propose to exploit the epipolar geometry for dynamic pose-estimation of mobile robots. This is performed using a filtering approach with measurements given by the epipoles. The contribution of the paper is a novel observability analysis using nonlinear and linear tools that leads to achieve an efficient position-based visual servoing (PBVS) approach. Additionally, the visibility constraint problem is solved for this type of approach by using any visual sensor obeying a central projection model. This scheme does not need a target model neither scene reconstruction nor the 3D structure. The effectiveness of the proposed estimation scheme is evaluated via simulations in servoing tasks including obstacles and using omnidirectional vision.

Driving mobile robots to precise locations is of recognized interest, and using vision sensors in this context supplies many advantages. We propose a novel control law based on sliding-mode theory in order to drive mobile robots to a target location, which is specified by a previously acquired reference image. The control scheme exploits the piecewise epipolar geometry of three views on the basis of image-based visual servoing, in such a way that no 3-D scene information is required. The contribution of the paper is a new control law that achieves convergence to the target with no auxiliary images and without changing to any approach other than epipolar-based control. Additionally, the use of sliding-mode control deals with singularities, thus allowing the robot to move directly toward the target as well as avoiding the need of a precise camera calibration. The effectiveness of our approach is tested with simulations and real-world experiments.

In this paper, we propose a visual path following control scheme for wheeled mobile robots based on the epipolar geometry. The control law only requires the position of the epipole computed between the current and target views along the sequence of a visual memory. The proposed approach has two main advantages: explicit pose parameters decomposition is not required and the rotational velocity is smooth or eventually piece-wise constant avoiding discontinuities that generally appear when the target image changes. The translational velocity is adapted as required for the path and the approach is independent of this velocity. Furthermore, our approach is valid for all cameras obeying the unified model, including conventional, central catadioptric and some fisheye cameras. Simulations as well as real-world experiments with a robot illustrate the validity of our approach.

This paper presents an image-based approach to perform visual control for differential-drive robots. We use for the first time the elements of the 1D trifocal tensor directly in the control law. The visual control utilizes the usual teach-by showing strategy without requiring any a prior knowledge of the scene and does not need any auxiliary image. The main contribution of the paper is that the proposed two-steps control law ensures total correction of both position and orientation without switching to any other visual constraint rather than the 1D trifocal tensor. The paper exploits the sliding mode control technique in a square system, ensuring stability and robustness for the closed loop. The good performance of the control system is proven via simulations.

A pose-estimation-based approach to perform visual control for differential-drive robots is presented in this paper. Our scheme recovers the camera location (position and orientation) using an extended kalman filter (EKF) algorithm with the 1D trifocal tensor (TT) as measurement model. This new visual servoing scheme allows knowing the real world path performed by the robot without the computational load introduced by position-based approaches. A state-estimated feedback control law is designed to solve a tracking problem for the lateral and longitudinal robot coordinates. The desired trajectories to be tracked ensure total correction of both position and orientation using a single control law, even though the orientation is a DOF in the control system. The effectiveness of our approach is tested via simulations.

In this paper, a robust control technique (sliding mode control) is proposed to be used in order to perform visual servoing for differential-drive mobile robots using the classical teach-by-showing strategy. We propose a commuted sliding mode control law that exploits the epipolar geometry. The major contribution of the paper is the design of a control law that solves the problem of passing through a singularity induced by the epipoles maintaining bounded inputs. Moreover, the designed control is able to drive the robot to the target even when it just starts on the singularity. The proposed approach does not need a precise camera calibration due to the robustness of the control system under uncertainty in parameters. It also ensures entire correction of both orientation and lateral error even with noise in the image. The effectiveness of our approach is tested via simulations.