Héctor M. Becerra

A single visual-servo controller of mobile robots with super-twisting control

This paper presents a novel approach for image-based visual servoing, extending the existing works that use the trifocal tensor (TT) as source for image measurements. In the proposed approach, singularities typically encountered in this kind of methods are avoided. A formulation of the TT-based control problem with a virtual target resulting from the vertical translation of the real target allows us to design a single controller, able to regulate the robot pose towards the desired configuration, without local minima. In this context, we introduce a super-twisting control scheme guaranteeing continuous control inputs, while exhibiting strong robustness properties. Our approach is valid for perspective cameras as well as catadioptric systems obeying the central camera model. All these contributions are supported by convincing numerical simulations and experiments under a popular dynamic robot simulator.

Visual path following using a sequence of target images and smooth robot velocities for humanoid navigation

In this paper, we propose an approach of following a visual path for humanoid navigation. The problem consists in computing appropriate robot velocities for the humanoid walking task from the visual data shared between the current robot view and a set of target images. Two types of visual controllers are evaluated: a position-based scheme and an image-based scheme. Both of them rely on the estimation of the homography model even for non-planar scenes. We assume that the sequence of target images is given and we focus on the controllers performance. Because classical visual path following controllers generate discontinuous robot velocities, we propose a generic controller (applicable for different types of visual feedback) to alleviate this issue, which is a main contribution of the paper. The stability of such controller is addressed theoretically and verified through experiments with a NAO humanoid robot.

A visual feedback-based time-optimal motion policy for capturing an unpredictable evader

In this paper, we address the pursuit–evasion problem of capturing an unpredictable omnidirectional evader using a differential drive robot (DDR) in an obstacle-free environment. We present three main contributions. (1) We provide a state feedback-based time-optimal motion policy for the DDR. The motion policy is based on a partition of the state space. One main contribution of this paper is to provide algebraic equations of the regions’ boundaries of this partition in terms of the state-space coordinates. (2) We estimate the state of the evader based on images using the one-dimensional trifocal tensor. We propose a new formulation of the estimation of the evaders state relative to the pursuer. (3) We present a bound, for conventional cameras, over the pursuers field of view that guarantees that, if the evader is initially visible, it will remain visible (inside the cameras view) regardless of its motion strategy, until the capture condition is achieved. We also present an implementation of the pursuers motion policy, the estimation of the evaders state and also present simulation results of the pursuit/evasion game.

Visual navigation of wheeled mobile robots using direct feedback of a geometric constraint

Many applications of wheeled mobile robots demand a good solution for the autonomous mobility problem, i.e., the navigation with large displacement. A promising approach to solve this problem is the following of a visual path extracted from a visual memory. In this paper, we propose an image-based control scheme for driving wheeled mobile robots along visual paths. Our approach is based on the feedback of information given by geometric constraints: the epipolar geometry or the trifocal tensor. The proposed control law only requires one measurement easily computed from the image data through the geometric constraint. 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 in previous works when the target image changes. The translational velocity is adapted as demanded for the path and the resultant motion is independent of this velocity. Furthermore, our approach is valid for all cameras with approximated central projection, including conventional, catadioptric and some fisheye cameras. Simulations and real-world experiments illustrate the validity of the proposal.

Visual servo walking control for humanoids with finite-time convergence and smooth robot velocities

ABSTRACT In this paper, we address the problem of humanoid locomotion guided from information of a monocular camera. The goal of the robot is to reach a desired location defined in terms of a target image, i.e., a positioning task. The proposed approach allows us to introduce a desired time to complete the positioning task, which is advantageous in contrast to the classical exponential convergence. In particular, finite-time convergence is achieved while generating smooth robot velocities and considering the omnidirectional waking capability of the robot. In addition, we propose a hierarchical task-based control scheme, which can simultaneously handle the visual positioning and the obstacle avoidance tasks without affecting the desired time of convergence. The controller is able to activate or inactivate the obstacle avoidance task without generating discontinuous velocity references while the humanoid is walking. Stability of the closed loop for the two task-based control is demonstrated theoretically even during the transitions between the tasks. The proposed approach is generic in the sense that different visual control schemes are supported. We evaluate a homography-based visual servoing for position-based and image-based modalities, as well as for eye-in-hand and eye-to-hand configurations. The experimental evaluation is performed with the humanoid robot NAO.

Modeling and Finite-Time Walking Control of a Biped Robot with Feet

This paper addresses the problem of modeling and controlling a planar biped robot with six degrees of freedom, which are generated by the interaction of seven links including feet. The biped is modeled as a hybrid dynamical system with a fully actuated single-support phase and an instantaneous double-support phase. The mathematical modeling is detailed in the first part of the paper. In the second part, we present the synthesis of a controller based on virtual constraints, which are codified in an output function that allows defining a local diffeomorphism to linearize the robot dynamics. Finite-time convergence of the output to the origin ensures a collision between the swing foot and the ground with an appropriate configuration for the robot to give a step forward. The components of the output track adequate references that encode a walking pattern. Finite-time convergence of the tracking errors is enforced by using second-order sliding mode control. The main contribution of the paper is an evaluation and comparison of discontinuous and continuous sliding mode control in the presence of parametric uncertainty and external disturbances. The robot model and the synthesized controller are evaluated through numerical simulations.

Fuzzy Visual Control for Memory-Based Navigation Using the Trifocal Tensor

In this paper, we present a control scheme for visual path-following of wheeled mobile robots based on a robust geometric constraint: the trifocal tensor (TT). The proposed control law only needs one element of the TT as feedback information, which is computed from the current and the target images along the sequence of the visual path. The scheme is valid for images captured by cameras having approximately a unique center of projection, e.g., conventional, central catadioptric and some fisheye cameras. The benefits of the proposed scheme are that explicit pose parameters decomposition is not required and the rotational velocity is smooth or eventually piece-wise constant avoiding discontinuities that generally appear when a new target image must be reached. Additionally, the translational velocity is adapted as required for the path. The validity and performance of the approach is shown through realistic simulations using synthetic images.

A Robust Control Scheme Based on the Trifocal Tensor

In this chapter, we rely on the natural geometric constraint for three views, the trifocal tensor. We present a novel image-based visual servoing scheme that also solves the pose regulation problem, in this case by exploiting the properties of omnidirectional images to preserve bearing information. This is achieved by using the additional information of a third image in the geometric model through a simplified trifocal tensor, which can be computed directly from image features avoiding the need of a complete camera calibration for any type of central camera. The main idea of the chapter is that the elements of the tensor are introduced directly in the control law and neither any a prior knowledge of the scene nor any auxiliary image are required. Additionally, a sliding mode control law in a square system ensures stability and robustness for the closed loop. The good performance of the control system is proven via simulations and real-world experiments with a hypercatadioptric imaging system.

Virtual Target Formulation for Singularity-Free Visual Control Using the Trifocal Tensor

We present a novel approach for visual control of wheeled mobile robots, extending the existing works that use the trifocal tensor as source for measurements. In our approach, singularities typically encountered in this kind of methods are removed by formulating the control problem based on the trifocal tensor and by using a virtual target vertical translated from the real target. A single controller able to regulate the robot pose towards the desired configuration without local minima is designed. Additionally, the proposed approach is valid for perspective cameras as well as catadioptric systems obeying a central camera model. All these contributions are supported by convincing simulations.

Humanoid navigation using a visual memory with obstacle avoidance

Abstract We present a complete humanoid navigation scheme based on a topological map known as visual memory (VM), which is composed by a set of key images acquired offline by means of a supervised teaching phase (human-guided). Our autonomous navigation scheme integrates the humanoid localization in the VM, a visual path planner and a path follower with obstacle avoidance. We propose a pure vision-based localization algorithm that takes advantage of the topological structure of the VM to find the key image that best fits the current image in terms of common visual information. In addition, the visual path planner benefits obstacle-free paths. The VM is updated when a new obstacle is detected with an RGB-D camera mounted on the humanoid’s head. The visual path following and obstacle avoidance problems are formulated in a unified sensor-based framework in which, a hierarchy of tasks is defined, and the transitions of consecutive and hierarchical tasks are performed smoothly to avoid instability of the humanoid. An extensive experimental evaluation using the NAO platform shows the good performance of the navigation scheme.