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We address the problem of systematically covering all accessible floor space in an unknown environment by a mobile robot. Our approach uses rectangular regions that are swept across the environment. In the first stage, the robot covers each region using the classic boustrophedon pattern and planning paths to uncovered areas within the region while keeping track of its position uncertainty. The region...
Efficiently solving large-scale sparse linear systems is important for robot mapping and navigation. Recently, the subgraph-preconditioned conjugate gradient method has been proposed to combine the advantages of two reigning paradigms, direct and iterative methods, to improve the efficiency of the solver. Yet the question of how to pick a good subgraph is still an open problem. In this paper, we propose...
Graphical models jointly with non linear optimization have become the most popular approaches for solving SLAM and Bundle Adjustment problems: using a non linear least squares (NLSQs) description of the problem, these math tools serve to formalize the minimization of an error cost function that relates state variables through relative sensor observations. The simplest case just considers as state...
This paper presents a strategy for large-scale SLAM through solving a sequence of linear least squares problems. The algorithm is based on submap joining where submaps are built using any existing SLAM technique. It is demonstrated that if submaps coordinate frames are judiciously selected, the least squares objective function for joining two submaps becomes a quadratic function of the state vector...
Simultaneous Localization and Mapping (SLAM) is one of the most difficult tasks in mobile robotics. While the construction of consistent and coherent local solutions is simple, the SLAM remains a critical problem as the distance travelled by the robot increases. To circumvent this limitation, many strategies divide the environment in small regions, and formulate the SLAM problem as a combination of...
In the state-of-the-art approaches to SLAM, the problem is often formulated as a non-linear least squares. SLAM back-ends often employ iterative methods such as Gauss-Newton or Levenberg-Marquardt to solve that problem. In general, there is no guarantee on the global convergence of these methods. The back-end might get trapped into a local minimum or even diverge depending on how good the initial...
This paper presents high speed and high accuracy visual servoing system. The algorithm has three major improvements, which can be implemented in practical applications; Firstly high-accuracy pose estimation by using stereo cameras, secondly real time implementation issues with non-real-time image processing platform and thirdly a consideration for industrial position controller. To resolve the issues,...
This paper presents practical vision-based collision avoidance for objects approximating a single point feature. Using a spherical camera model, a visual predictive control scheme guides the aircraft around the object along a conical spiral trajectory. Visibility, state and control constraints are considered explicitly in the controller design by combining image and vehicle dynamics in the process...
Aim at enhancing dexterous and safe operation in unstructured environment, a cable-driven soft robotic manipulator is designed in this paper. Due to soft material it made of and nearly infinite degree of freedom it owns, the soft robotic manipulator has higher security and dexterity than traditional rigid-link manipulator, which make it suitable to perform tasks in complex environments that is narrow,...
This paper introduces a new comprehensive solution for the open problem of uncalibrated 3D image-based stereo visual servoing for robot manipulators. One of the main contributions of this article is a novel 3D stereo camera model to map positions in the task space to positions in a new 3D Visual Cartesian Space (a visual feature space where 3D positions are measured in pixel). This model is used to...
This article addresses the problem of direct vision-based robot control where the equilibrium state is defined via a reference image. Direct methods refer to intensity-based nonmetric techniques to perform that stabilization. Intensity-based strategies provide for higher accuracy, whereas not requiring any metric information improves their versatility. However, existing direct techniques either have...
We present a new image-based visual servoing scheme for tracking moving targets. This is achieved with a twofold approach. First, we devise a straightforward adaptation of a previously proposed depth observer to account for the fact that the target is not stationary. Second, we estimate the disturbance on the visual feature dynamics due to the target motion, and we add a related compensation term...
On-the-fly learning systems are necessary for the deployment of general purpose robots. New training examples for such systems are often supplied by mentor interactions. Due to the cost of acquiring such examples, it is desirable to reduce the number of necessary interactions. Transfer learning has been shown to improve classification results for classes with small numbers of training examples by...
This paper proposes a method for vascular load reduction control that operates in regard to the load on contact points of catheter and blood vessels in catheter insertion. We make an extracorporeal estimation of the load on the contact points, and perform control with a robot arm to reduce the estimated load. We aim to reduce the vascular load through extracting and vibrating action as actually applied...
This paper proposes an identification technique of a human standing controller. The dynamics of a human is approximated by the macroscopic relationship between the center of mass and the zero-moment point. The standing controller is modelled by a piecewise-linear feedback, which was originally developed for humanoid robots. In the previous work, the authors found a qualitative similarity of the model...
Teaching robots everyday tasks like making pancakes by instructions requires interfaces that can be intuitively operated by non-experts. By performing novel manipulation tasks in a virtual environment using a data glove task-related information of the demonstrated actions can directly be accessed and extracted from the simulator. We translate low-level data structures of these simulations into meaningful...
To achieve a certain task, a skilligent robot should be able to learn the skills embedded in that task. Furthermore, the robot should be able to infer such skills to handle uncertainties and perturbations, since most robot tasks are usually daily-life tasks that include many unexpected situations. Therefore, we propose a unified skill learning and inference framework. The framework includes six processing...
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