Active rough shape estimation of unknown objects

International audienceThis paper presents a method to determine the rough shape of an object. This is a step in the development of a One Click Grasping Tool, a grasping tool of everyday-life objects for an assistant robot dedicated to elderly or disabled. The goal is to determine the quadric that approximates at best the shape of an unknown object using multi-view measurements. Non-linear optimization techniques are considered to achieve this goal. Since multiple views are necessary, an active vision process is considered in order to minimize the uncertainty on the estimated parameters and determine the next best view. Finally, results that show the validity of the approach are presented

Polar snakes: a fast and robust parametric active contour model

We present in this paper a way to perform a fast and robust image segmentation and to track a contour along a sequence of images. Our approach is based on a dynamic deformable model. More precisely, we revisit the physics basedmodel proposed in [1] to show the benefit of using a polar description to model the contour, in particular to cope with the well-known initialization problem. Indeed, we show that this way to proceed leads to diagonal and constant matrices in the equations of the snake evolution yielding therefore to a faster algorithm. Experimental results on image segmentation and contour tracking validate the efficiency of this new formulation. Index Terms — Active contour model, polar description, segmentation, contour tracking. 1

Grasping of Solid Industrial Objects Using 3D Registration

Robots allow industrial manufacturers to speed up production and to increase the product’s quality. This paper deals with the grasping of partially known industrial objects in an unstructured environment. The proposed approach consists of two main steps: (1) the generation of an object model, using multiple point clouds acquired by a depth camera from different points of view; (2) the alignment of the generated model with the current view of the object in order to detect the grasping pose. More specifically, the model is obtained by merging different point clouds with a registration procedure based on the iterative closest point (ICP) algorithm. Then, a grasping pose is placed on the model. Such a procedure only needs to be executed once, and it works even in the presence of objects only partially known or when a CAD model is not available. Finally, the current object view is aligned to the model and the final grasping pose is estimated. Quantitative experiments using a robot manipulator and three different real-world industrial objects were conducted to demonstrate the effectiveness of the proposed approach

Data-Driven Grasp Synthesis - A Survey

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic

Visual Grasping of Unknown Objects

The objective of the thesis is to compare and study recent visual grasping techniques which are applied on a robotic arm for grasping of unknown objects in an indoor environment. The novelty of the thesis is that the study has led to questioning the general approach used by researchers to solve the grasping problem. The result can help future researchers in investing more on the problem areas of grasping techniques and can also lead us to question ourselves on the approach we are using to solve the grasping problem

“I Want That”: Human-in-the-Loop Control of a Wheelchair-Mounted Robotic Arm

Wheelchair-mounted robotic arms have been commercially available for a decade. In order to operate these robotic arms, a user must have a high level of cognitive function. Our research focuses on replacing a manufacturer-provided, menu-based interface with a vision-based system while adding autonomy to reduce the cognitive load. Instead of manual task decomposition and execution, the user explicitly designates the end goal, and the system autonomously retrieves the object. In this paper, we present the complete system which can autonomously retrieve a desired object from a shelf. We also present the results of a 15-week study in which 12 participants from our target population used our system, totaling 198 trials

Autonomous Robotic Grasping in Unstructured Environments

A crucial problem in robotics is interacting with known or novel objects in unstructured environments. While the convergence of a multitude of research advances is required to address this problem, our goal is to describe a framework that employs the robot\u27s visual perception to identify and execute an appropriate grasp to pick and place novel objects. Analytical approaches explore for solutions through kinematic and dynamic formulations. On the other hand, data-driven methods retrieve grasps according to their prior knowledge of either the target object, human experience, or through information obtained from acquired data. In this dissertation, we propose a framework based on the supporting principle that potential contacting regions for a stable grasp can be found by searching for (i) sharp discontinuities and (ii) regions of locally maximal principal curvature in the depth map. In addition to suggestions from empirical evidence, we discuss this principle by applying the concept of force-closure and wrench convexes. The key point is that no prior knowledge of objects is utilized in the grasp planning process; however, the obtained results show that the approach is capable to deal successfully with objects of different shapes and sizes. We believe that the proposed work is novel because the description of the visible portion of objects by the aforementioned edges appearing in the depth map facilitates the process of grasp set-point extraction in the same way as image processing methods with the focus on small-size 2D image areas rather than clustering and analyzing huge sets of 3D point-cloud coordinates. In fact, this approach dismisses reconstruction of objects. These features result in low computational costs and make it possible to run the proposed algorithm in real-time. Finally, the performance of the approach is successfully validated by applying it to the scenes with both single and multiple objects, in both simulation and real-world experiment setups

Enhanced Learning Strategies for Tactile Shape Estimation and Grasp Planning of Unknown Objects

Grasping is one of the key capabilities for a robot operating and interacting with humans in a real environment. The conventional approaches require accurate information on both object shape and robotic system modeling. The performance, therefore, can be easily influenced by any noise sensor data or modeling errors. Moreover, identifying the shape of an unknown object under some vision-denied conditions is still a challenging problem in the robotics eld. To address this issue, this thesis investigates the estimation of unknown object shape using tactile exploration and the task-oriented grasp planning for a novel object using enhanced learning techniques. In order to rapidly estimate the shape of an unknown object, this thesis presents a novel multi- fidelity-based optimal sampling method which attempts to improve the existing shape estimation via tactile exploration. Gaussian process regression is used for implicit surface modeling with sequential sampling strategy. The main objective is to make the process of sample point selection more efficient and systematic such that the unknown shape can be estimated fast and accurately with highly limited sample points (e.g., less than 1% of number of data set for the true shape). Specifically, we propose to select the next best sample point based on two optimization criteria: 1) the mutual information (MI) for uncertainty reduction, and 2) the local curvature for fidelity enhancement. The combination of these two objectives leads to an optimal sampling process that balances between the exploration of the whole shape and the exploitation of the local area where the higher fidelity (or more sampling) is required. Simulation and experimental results successfully demonstrate the advantage of the proposed method in terms of estimation speed and accuracy over the conventional one, which allows us to reconstruct recognizable 3D shapes using only around optimally selected 0.4% of the original data set. With the available object shape, this thesis also introduces a knowledge-based approach to quickly generate a task-oriented grasp for a novel object. A comprehensive training dataset which consists of specific tasks and geometrical and physical knowledge of grasping is built up from physical experiment. To analyze and e fficiently utilize the training data, a multi-step clustering algorithm is developed based on a self-organizing map. A number of representative grasps are then selected from the entire training dataset and used to generate a suitable grasp for a novel object. The number of representative grasps is automatically determined using the proposed auto-growing method. In addition, to improve the accuracy and efficiency of the proposed clustering algorithm, we also develop a novel method to localize the initial centroids while capturing the outliers. The results of simulation illustrate that the proposed initialization method and the auto-growing method outperform some conventional approaches in terms of accuracy and efficiency. Furthermore, the proposed knowledge-based grasp planning is also validated on a real robot. The results demonstrate the effectiveness of this approach to generate task-oriented grasps for novel objects