Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

In this paper, a manipulation planning method for object re-orientation based on semantic segmentation keypoint detection is proposed for robot manipulator which is able to detect and re-orientate the randomly placed objects to a specified position and pose. There are two main parts: (1) 3D keypoint detection system; and (2) manipulation planning system for object re-orientation. In the 3D keypoint detection system, an RGB-D camera is used to obtain the information of the environment and can generate 3D keypoints of the target object as inputs to represent its corresponding position and pose. This process simplifies the 3D model representation so that the manipulation planning for object re-orientation can be executed in a category-level manner by adding various training data of the object in the training phase. In addition, 3D suction points in both the object's current and expected poses are also generated as the inputs of the next operation stage. During the next stage, Mask Region-Convolutional Neural Network (Mask R-CNN) algorithm is used for preliminary object detection and object image. The highest confidence index image is selected as the input of the semantic segmentation system in order to classify each pixel in the picture for the corresponding pack unit of the object. In addition, after using a convolutional neural network for semantic segmentation, the Conditional Random Fields (CRFs) method is used to perform several iterations to obtain a more accurate result of object recognition. When the target object is segmented into the pack units of image process, the center position of each pack unit can be obtained. Then, a normal vector of each pack unit's center points is generated by the depth image information and pose of the object, which can be obtained by connecting the center points of each pack unit. In the manipulation planning system for object re-orientation, the pose of the object and the normal vector of each pack unit are first converted into the working coordinate system of the robot manipulator. Then, according to the current and expected pose of the object, the spherical linear interpolation (Slerp) algorithm is used to generate a series of movements in the workspace for object re-orientation on the robot manipulator. In addition, the pose of the object is adjusted on the z-axis of the object's geodetic coordinate system based on the image features on the surface of the object, so that the pose of the placed object can approach the desired pose. Finally, a robot manipulator and a vacuum suction cup made by the laboratory are used to verify that the proposed system can indeed complete the planned task of object re-orientation.

https://www.mdpi.com/1424-8220/21/7/2280/pdf

Manipulation Planning for Object Re-Orientation Based on Semantic Segmentation Keypoint Detection

To make the human-robot collaboration effective, it may be necessary to provide robots with “senses” like vision and hearing. Task-oriented man-machine speech communication often relies on the use of abstract terms to describe objects. Therefore it is necessary to correctly map those terms into images of proper objects in a camera’s field of view. This paper presents the results of our research in this field. A novel method for contour identification, based on flexible editable contour templates (FECT), has been developed. We demonstrate that existing methods are not appropriate for this purpose because it is difficult to formulate general rules that humans employ to rank shapes into proper classes. Therefore, the rules for shape classification should be individually formulated by the users for each application. Our aim was to create appropriate tool facilitating formulation of those rules as it could potentially be a very labor-intensive task. The core of our solution is FCD (flexible contour description) format for description of flexible templates. Users will be able to create and edit flexible contour templates, and thus, adjust image recognition systems to their needs, in order to provide task-oriented communication between humans and robots.

https://www.mdpi.com/1424-8220/20/6/1773/pdf

Object Identification for Task-Oriented Communication with Industrial Robots.

This paper proposes a novel online object-packing system which can measure the dimensions of every incoming object and calculate its desired position in a given container. Existing object-packing systems have the limitations of requiring the exact information of objects in advance or assuming them as boxes. Thus, this paper is mainly focused on the following two points: (1) Real-time calculation of the dimensions and orientation of an object; (2) Online optimization of the object’s position in a container. The dimensions and orientation of the object are obtained using an RGB-D sensor when the object is picked by a manipulator and moved over a certain position. The optimal position of the object is calculated by recognizing the container’s available space using another RGB-D sensor and minimizing the cost function that is formulated by the available space information and the optimization criteria inspired by the way people place things. The experimental results show that the proposed system successfully places the incoming various shaped objects in their proper positions.

Smart Pack: Online Autonomous Object-Packing System Using RGB-D Sensor Data.

Dexterous manipulation, especially dexterous grasping, is a primitive and crucial ability of robots that allows the implementation of performing human-like behaviors. Deploying the ability on robots enables them to assist and substitute human to accomplish more complex tasks in daily life and industrial production. A comprehensive review of the methods based on point cloud and deep learning for robotics dexterous grasping from three perspectives is given in this paper. As a new category schemes of the mainstream methods, the proposed generation-evaluation framework is the core concept of the classification. The other two classifications based on learning modes and applications are also briefly described afterwards. This review aims to afford a guideline for robotics dexterous grasping researchers and developers.

/pdf/robotics-dexterous-grasping-the-methods-based-on-point-cloud-21wyj8g9ll.pdf

Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning.

Bin-picking of small parcels and other textureless planar-faced objects is a common task at warehouses. A general color image-based vision-guided robot picking system requires feature extraction and goal image preparation of various objects. However, feature extraction for goal image matching is difficult for textureless objects. Further, prior preparation of huge numbers of goal images is impractical at a warehouse. In this paper, we propose a novel depth image-based vision-guided robot bin-picking system for textureless planar-faced objects. Our method uses a deep convolutional neural network (DCNN) model that is trained on 15,000 annotated depth images synthetically generated in a physics simulator to directly predict grasp points without object segmentation. Unlike previous studies that predicted grasp points for a robot suction hand with only one vacuum cup, our DCNN also predicts optimal grasp patterns for a hand with two vacuum cups (left cup on, right cup on, or both cups on). Further, we propose a surface feature descriptor to extract surface features (center position and normal) and refine the predicted grasp point position, removing the need for texture features for vision-guided robot control and sim-to-real modification for DCNN model training. Experimental results demonstrate the efficiency of our system, namely that a robot with 7 degrees of freedom can pick randomly posed textureless boxes in a cluttered environment with a 97.5% success rate at speeds exceeding 1000 pieces per hour.

/pdf/depth-image-based-deep-learning-of-grasp-planning-for-3qu16j8ab0.pdf

Depth Image-Based Deep Learning of Grasp Planning for Textureless Planar-Faced Objects in Vision-Guided Robotic Bin-Picking.

Deep learning has been widely used for inferring robust grasps. Although human-labeled RGB-D datasets were initially used to learn grasp configurations, preparation of this kind of large dataset is expensive. To address this problem, images were generated by a physical simulator, and a physically inspired model (e.g., a contact model between a suction vacuum cup and object) was used as a grasp quality evaluation metric to annotate the synthesized images. However, this kind of contact model is complicated and requires parameter identification by experiments to ensure real world performance. In addition, previous studies have not considered manipulator reachability such as when a grasp configuration with high grasp quality is unable to reach the target due to collisions or the physical limitations of the robot. In this study, we propose an intuitive geometric analytic-based grasp quality evaluation metric. We further incorporate a reachability evaluation metric. We annotate the pixel-wise grasp quality and reachability by the proposed evaluation metric on synthesized images in a simulator to train an auto-encoder--decoder called suction graspability U-Net++ (SG-U-Net++). Experiment results show that our intuitive grasp quality evaluation metric is competitive with a physically-inspired metric. Learning the reachability helps to reduce motion planning computation time by removing obviously unreachable candidates. The system achieves an overall picking speed of 560 PPH (pieces per hour).

Learning suction graspability considering grasp quality and robot reachability for bin-picking.

An operating system according to an embodiment includes a target surface detection unit, a position calculation unit, a direction calculation unit, and a movement control unit. The target surface detection unit detects a target surface of a target object from a depth image obtained by a depth sensor. The position calculation unit calculates a first position for the detected target surface. The direction calculation unit calculates a first direction for the detected target surface. The movement control unit controls an actuator so as to reduce a positional deviation between a second position fixed with respect to the movable member and the first position and to reduce a directional deviation between a second direction fixed with respect to the movable member and the first direction.

Ping Jiang

Papers

Depth Image-Based Deep Learning of Grasp Planning for Textureless Planar-Faced Objects in Vision-Guided Robotic Bin-Picking.

Learning suction graspability considering grasp quality and robot reachability for bin-picking.

Operating system, control device, and computer program product