Real-time simulation of construction workers using combined human body and hand tracking for robotic construction worker system

Construction is an inherently less safe sector than other sectors because it exposes workers to harsh and dangerous working environments. The nature of the construction industry results in a comparatively high incidence of serious injuries and death caused by falls from a height, musculoskeletal disorders and being struck by objects. This paper presents a new concept that can tackle this problem in the future. The central hypothesis of this study is that it is possible to eliminate injuries if we move the human construction worker off-site and remotely link his/her motions to a Robotic Construction Worker (RCW) on-site. As a first steppingstone towards this ultimate goal, two systems essential for the RCW were developed in this study. First, a novel system that combines 3D body and hand position tracking was developed to capture the movements of human construction worker. This combination of tracking enables the capture of changes in the orientations and articulations of the entire human body. Second, a real-time simulation system that connects a human construction worker off-site to a virtual RCW was developed to demonstrate the proposed concept in a variety of construction scenarios. The simulation results demonstrate the future viability of the RCW concept and indicate the promise of this system for eliminating the health and safety risks faced by human construction workers

High-precision grasping and placing for mobile robots

This work presents a manipulation system for multiple labware in life science laboratories using the H20 mobile robots. The H20 robot is equipped with the Kinect V2 sensor to identify and estimate the position of the required labware on the workbench. The local features recognition based on SURF algorithm is used. The recognition process is performed for the labware to be grasped and for the workbench holder. Different grippers and labware containers are designed to manipulate different weights of labware and to realize a safe transportation

Learning to grasp in unstructured environments with deep convolutional neural networks using a Baxter Research Robot

Recent advancements in Deep Learning have accelerated the capabilities of robotic systems in terms of visual perception, object manipulation, automated navigation, and human-robot collaboration. The capability of a robotic system to manipulate objects in unstructured environments is becoming an increasingly necessary skill. Due to the dynamic nature of these environments, traditional methods, that require expert human knowledge, fail to adapt automatically. After reviewing the relevant literature a method was proposed to utilise deep transfer learning techniques to detect object grasps from coloured depth images. A grasp describes how a robotic end-effector can be arranged to securely grasp an object and successfully lift it without slippage. In this study, a ResNet-50 convolutional neural network (CNN) model is trained on the Cornell grasp dataset. The training was completed within 30 hours using a workstation PC with accelerated GPU support via an NVIDIA Titan X. The trained grasp detection model was further evaluated with a Baxter research robot and a Microsoft Kinect-v2 and a successful grasp detection accuracy of 93.91% was achieved on a diverse set of novel objects. Physical grasping trials were conducted on a set of 8 different objects. The overall system achieves an average grasp success rate of 65.0% while performing the grasp detection in under 25 milliseconds. The results analysis concluded that the objects with reasonably straight edges and moderately pronounced heights above the table are easily detected and grasped by the system

Autonomous Grasping of 3-D Objects by a Vision-Actuated Robot Arm using Brain-Computer Interface

A major drawback of a Brain–Computer Interface-based robotic manipulation is the complex trajectory planning of the robot arm to be carried out by the user for reaching and grasping an object. The present paper proposes an intelligent solution to the existing problem by incorporating a novel Convolutional Neural Network (CNN)-based grasp detection network that enables the robot to reach and grasp the desired object (including overlapping objects) autonomously using a RGB-D camera. This network uses a simultaneous object and grasp detection to affiliate each estimated grasp with its corresponding object. The subject uses motor imagery brain signals to control the pan and tilt angle of a RGB-D camera mounted on a robot link to bring the desired object inside its Field-of-view presented through a display screen while the objects appearing on the screen are selected using the P300 brain pattern. The robot uses inverse kinematics along with the RGB-D camera information to autonomously reach the selected object and the object is grasped using proposed grasping strategy. The overall BCI system outperforms other comparative systems involving manual trajectory planning significantly. The overall accuracy, steady-state error, and settling time of the proposed system are 93.4%, 0.05%, and 15.92 s, respectively. The system also shows a significant reduction of the workload of the operating subjects in comparison to manual trajectory planning based approaches for reaching and grasping

Computer Vision-based Robotic Arm for Object Color, Shape, and Size Detection

Various aspects of the human workplace have been influenced by robotics due to its precision and accessibility. Nowadays, industrial activities have become more automated, increasing efficiency while reducing the production time, human labor, and risks involved. With time, electronic technology has advanced, and the ultimate goal of such technological advances is to make robotic systems as human-like as possible. As a result of this blessing of technological advances, robots will perform jobs far more efficiently than humans in challenging situations. In this paper, an automatic computer vision-based robotic gripper has been built that can select and arrange objects to complete various tasks. This study utilizes the image processing methodology of the PixyCMU camera sensor to distinguish multiple objects according to their distinct colors (red, yellow, and green). Next, a preprogrammed command is generated in the robotic arm to pick the item employing Arduino Mega and four MG996R servo motors. Finally, the device releases the object according to its color behind the fixed positions of the robotic arm to a specific place. The proposed system can also detect objects' geometrical shapes (circle, triangle, square, rectangle, pentagon, and star) and sizes (large, medium, and small) by utilizing OpenCV image processing libraries in Python language. Empirical results demonstrate that the designed robotic arm detects colored objects with 80% accuracy. It performs an excellent size and shapes recognition precision in real-time with 100% accuracy