3D vision-guided pick-and-place using kuka LBR iiwa robot

This paper presents the development of a control system for vision-guided pick-and-place tasks using a robot arm equipped with a 3D camera. The main steps include camera intrinsic and extrinsic calibration, hand-eye calibration, initial object pose registration, objects pose alignment algorithm, and pick-and-place execution. The proposed system allows the robot be able to pick and place object with limited times of registering a new object and the developed software can be applied for new object scenario quickly. The integrated system was tested using the hardware combination of kuka iiwa, Robotiq grippers (two finger gripper and three finger gripper) and 3D cameras (Intel real sense D415 camera, Intel real sense D435 camera, Microsoft Kinect V2). The whole system can also be modified for the combination of other robotic arm, gripper and 3D camera

MODELING OF MOBILE ROBOT WITH OBSTACLE AVOIDANCE USING FUZZY CONTROLLER

This paper presents the modeling of a robot's navigation using ultrasonic sensors under uncertainty. The robot tries to avoid obstacles by using the fuzzy logic controller to process the data coming from three ultrasonic sensors. To assess the performance of fuzzy logic optimized robot navigation controller with ultrasonic sensors, which measure the distance by calculating the time spent on the object and its return, the obstacles are placed in front of, left, and right of the robot.  Mamdani fuzzy reasoning system is used for the designed controller for its intuitive properties and fewer setting parameters which reduces the amount of time spent on the programming of the controller. 25 rules are considered to cover a robot’s possible interactions with obstacles. For an easy understanding of navigation architecture and rapid algorithm implementation, in this paper, a MATLAB simulation framework is developed. MATLAB/Simulink is one of the best simulation tools required to design the architecture and verify algorithms with real-time constraints. Resultant models of the fuzzy optimized controller demonstrate the superior performance of the fuzzy logic controller with high adaptability to the environment while maintaining a sufficient level of accuracy. The designed fuzzy controller can be used in microprocessor/microcontroller-based robots owing to easiness in implementation and coding

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

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