This paper shows a methodology for on-line recognition and classification of pieces in robotic assembly tasks and its application into an intelligent manufacturing cell. The performance of industrial robots working in...
详细信息
This paper shows a methodology for on-line recognition and classification of pieces in robotic assembly tasks and its application into an intelligent manufacturing cell. The performance of industrial robots working in unstructured environments can be improved using visual perception and learning techniques The object recognition is accomplished using a neuronal network with FuzzyARTMAP architecture for learning and recognition purposes, which receives a descriptor vector called CFD&POSE as the input. This vector represents an innovative methodology for classification and identification of pieces in robotic tasks, every single stage of the methodology, is described step by step and the proposed algorithms explained. The vector compresses 3D object data from assembly parts and is invariant to scale, rotation and orientation. The approach in combination with the fast learning capability of ART networks indicates the suitability for industrial robot applications as it is shown in experimental results and the possibility to add concatenated information into the descriptor vector to achieve a much more robust methodology.
Microscopic computervision differs significantly from macroscale computervision. Autofocusing techniques is of fundamental importance to automated micromanipulation in providing high level task understanding, task p...
详细信息
ISBN:
(纸本)9781424420575
Microscopic computervision differs significantly from macroscale computervision. Autofocusing techniques is of fundamental importance to automated micromanipulation in providing high level task understanding, task planning and real time control. Depth-from-Focus (DFF) based autofocusing method is widely used in many microscope systems, while the low efficiency limited its application in micromanipulation system which needs fast autofocus for real time control. In this paper, Depth-From-Defocus (DFD) algorithms are proposed to improve autofocusing performance and robustness for microscopic optics. Two different schemes based on look-up table and function relation are investigated and evaluated through actual experiments. Experimental results validate the performances of the two proposed autofocusing methods.
Collaborative robotics, in conjunction with artificial intelligence (AI), offers a contemporary and effective paradigm for secure machine-human interactions. This synergy branches out into a variety of industries, inc...
详细信息
ISBN:
(数字)9798350360165
ISBN:
(纸本)9798350360172
Collaborative robotics, in conjunction with artificial intelligence (AI), offers a contemporary and effective paradigm for secure machine-human interactions. This synergy branches out into a variety of industries, including education and entertainment, in addition to industrial uses. Two intriguing board games that offer a platform for examining the potential of cooperative robotic systems are chess and checkers. An intelligent and cooperative robotic system designed for use in Italian checkers games is described in this context by the study being presented. To record the game state, the gadget employs a camera. To physically move pieces across the board, a pick-and-place mechanism is used. An algorithm is used to automatically choose the optimal moves that comply with the rules. The system respects the kinematic restrictions of the manipulator while optimizing minimum-time trajectories live for every manipulation, guaranteeing a smooth and dynamic gaming experience. An experimental validation employing a seven-degree-of-freedom Franka Emika arm verifies the effectiveness of the proposed approach in real-world settings.
暂无评论