Software upgrade for automatic rough milling technology design for parts with free form surfaces

Use of free form surfaces is present in almost all area of everyday living. In mechanical engineering such products are usually called parts with free form surfaces. Today is very easy to design such parts using some of commercial CAD software package. Unlike designing, production of those parts are more difficult. There are many strategies to machine parts with free form surfaces. The most used is milling method with ball mill cutter. In previous, at the Department for Production Engineering at the Faculty of Mechanical Engineering at Belgrade many research were conducted in the field of free form surface milling. Recently, the software for automatic technology design for parts with free form surfaces was developed. In this paper is presented developed procedure for software upgrade with new procedure for rough machining with milling head and ball end mill cutter

Object Detection and Tracking in Cooperative Multi-Robot Transportation

Contemporary manufacturing systems imply the utilization of autonomous robotic systems, mainly for the execution of manipulation and transportation tasks. With a goal to reduce transportation and manipulation time, improve efficiency, and achieve flexibility of intelligent manufacturing systems, two or more intelligent mobile robots can be exploited. Such multi-robot systems require coordination and some level of communication between heterogeneous or homogeneous robotic systems. In this paper, we propose the utilization of two heterogeneous robotic systems, original intelligent mobile robots RAICO (Robot with Artificial Intelligence based COgnition) and DOMINO (Deep learning-based Omnidirectional Mobile robot with Intelligent cOntrol), for transportation tasks within a laboratory model of a manufacturing environment. In order to reach an adequate cooperation level and avoid collision while moving along predefined paths, our own developed intelligent mobile robots RAICO and DOMINO will communicate their current poses, and object detection and tracking system is developed. A stereo vision system equipped with two parallelly placed industrial-grade cameras is used for image acquisition, while convolutional neural networks are utilized for object detection, classification, and tracking. The proposed object detection and tracking system enables real-time tracking of another mobile robot within the same manufacturing environment. Furthermore, continuous information about mobile robot poses and the size of the bounding box generated by the convolutional neural network in the process of detection of another mobile robot is used for estimation of object movement and collision avoidance. Mobile robot localization through time is performed based on kinematic models of two intelligent mobile robots, and conducted experiments within a laboratory model of manufacturing environment confirm the applicability of the proposed framework for object detection and collision avoidance