Hybrid Collaborative Stereo Vision System for Mobile Robots Formation

Abstract

For collaborative robots to become useful, end users who are not robotics experts must be able to instruct them to perform a variety of tasks. With this goal in mind, we developed a system for end-user creation of robust task plans with a broad range of capabilities. CoSTAR: the Collaborative System for Task Automation and Recognition is our winning entry in the 2016 KUKA Innovation Award competition at the Hannover Messe trade show, which this year focused on Flexible Manufacturing. CoSTAR is unique in how it creates natural abstractions that use perception to represent the world in a way users can both understand and utilize to author capable and robust task plans. Our Behavior Tree-based task editor integrates high-level information from known object segmentation and pose estimation with spatial reasoning and robot actions to create robust task plans. We describe the cross-platform design and implementation of this system on multiple industrial robots and evaluate its suitability for a wide variety of use cases.

Structure from motion using a hybrid stereo-vision system

“…In [7], the authors propose to use a catadioptric sensor and a perspective camera for obsta-cle detection. For multi-robot collaboration, Roberti et al [8] propose an original approach to compute the structure and the motion from multiple robots equipped with different types of camera. Eynard et al [2] also take advantage of this setup in order to estimate both the attitude and the altitude of a UAV using a dense plane sweeping based registration.…”

Case study: Performance analysis and development of robotized screwing application with integrated vision sensing system for automotive industry

“…Modern advanced sensing systems based on "smart camera sensors" have been quickly spread into many fields of industry, such as general process control, 4 general object identification and recognition, 5 quality check (Stejskal, Bayraktar, Alghamdi), [5][6][7] reading texts and codes (e.g. car identification plate), 8,9 face recognition (Park), 10 robot control via gestures 10 and general pose control of robots using visual servoing, 3 visual navigation of single/multiple mobile robot/s (Kim, Ostertag, Mikulova, Roberti), [11][12][13][14] collision detection and perception (Lehnhardt), 15 many applications in food industry, and online process control and monitoring (Kosinar) 16 using component recognition (Stipancic, Scholz-Reiter). 17,18 Smart sensors are key elements for visual inspection of parts or visual navigation of robots for assembly tasks that has been manually done by human operators before.…”

Formation Control of Robotic Swarm Using Bounded Artificial Forces

“…With advances in communication, networking, and computing, robotic swarms are becoming more affordable in many given tasks that are too complex to be achieved by a single robot working alone. So far the possible applications range from coordinated control of UAVs [ 1 – 4 ] to spacecraft [ 5 – 8 ] and automatic vehicles [ 9 , 10 ], and so forth.…”