Autonomous and hybrid plants require a high level of automation and understanding of the processes within where collaborative robots have a high potential. The development of collaborative robotics as a research area is based on the study of computer vision, machine learning and artificial intelligence to provide robots with high vision skills and knowledge to interact safely and effectively in the workplace and assist humans in arduous or repetitive tasks.
In this training session, the first of a series of training workshops, several perception techniques to increase collaborative robots’ knowledge and skills to perceive, comprehend and reason about the surrounding environment were presented. The concepts presented included several technologies and methodologies to enhance the robot ability for the detection of the objects and humans in the environment, identify humans’ posture and activities and understand the human behavior or model desired motions for our robots.
During the session, showcasing basic concepts on methods to enrich collaborative robotics recognition and perception capabilities, Sharework researchers aimed to contribute to training the next generation of Human-Robot Collaboration investigators and disseminating project advancements to the scientific and education community. The session content comprised basic concepts and techniques used to perform an environmental analysis and provide robots with the necessary intelligence to recognise their surroundings and act accordingly. The concepts presented were exemplified with comprehensive case studies developed under the framework of the Sharework project.