Programming industrial robots is not very intuitive, and the programmer has to be a domain expert for e.g. welding and programming to know how the task is optimally executed. For SMEs such employees are not affordable, nor cost-effective. Therefore a new system is needed where domain experts from a specific area, like welding or assembly, can easily program a robot without knowing anything about programming languages or how to use TeachPads. Such a system needs to be flexible to adapt to new tasks and functions. These requirements can be met by using a task based programming approach where the robot program is built up using a hierarchical structure of process, tasks and skills. It also needs to be intuitive so that domain experts don't need much training time on handling the system. Intuitive interaction is achieved by using different input and output modalities like gesture input, speech input, or touch input which are suitable for the current task.
This master thesis focuses on the implementation of a user interface (GUI) for task based industrial robot programming and evaluates different input modalities (gesture, speech, touch, pen input) for the interaction with the system. The evaluation is based on a user study conducted with 30 participants as a Wizard-Of-Oz experiment, where non expert users had to program assembly and welding tasks to an industrial robot, using the previously developed GUI and various input and output modalities.
The findings of the task analysis and user study are then used for creating a semantic description which will be used in the cognitive robotics-worker cell for automatically inferring required system components, and to provide the best suited input modality.
Hiding task-oriented programming complexity: an industrial case study
Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy（意大利国家研究委员会智能工业技术与先进制造研究所）；University of Brescia（布雷西亚大学）
PRISM: Precision and contact-rich Real-world Industrial Skill dataset with Multimodal sensing
State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院通用人工智能国家重点实验室）；Delta Intelligence（三角洲智能公司）；PKU-Wuhan Institute for Artificial Intelligence（北京大学武汉人工智能研究院）；Hubei Humanoid Robot Innovation Center Co., Ltd.（湖北人形机器人创新中心有限公司）；China Academy of Information and Communications Technology（中国信息通信研究院）
Multimodal Benchmark for Safety Assessment in Industrial Inspection Scenarios
Department of Automation, Tsinghua University（清华大学自动化系）；Institute for Embodied Intelligence and Robotics, Tsinghua University（清华大学具身智能与机器人研究所）；TetraBOT Intelligence Co., Ltd.（天博智能科技有限公司）；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；School of Automation, Southeast University（东南大学自动化学院）
BANSAI: Towards Bridging the AI Adoption Gap in Industrial Robotics with Neurosymbolic Programming
ArtiMinds Robotics（阿蒂敏兹机器人公司）；Institute for Artificial Intelligence, University of Bremen（不来梅大学人工智能研究所）；wbk Institute of Production Science, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院生产科学wbk研究所）
IndEgo: A Dataset of Industrial Scenarios and Collaborative Work for Egocentric Assistants
Fraunhofer IPK（弗劳恩霍夫研究所）；Technical University of Berlin（柏林技术大学）；University of Tübingen（图宾根大学）；RWTH Aachen University（亚琛工业大学）；Leibniz University Hannover（汉诺威莱布尼茨大学）
