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Programming by Demonstration (PbD) is a method to program robots through the performance of a task by humans. Most implementations are online methods that use visual or force feedback of the demonstrator. However, we developed an offline programming approach for PbD with a special input device within an Augmented Reality Environment. Therefore, this paper aims to answer how the characteristics and functionality of the end effector of a jointed-arm robot can be represented by a haptic input device in order to perform PbD. The PbD process is first carried out on a digital twin of the robot, visualized to the user in real physical space by means of augmented reality technology. The programming of the digital twin can later be transferred to the real robot. The haptic input device in this context is the main part of the Cyber-Physical-System (CPS), which enables the user to interact with the virtual robot. Therefore, the specification of the mechanical and software components of the CPS is of main importance. Within this paper, strategies for the implementation of shape and function abstraction, as well as for ensuring communication, have been worked out. The physical shape of the CPS is kept generic and is only subject to ergonomic restrictions. However, Augmented Reality overlays the physical shape with an exact digital image of the end effector used later in the process. Nevertheless, the physical characteristics of the real robot should be represented as real as possible by the CPS. Therefore, the CPS is equipped with various sensors and actuators. With the CPS it is possible to determine contact forces and to manipulate objects to a certain extent in order to teach gripping strategies to the digital twin. An operating system was developed for communication and control of the electronic components. For the validation of the functionality of the CPS an exemplary PbD process was developed, the results were analyzed and evaluated.
The paper presents a novel offline programming (OLP) method based on programming by demonstration (PbD), which has been validated through user study. PbD is a programming method that involves physical interaction with robots, and kinesthetic teaching (KT) is a commonly used online programming method in industry. However, online programming methods consume significant robot resources, limiting the speed advantages of PbD and emphasizing the need for an offline approach. The method presented here, based on KT, uses a virtual representation instead of a physical robot, allowing independent programming regardless of the working environment. It employs haptic input devices to teach a simulated robot in augmented reality and uses automatic path planning. A benchmarking test was conducted to standardize equipment, procedures, and evaluation techniques to compare different PbD approaches. The results indicate a 47% decrease in programming time when compared to traditional KT methods in established industrial systems. Although the accuracy is not yet at the level of industrial systems, users have shown rapid improvement, confirming the learnability of the system. User feedback on the perceived workload and the ease of use was positive. In conclusion, this method has potential for industrial use due to its learnability, reduction in robot downtime, and applicability across different robot sizes and types.
The main idea of this paper is to present a framework for an easy and intuitive program generation for human-robot collaboration implementations. This framework consists of three key ideas, which make up the three main sections. The first section is about automated task allocation with focus on economics. The second part is about intuitive robotic teaching with an offline motion capture (MoCap) tool and the third section features an automated program generation of the paths and the human-robot collaborative aspects of the application. With this framework, we provide a method to program human-robot collaborative (HRC) applications for future industry purposes.
