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In human and robot collaborative hybrid assembly cell as we proposed, it is important to develop automatic subtask allocation strategy for human and robot in usage of their advantages. We introduce a folk-joint task model that describes the sequential and parallel features and logic restriction of human and robot collaboration appropriately. To preserve a cost-effectiveness level of task allocation, we develop a logic mathematic method to quantitatively describe this discrete-event system by considering the system tradeoff between the assembly time cost and payment cost. A genetic based revolutionary algorithm is developed for real-time and reliable subtask allocation to meet the required cost-effectiveness. This task allocation strategy is built for a human worker and collaborates with various robot co-workers to meet the small production situation in future. The performance of proposed algorithm is experimentally studied, and the cost-effectiveness is analyzed comparatively on an electronic assembly case.Note to Practitioners-This paper was motivated by the critical demand within the manufacturing industry to meet the High-Mix, Low-Volume requirements for the changing consumer market demands. A fully robotic manufacturing process cannot obtain sufficient flexibility with a highly variable product line. Therefore, it must aim towards a complimentary cost-effectiveness to improve productivity from other ways. By taking advantage of a human's adaptability and flexibility, we can exploit the concept of a hybrid assembly system for medium sized manufacturing processes. Hybrid assembly creates a modern assembly mode where the robot works as co-worker to collaborate with the human and share the same working space and time. Hybrid assembly cell emphasizes two challenging issues to somehow improve the manufacturing productivity: (1) the way of describing and modeling human and robot collaboration and coordination, and (2) the effective task scheduling and allocation strategy for human and robot. This research is focused on the subtask allocation method while considering the features of human and robot collaboration. The original contribution of this work is the design of an offline and online resource constraint project scheduling problem (RCPSP) algorithm for hybrid assembly systems. The resource is not only limited to the recycle resource, but also extend to the features of human and robot, such as human fatigue issues, and robot assembly failure issues. This RCPSP for hybrid assembly is to realize both sequential and parallel task scheduling between human and several robots while minimizing the assembly time and payment Manuscript cost. This algorithm is fast in reaching the semi-optimal solution, therefore it can be used for both offline and online situations. The simulation results demonstrates the effectiveness of this task scheduling algorithms. We believe this study is helpful to improve the productivity for hybrid assembly system. Index Terms-Genetic algorithm, human and robot collaboration, hybrid assembly sys...

Exoskeleton robots demonstrate promise in their application in assisting or enhancing human physical capacity. Joint muscular torques (JMT) reflect human effort, which can be applied on an exoskeleton robot to realize an active power-assist function. The estimation of human JMT with a wearable exoskeleton is challenging. This paper proposed a novel human lower limb JMT estimation method based on the inverse dynamics of the human body. The method has two main parts: the inverse dynamic approach (IDA) and the sensing system. We solve the inverse dynamics of each human leg separately to shorten the serial chain and reduce computational complexity, and divide the JMT into the mass-induced one and the foot-contact-force (FCF)-induced one to avoid switching the dynamic equation due to different contact states of the feet. An exoskeleton embedded sensing system is designed to obtain the user’s motion data and FCF required by the IDA by mapping motion information from the exoskeleton to the human body. Compared with the popular electromyography (EMG) and wearable sensor based solutions, electrodes, sensors, and complex wiring on the human body are eliminated to improve wearing convenience. A comparison experiment shows that this method produces close output to a motion analysis system with different subjects in different motion.

Gesture recognition is essential for human and robot collaboration. Within an industrial hybrid assembly cell, the performance of such a system significantly affects the safety of human workers. This work presents an approach to recognizing hand gestures accurately during an assembly task while in collaboration with a robot co-worker. We have designed and developed a sensor system for measuring natural human-robot interactions. The position and rotation information of a human worker's hands and fingertips are tracked in 3D space while completing a task. A modified chain-code method is proposed to describe the motion trajectory of the measured hands and fingertips. The Hidden Markov Model (HMM) method is adopted to recognize patterns via data streams and identify workers' gesture patterns and assembly intentions. The effectiveness of the proposed system is verified by experimental results. The outcome demonstrates that the proposed system is able to automatically segment the data streams and recognize the gesture patterns thus represented with a reasonable accuracy ratio.