###### Abstract  
To enable safe and effective human-robot collaboration (HRC) in smart manufacturing, it is critical to seamlessly integrate sensing, cognition, and prediction into the robot controller for real-time awareness, response, and communication inside a heterogeneous environment (robots, humans, and equipment). The proposed approach takes advantage of the prediction capabilities of nonlinear model predictive control (NMPC) to execute safe path planning based on feedback from a vision system. To satisfy the requirements of real-time path planning, an embedded solver based on a penalty method is applied. However, due to tight sampling times, NMPC solutions are approximate; therefore, the safety of the system cannot be guaranteed. To address this, we formulate a novel safety-critical paradigm that uses an exponential control barrier function (ECBF) as a safety filter. Several common human-robot assembly subtasks have been integrated into a real-life HRC assembly task to validate the performance of the proposed controller and to investigate whether integrating human pose prediction can help with safe and efficient collaboration. The robot uses OptiTrack cameras for perception and dynamically generates collision-free trajectories to the predicted target interactive position. Results for a number of different configurations confirm the efficiency of the proposed motion planning and execution framework, with a 23.2% reduction in execution time achieved for the HRC task compared to an implementation without human motion prediction. human-centric manufacturing; embedded path planning; safe human-robot collaboration; safety-critical control 2020 IEEE Conference on Robotics and Automation (ICRA), Vol. 10, No. 1, pp. 1-10, 2010. IEEE. Personal notice on all other things is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, no. 100 or more, no. 000100, p. 100010, p.  
dynamics. To activate a robot at the proper time, the estimated classification category can be used as a prior condition for motion prediction. Motion prediction is concerned with forecasting future body movements and poses based on observations of past movements and can be used to improve the collaboration efficiency in HRC [8]. Estimates of the future evolution of body poses can be used to define both the target position for the robot end-effector and the dynamic obstacles (body parts) for collision avoidance. In our work, an OptiTrack camera is used to achieve high-performance optical tracking combined with wearable devices to obtain a streaming skeletal representation of the human body. These data are then used as input to the task recognition and motion prediction modules. Safety regulation ISO 10218 [9] and technical specification ISO 15066 [10] must be adhered to if a robot is deployed in a working environment shared with a human. These define cooperative robot operation as either contact-type (e.g., power and force limiting (PFL), hand guiding (HG)) or non-contact-type (e.g., safety-rated monitored stop (SRMS), speed and separation monitoring (SSM)) interactions. In this work, we consider the design of a control framework for SSM-based non-contact HRC, which guarantees safety by remaining a safe distance from humans during interactions. A model predictive control (MPC)-based control system is employed to ensure compliance with safety standards while processing real-time feedback from a vision system. Given the dynamic and stochastic nature of HRC environments, which cannot be fully predicted a priori, MPC has gained increasing popularity in motion planning due to its ability to handle diverse kinematic and dynamic constraints [11; 12; 13]. The nonlinear model predictive control (NMPC) law is derived by solving an online nonlinear optimal control problem, incorporating a kinematic model. Previous work, such as [14], demonstrates the application of NMPC for task-constrained motion planning. For safe physical human-robot interaction (pHRI), ref.