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  1. Industry 4.0 projects ubiquitous collaborative robots in smart factories of the future, particularly in assembly and material handling. To ensure efficient and safe human-robot collaborative interactions, this paper presents a novel algorithm for estimating Risk of Passage (ROP) a robot incurs by passing between dynamic obstacles (humans, moving equipment, etc.). This paper posits that robot trajectory durations will be shorter and safer if the robot can react proactively to predicted collision between a robot and human worker before it occurs, compared to reacting when it is imminent. I.e., if the risk that obstacles may prohibit robot passage at a future time in the robot’s trajectory is greater than a user defined risk limit, then an Obstacle Pair Volume (OPV), encompassing the obstacles at that time, is added to the planning scene. Results found from simulation show that an ROP algorithm can be trained in ∼120 workcell cycles. Further, it is demonstrated that when a trained ROP algorithm introduces an OPV, trajectory durations are shorter compared to those avoiding obstacles without the introduction of an OPV. The use of ROP estimation with addition of OPV allows workcells to operate proactively smoother with shorter cycle times in the presence of unforeseen obstacles.
  2. Abstract To enable safe and effective human–robot collaboration (HRC) in smart manufacturing, seamless integration of sensing, cognition, and prediction into the robot controller is critical for real-time awareness, response, and communication inside a heterogeneous environment (robots, humans, and equipment). The specific research objective is to provide the robot Proactive Adaptive Collaboration Intelligence (PACI) and switching logic within its control architecture in order to give the robot the ability to optimally and dynamically adapt its motions, given a priori knowledge and predefined execution plans for its assigned tasks. The challenge lies in augmenting the robot’s decision-making process to have greater situation awareness and to yield smart robot behaviors/reactions when subject to different levels of human–robot interaction, while maintaining safety and production efficiency. Robot reactive behaviors were achieved via cost function-based switching logic activating the best suited high-level controller. The PACI’s underlying segmentation and switching logic framework is demonstrated to yield a high degree of modularity and flexibility. The performance of the developed control structure subjected to different levels of human–robot interactions was validated in a simulated environment. Open-loop commands were sent to the physical e.DO robot to demonstrate how the proposed framework would behave in a real application.
  3. To enable safe and effective human-robot collaboration (HRC) in smart manufacturing, seamless integration of sensing, cognition and prediction into the robot controller is critical for real-time awareness, response and communication inside a heterogeneous environment (robots, humans, equipment). The specific research objective is to provide the robot Proactive Adaptive Collaboration Intelligence (PACI) and switching logic within its control architecture in order to give the robot the ability to optimally and dynamically adapt its motions, given a priori knowledge and predefined execution plans for its assigned tasks. The challenge lies in augmenting the robot’s decision-making process to have greater situation awareness and to yield smart robot behaviors/reactions when subject to different levels of human-robot interaction, while maintaining safety and production efficiency. Robot reactive behaviors were achieved via cost function-based switching logic activating the best suited high-level controller. The PACI’s underlying segmentation and switching logic framework is demonstrated to yield a high degree of modularity and flexibility. The performance of the developed control structure subjected to different levels of human-robot interactions was validated in a simulated environment. Open-loop commands were sent to the physical e.DO robot to demonstrate how the proposed framework would behave in a real application.