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  1. This paper develops a predictive collision detection algorithm for enhancing safety while respecting productivity in a Human Robot Collaborative (HRC) setting that operates on outputs from a Computer Vision (CV) environmental monitor. This prediction can trigger reactive and proactive robot action. The algorithm is designed to address two key challenges: 1) outputs from CV techniques are often highly noisy and incomplete due to occlusions and other factors, and 2) human tracking CV approaches typically provide a minimal set of points on the human. This noisy set of points must be augmented to define a high-fidelity model of the human’s predicted spatial and temporal occupancy. A filter is applied to decrease sensitivity of the algorithm to errors in the CV predictions. Kinematics of the human are leveraged to infer a full model of the human from a set of, at most, 18 points, and transform them into a point cloud occupying the swept volume of the human’s motion. This form can then quickly be compared with a compatible robot model for collision detection. Timed tests show that creation of human and robot models, and the subsequent collision check occurs in less than 30 ms on average, making this algorithm real-time capable. 
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  2. null (Ed.)
    As demands on manufacturing rapidly evolve, flexible manufacturing is becoming more essential for acquiring the necessary productivity to remain competitive. An innovative approach to flexible manufacturing is the introduction of fenceless robotic manufacturing cells to acquire and leverage greater human-robot collaboration (HRC). This involves operations in which a human and a robot share a space, complete tasks together, and interact with each other. Such operations, however, pose serious safety concerns. Before HRC can become a viable possibility, robots must be capable of safely operating within and responding to events in dynamic environments. Furthermore, the robot must be able to do this quickly during online operation. This paper outlines an algorithm for predictive collision detection. This algorithm gives the robot the ability to look ahead at its trajectory, and the trajectories of other bodies in its environment and predict potential collisions. The algorithm approximates a continuous swept volume of any articulated body along its trajectory by taking only a few time sequential samples of the predicted orientations of the body and creating surfaces that patch the orientations together with Coons patches. Run time data collected on this algorithm suggest that the algorithm can accurately predict future collisions in under 30 ms. 
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