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.

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.

Robots and humans closely working together within dynamic environments must be able to continuously look ahead and identify potential collisions within their ever-changing environment. To enable the robot to act upon such situational awareness, its controller requires an iterative collision detection capability that will allow for computationally efficient Proactive Adaptive Collaboration Intelligence (PACI) to ensure safe interactions. In this paper, an algorithm is developed to evaluate a robot’s trajectory, evaluate the dynamic environment that the robot operates in, and predict collisions between the robot and dynamic obstacles in its environment. This algorithm takes as input the joint motion data of predefined robot execution plans and constructs a sweep of the robot’s instantaneous poses throughout time. The sweep models the trajectory as a point cloud containing all locations occupied by the robot and the time at which they will be occupied. To reduce the computational burden, Coons patches are leveraged to approximate the robot’s instantaneous poses. In parallel, the algorithm creates a similar sweep to model any human(s) and other obstacles being tracked in the operating environment. Overlaying temporal mapping of the sweeps reveals anticipated collisions that will occur if the robot-human do not proactively modify their motion. The algorithm is designed to feed into a segmentation and switching logic framework and provide real-time proactive-n-reactive behavior for different levels of human-robot interactions, while maintaining safety and production efficiency. To evaluate the predictive collision detection approach, multiple test cases are presented to quantify the computational speed and accuracy in predicting collisions.