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1. A Spatio-Temporal Prediction and Planning Framework for Proactive Human–Robot Collaboration

   https://doi.org/10.1115/1.4063502  

   Flowers, Jared; Wiens, Gloria (December 2023, Journal of Manufacturing Science and Engineering)

   Pai Zheng (Ed.)

   Abstract A significant challenge in human–robot collaboration (HRC) is coordinating robot and human motions. Discoordination can lead to production delays and human discomfort. Prior works seek coordination by planning robot paths that consider humans or their anticipated occupancy as static obstacles, making them nearsighted and prone to entrapment by human motion. This work presents the spatio-temporal avoidance of predictions-prediction and planning framework (STAP-PPF) to improve robot–human coordination in HRC. STAP-PPF predicts multi-step human motion sequences based on the locations of objects the human manipulates. STAP-PPF then proactively determines time-optimal robot paths considering predicted human motion and robot speed restrictions anticipated according to the ISO15066 speed and separation monitoring (SSM) mode. When executing robot paths, STAP-PPF continuously updates human motion predictions. In real-time, STAP-PPF warps the robot’s path to account for continuously updated human motion predictions and updated SSM effects to mitigate delays and human discomfort. Results show the STAP-PPF generates robot trajectories of shorter duration. STAP-PPF robot trajectories also adapted better to real-time human motion deviation. STAP-PPF robot trajectories also maintain greater robot/human separation throughout tasks requiring close human–robot interaction. Tests with an assembly sequence demonstrate STAP-PPF’s ability to predict multi-step human tasks and plan robot motions for the sequence. STAP-PPF also most accurately estimates robot trajectory durations, within 30% of actual, which can be used to adapt the robot sequencing to minimize disruption. 

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2. Human-Robot Collaboration: A Predictive Collision Detection Approach for Operation Within Dynamic Environments

   https://doi.org/10.1115/ISFA2020-9659  

   Streitmatter, G.L.; Wiens, G.J (July 2020, ASME International Symposium on Flexible Automation Conference)

   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. 

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3. Transferable Two-Stream Convolutional Neural Network for Human Action Recognition

   Xiong, Q. (January 2020, 48th North American Manufacturing Research Conference)

   Human-Robot Collaboration (HRC), which envisions a workspace in which human and robot can dynamically collaborate, has been identified as a key element in smart manufacturing. Human action recognition plays a key role in the realization of HRC as it helps identify current human action and provides the basis for future action prediction and robot planning. Despite recent development of Deep Learning (DL) that has demonstrated great potential in advancing human action recognition, one of the key issues remains as how to effectively leverage the temporal information of human motion to improve the performance of action recognition. Furthermore, large volume of training data is often difficult to obtain due to manufacturing constraints, which poses challenge for the optimization of DL models. This paper presents an integrated method based on optical flow and convolutional neural network (CNN)-based transfer learning to tackle these two issues. First, optical flow images, which encode the temporal information of human motion, are extracted and serve as the input to a two-stream CNN structure for simultaneous parsing of spatial-temporal information of human motion. Then, transfer learning is investigated to transfer the feature extraction capability of a pretrained CNN to manufacturing scenarios. Evaluation using engine block assembly confirmed the effectiveness of the developed method. 

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4. Prediction of Human Reaching Pose Sequences In Human-Robot Collaboration

   Flowers, Jared T.; Wiens, Gloria J. (August 2023, ASME 2023 International Design Engineering Technical Conferences and Computers Information in Engineering Conference (IDETC-CIE2023))

   In Human-Robot Collaboration (HRC), robots and humans must work together in shared, overlapping, workspaces to accomplish tasks. If human and robot motion can be coordinated, then collisions between robot and human can seamlessly be avoided without requiring either of them to stop work. A key part of this coordination is anticipating humans’ future motion so robot motion can be adapted proactively. In this work, a generative neural network predicts a multi-step sequence of human poses for tabletop reaching motions. The multi-step sequence is mapped to a time-series based on a human speed versus motion distance model. The input to the network is the human’s reaching target relative to current pelvis location combined with current human pose. A dataset was generated of human motions to reach various positions on or above the table in front of the human starting from a wide variety of initial human poses. After training the network, experiments showed that the predicted sequences generated by this method matched the actual recordings of human motion within an L2 joint error of 7.6 cm and L2 link roll-pitch-yaw error of 0.301 radians on average. This method predicts motion for an entire reach motion without suffering from the exponential propagation of prediction error that limits the horizon of prior works. 

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5. Human–Robot Collaboration in Smart Manufacturing: Robot Reactive Behavior Intelligence

   https://doi.org/10.1115/1.4048950  

   Nicora, Matteo Lavit; Ambrosetti, Roberto; Wiens, Gloria J.; Fassi, Irene (March 2021, Journal of Manufacturing Science and Engineering)

   null (Ed.)

   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. 

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