Radar Based Target Tracking and Classification for Efficient Robot Speed Control in Fenceless Environments

Awareness of its surroundings is a crucial capability for a robot meant to be working alongside other robots or human operators. When considering safety norms and modalities, in particular the Speed and Separation Monitoring (SSM), proper proximity information can make the difference in the overall efficiency of a use case, for example avoiding unnecessary penalizations in the cycle-time. This paper presents a method to exploit the proximity perception capabilities of radar sensors to construct a continuous speed control algorithm for a UR10 robot. With respect to standard implementations of the SSM in industrial and collaborative environments, the proposed speed control is enhanced by the addition of direct human’s velocity measurement, full direction of travel and target classification. The results are evalauted according to the SSM metrics for safety and productivity, showing an overall increase in efficiency while still maintaining safety level requirements.

[1]  Hubert Zangl,et al.  Virtual Radar: Real-Time Millimeter-Wave Radar Sensor Simulation for Perception-Driven Robotics , 2021, IEEE Robotics and Automation Letters.

[2]  Ingmar Posner,et al.  Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[3]  Philipp Sommer,et al.  Machine Perception Platform for Safe Human-Robot Collaboration , 2019, 2019 IEEE SENSORS.

[4]  Michele Taragna,et al.  Kalman Filter Based Sensor Fusion for a Mobile Manipulator , 2019, Volume 5A: 43rd Mechanisms and Robotics Conference.

[5]  Andrew Markham,et al.  mID: Tracking and Identifying People with Millimeter Wave Radar , 2019, 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS).

[6]  Ferat Sahin,et al.  Speed and Separation Monitoring using On-Robot Time-of-Flight Laser-ranging Sensor Arrays , 2019, 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE).

[7]  Bong-seok Kim,et al.  A Low-Complexity FMCW Surveillance Radar Algorithm Using Two Random Beat Signals , 2019, Sensors.

[8]  Philipp Sommer,et al.  Radar Sensor for Fenceless Machine Guarding and Collaborative Robotics , 2018, 2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR).

[9]  Antonio Torralba,et al.  Through-Wall Human Pose Estimation Using Radio Signals , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[10]  Ruowen Ma,et al.  The simulation of human walking micro-Doppler echo and comparison of time-frequency analysis method , 2017, 2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS).

[11]  Terrence Fong,et al.  A Survey of Methods for Safe Human-Robot Interaction , 2017, Found. Trends Robotics.

[12]  Jeremy A Marvel,et al.  Implementing Speed and Separation Monitoring in Collaborative Robot Workcells. , 2017, Robotics and computer-integrated manufacturing.

[13]  Andrea Maria Zanchettin,et al.  Safety in human-robot collaborative manufacturing environments: Metrics and control , 2016, IEEE Transactions on Automation Science and Engineering.

[14]  Frédo Durand,et al.  Capturing the human figure through a wall , 2015, ACM Trans. Graph..

[15]  Lorenzo Molinari Tosatti,et al.  Trajectory-dependent safe distances in human-robot interaction , 2014, Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA).

[16]  Christoph Walter,et al.  A projection-based sensor system for safe physical human-robot collaboration , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  Jeremy A. Marvel,et al.  Performance Metrics of Speed and Separation Monitoring in Shared Workspaces , 2013, IEEE Transactions on Automation Science and Engineering.

[18]  Sandor S. Szabo,et al.  A Testbed for Evaluation of Speed and Separation Monitoring in a Human Robot Collaborative Environment , 2012 .

[19]  I. Bilik,et al.  Radar target classification using doppler signatures of human locomotion models , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[20]  Dennis Barrett,et al.  mmWave radar sensors in robotics applications , 2017 .

[21]  Sandeep Rao,et al.  The fundamentals of millimeter wave sensors , 2017 .