Machine Perception Platform for Safe Human-Robot Collaboration

Speed and separation monitoring, operation defined in safety standards for collaborative robots, is meant for real-time collision avoidance. Laser scanners are safety-certified devices and a traditional sensor choice for this application. Unfortunately, the limited amount of target information they provide restricts their use in realistic collaborative robot scenarios, in which knowledge about the nature of the detected targets is required. We propose a machine perception platform for safe human-robot collaboration based on a broadband W-band radar, a 3D camera, and a laser scanner. Besides computing range and angle-of-arrival information, we use the micro-Doppler signatures of the radar echo signals to distinguish between humans and objects.

[1]  A. Stelzer,et al.  A 77-GHz FMCW MIMO Radar Based on an SiGe Single-Chip Transceiver , 2009, IEEE Transactions on Microwave Theory and Techniques.

[2]  Arijit Sinharay,et al.  A Novel Microwave Measurement Technique for Non-Contact Vital Sign Monitoring , 2018, 2018 IEEE SENSORS.

[3]  V. Nikolic,et al.  of Advanced Robotic Systems Stereo Vision-Based Human Tracking for Robotic Follower Regular Paper , 2013 .

[4]  Hermann Rohling,et al.  Radar CFAR Thresholding in Clutter and Multiple Target Situations , 1983, IEEE Transactions on Aerospace and Electronic Systems.

[5]  Jeffrey Too Chuan Tan,et al.  Triple stereo vision system for safety monitoring of human-robot collaboration in cellular manufacturing , 2011, 2011 IEEE International Symposium on Assembly and Manufacturing (ISAM).

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

[7]  Fabien Moutarde,et al.  Gesture Recognition Using a Depth Camera for Human Robot Collaboration on Assembly Line , 2015 .

[8]  Björn Matthias,et al.  Safety of Industrial Robots: From Conventional to Collaborative Applications , 2012, ROBOTIK.

[9]  S. Bjorklund,et al.  Evaluation of a micro-Doppler classification method on mm-wave data , 2012, 2012 IEEE Radar Conference.

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

[11]  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).

[12]  B.D. Van Veen,et al.  Beamforming: a versatile approach to spatial filtering , 1988, IEEE ASSP Magazine.

[13]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[14]  J. J. M. de Wit,et al.  Classification of small UAVs and birds by micro-Doppler signatures , 2013, 2013 European Radar Conference.

[15]  Andrew Gerald Stove,et al.  Linear FMCW radar techniques , 1992 .

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

[17]  Monika A. Minarcin,et al.  Considerations in Collaborative Robot System Designs and Safeguarding , 2016 .

[18]  Hadi Heidari,et al.  Multisensor data fusion for human activities classification and fall detection , 2017, 2017 IEEE SENSORS.

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

[20]  Przemyslaw A. Lasota,et al.  Toward safe close-proximity human-robot interaction with standard industrial robots , 2014, 2014 IEEE International Conference on Automation Science and Engineering (CASE).

[21]  Emil Nilsson,et al.  60GHz vital sign radar using 3D-printed lens , 2016, 2016 IEEE SENSORS.

[22]  Reid G. Simmons,et al.  Sensor fusion for human safety in industrial workcells , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.