Safe physical HRI: Toward a unified treatment of speed and separation monitoring together with power and force limiting

So-called collaborative robots are a current trend in industrial robotics. However, they still face many problems in practical application such as reduced speed to ascertain their collaborativeness. The standards prescribe two regimes: (i) speed and separation monitoring and (ii) power and force limiting, where the former requires reliable estimation of distances between the robot and human body parts and the latter imposes constraints on the energy absorbed during collisions prior to robot stopping. Following the standards, we deploy the two collaborative regimes in a single application and study the performance in a mock collaborative task under the individual regimes, including transitions between them. Additionally, we compare the performance under “safety zone monitoring” with keypoint pair-wise separation distance assessment relying on an RGB-D sensor and skeleton extraction algorithm to track human body parts in the workspace. Best performance has been achieved in the following setting: robot operates at full speed until a distance threshold between any robot and human body part is crossed; then, reduced robot speed per power and force limiting is triggered. Robot is halted only when the operator’s head crosses a predefined distance from selected robot parts. We demonstrate our methodology on a setup combining a KUICA LBR iiwa robot, Intel RealSense RGB-D sensor and OpenPose for human pose estimation.

[1]  Sami Haddadin,et al.  Physical Human-Robot Interaction , 2016, Springer Handbook of Robotics, 2nd Ed..

[2]  Paolo Fiorini,et al.  Motion Planning in Dynamic Environments Using Velocity Obstacles , 1998, Int. J. Robotics Res..

[3]  Alessandro De Luca,et al.  Real-Time Computation of Distance to Dynamic Obstacles With Multiple Depth Sensors , 2017, IEEE Robotics Autom. Lett..

[4]  Federico Vicentini,et al.  COVR – Towards simplified evaluation and validation of collaborative robotics applications across a wide range of domains based on robot safety skills , 2018 .

[5]  Yaser Sheikh,et al.  OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Oussama Khatib,et al.  A depth space approach to human-robot collision avoidance , 2012, 2012 IEEE International Conference on Robotics and Automation.

[7]  Matej Hoffmann,et al.  Toward safe separation distance monitoring from RGB-D sensors in human-robot interaction , 2018, ArXiv.

[8]  Paolo Rocco,et al.  Kinetostatic danger field - a novel safety assessment for human-robot interaction , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Alessandro Roncone,et al.  Peripersonal Space and Margin of Safety around the Body: Learning Visuo-Tactile Associations in a Humanoid Robot with Artificial Skin , 2016, PloS one.

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

[12]  O. Brock,et al.  Elastic Strips: A Framework for Motion Generation in Human Environments , 2002, Int. J. Robotics Res..

[13]  Oussama Khatib,et al.  A Depth Space Approach for Evaluating Distance to Objects , 2015, J. Intell. Robotic Syst..

[14]  Matteo Parigi Polverini,et al.  A computationally efficient safety assessment for collaborative robotics applications , 2017 .

[15]  Sami Haddadin,et al.  Safety Map: A Unified Representation for Biomechanics Impact Data and Robot Instantaneous Dynamic Properties , 2018, IEEE Robotics and Automation Letters.

[16]  Nikolaus Correll,et al.  Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study , 2014, ArXiv.

[17]  Matteo Parigi Polverini,et al.  A pre-collision control strategy for human-robot interaction based on dissipated energy in potential inelastic impacts , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[18]  Vincent Padois,et al.  Control of robots sharing their workspace with humans: An energetic approach to safety , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[19]  Vittorio Rampa,et al.  Device-Free Human Sensing and Localization in Collaborative Human–Robot Workspaces: A Case Study , 2016, IEEE Sensors Journal.

[20]  Alessandro De Luca,et al.  Robot Collisions: A Survey on Detection, Isolation, and Identification , 2017, IEEE Transactions on Robotics.

[21]  O. Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[22]  Jing Xiao,et al.  Perceiving guaranteed continuously collision-free robot trajectories in an unknown and unpredictable environment , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  Dennis S. Bernstein,et al.  Naive control of the double integrator , 2001 .

[24]  Carme Torras,et al.  3D collision detection: a survey , 2001, Comput. Graph..

[25]  Andrea Maria Zanchettin,et al.  Trajectory generation algorithm for safe human-robot collaboration based on multiple depth sensor measurements , 2018, Mechatronics.

[26]  Henrik Gordon Petersen,et al.  Computation of Safe Path Velocity for Collaborative Robots , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[27]  Alessandro Roncone,et al.  Compact Real-time Avoidance on a Humanoid Robot for Human-robot Interaction , 2018, 2018 13th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[28]  Alessandro De Luca,et al.  Integrated control for pHRI: Collision avoidance, detection, reaction and collaboration , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[29]  Bernt Schiele,et al.  DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model , 2016, ECCV.

[30]  S. LaValle,et al.  Randomized Kinodynamic Planning , 2001 .

[31]  Iasonas Kokkinos,et al.  DensePose: Dense Human Pose Estimation in the Wild , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[32]  Yaser Sheikh,et al.  OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Ciro Natale,et al.  Safeguarding a mobile manipulator using dynamic safety fields , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).