Adaptive Obstacle Avoidance for a Class of Collaborative Robots

In a human–robot collaboration scenario, operator safety is the main problem and must be guaranteed under all conditions. Collision avoidance control techniques are essential to improve operator safety and robot flexibility by preventing impacts that can occur between the robot and humans or with objects inadvertently left within the operational workspace. On this basis, collision avoidance algorithms for moving obstacles are presented in this paper: inspired by algorithms already developed by the authors for planar manipulators, algorithms are adapted for the 6-DOF collaborative manipulators by Universal Robots, and some new contributions are introduced. First, in this work, the safety region wrapping each link of the manipulator assumes a cylindrical shape whose radius varies according to the speed of the colliding obstacle, so that dynamical obstacles are avoided with increased safety regions in order to reduce the risk, whereas fixed obstacles allow us to use smaller safety regions, facilitating the motion of the robot. In addition, three different modalities for the collision avoidance control law are proposed, which differ in the type of motion admitted for the perturbation of the end-effector: the general mode allows for a 6-DOF perturbation, but restrictions can be imposed on the orientation part of the avoidance motion using 4-DOF or 3-DOF modes. In order to demonstrate the effectiveness of the control strategy, simulations with dynamic and fixed obstacles are presented and discussed. Simulations are also used to estimate the required computational effort in order to verify the transferability to a real system.

[1]  Lihui Wang,et al.  Active collision avoidance for human–robot collaboration driven by vision sensors , 2017, Int. J. Comput. Integr. Manuf..

[2]  Alberto Borboni,et al.  Cobot User Frame Calibration: Evaluation and Comparison between Positioning Repeatability Performances Achieved by Traditional and Vision-Based Methods , 2021, Robotics.

[3]  Véronique Perdereau,et al.  Real-time control of redundant robotic manipulators for mobile obstacle avoidance , 2002, Robotics Auton. Syst..

[4]  Shuai He,et al.  An improved artificial potential field method of trajectory planning and obstacle avoidance for redundant manipulators , 2018, International Journal of Advanced Robotic Systems.

[5]  Xin Huang,et al.  Active Collision Avoidance for Human-Robot Interaction With UKF, Expert System, and Artificial Potential Field Method , 2018, Front. Robot. AI.

[6]  Weidong Li,et al.  Cobot programming for collaborative industrial tasks: An overview , 2019, Robotics Auton. Syst..

[7]  Lihui Wang,et al.  Depth camera based collision avoidance via active robot control , 2014 .

[8]  Bruno Siciliano,et al.  Review of the damped least-squares inverse kinematics with experiments on an industrial robot manipulator , 1994, IEEE Trans. Control. Syst. Technol..

[9]  Irene Fassi,et al.  Validating Safety in Human-Robot Collaboration: Standards and New Perspectives , 2021, Robotics.

[10]  Jan Tommy Gravdahl,et al.  Set-based collision avoidance applications to robotic systems , 2020 .

[11]  Alessandro Gasparetto,et al.  Path Planning and Trajectory Planning Algorithms: A General Overview , 2015 .

[12]  Giacomo Palmieri,et al.  Real-Time Strategy for Obstacle Avoidance in Redundant Manipulators , 2021 .

[13]  Pedro Neto,et al.  On-line collision avoidance for collaborative robot manipulators by adjusting off-line generated paths: An industrial use case , 2019, Robotics Auton. Syst..

[14]  Francisco A. Candelas,et al.  Safe human-robot interaction based on dynamic sphere-swept line bounding volumes , 2011 .

[15]  Federico Vicentini,et al.  Collaborative Robotics: A Survey , 2020 .

[16]  Xiao Xu,et al.  A novel non-collision trajectory planning algorithm based on velocity potential field for robotic manipulator , 2018, International Journal of Advanced Robotic Systems.

[17]  Massimo Callegari,et al.  A Collision Avoidance Strategy for Redundant Manipulators in Dynamically Variable Environments: On-Line Perturbations of Off-Line Generated Trajectories , 2021, Machines.

[18]  Kristin Ytterstad Pettersen,et al.  Set-Based Tasks within the Singularity-Robust Multiple Task-Priority Inverse Kinematics Framework: General Formulation, Stability Analysis, and Experimental Results , 2016, Front. Robot. AI.

[19]  A. A. Maciejewski,et al.  Obstacle Avoidance , 2005 .

[20]  Stefano Mauro,et al.  A Practical and Effective Layout for a Safe Human-Robot Collaborative Assembly Task , 2021, Applied Sciences.

[21]  Xifeng Gao,et al.  An obstacle avoidance algorithm for robot manipulators based on decision-making force , 2021, Robotics Comput. Integr. Manuf..

[22]  Daniel Schilberg,et al.  RETRACTED: Obstacle Avoidance Algorithms: A Review , 2021, IOP Conference Series: Materials Science and Engineering.

[23]  Yanhe Zhu,et al.  Real-Time Kinematic Control for Redundant Manipulators in a Time-Varying Environment: Multiple-Dynamic Obstacle Avoidance and Fast Tracking of a Moving Object , 2020, IEEE Transactions on Industrial Informatics.

[24]  Robert Seifried,et al.  Trajectory tracking with collision avoidance for a parallel robot with flexible links , 2021 .

[25]  Paolo Gallina,et al.  Human-Robot Interaction through Eye Tracking for Artistic Drawing , 2021, Robotics.

[26]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1986 .

[27]  J. R. Llata,et al.  Working Together: A Review on Safe Human-Robot Collaboration in Industrial Environments , 2017, IEEE Access.