IJAT Vol.11 No.3 pp. 481-489
doi: 10.20965/ijat.2017.p0481


Collision Avoidance Algorithm for Collaborative Robotics

Stefano Mauro, Stefano Pastorelli, and Leonardo Sabatino Scimmi

Department of Mechanical and Aerospace Engineering, Politecnico di Torino Duca degli Abruzzi 24, 10129 Torino, Italy

Corresponding author

October 1, 2016
December 6, 2016
Online released:
April 28, 2017
May 5, 2017
collaborative robotics, artificial potentials, collision avoidance algorithm, vision system

The paper discusses a study on a real-time collision avoidance algorithm for collaborative robotics applications. Within the work it is considered that a vision system detects the position of an obstacle and defines an ellipsoid which completely includes it. A similar virtual ellipsoid is considered to include the end effector, and its pose is computed based on the robot configuration. The distance between ellipsoids is input into the collision avoidance algorithm based on the method of artificial potentials. The tuning of the algorithm is described herein, along with an analysis of its performance under different operating conditions. The results of two collision avoidance tests are also presented. For the first test, the end-effector must avoid a fixed obstacle placed along a planned path. For the second test, the obstacle is moving, following a trajectory that intersects that of the end-effector. Finally, the behavior of the algorithm with increasing velocities of the end-effector and obstacle is analyzed.

  1. [1] [Accessed March 29, 2017]
  2. [2] G. Michalos, S. Makris, J. Spiliotopoulos, I. Misios, P. Tsarouchi, and G. Chryssolouris, “ROBO-PARTNER: Seamless Human-Robot Cooperation for Intelligent, Flexible and Safe Operations in the Assembly Factories of the Future,” (CATS 2014) 5th CIPR Conf. on Assembly Technologies and Systems, 13-14, Dresden, Germany, pp. 71-76, November 2014.
  3. [3] [Accessed March 29, 2017]
  4. [4] T. Koskinen, T. Heikkilddota, and T. Pulkkinen, “Monitoring of co-operative assembly tasks: functional, safety and quality aspects,” Proc. of 2009 IEEE Int. Symp. on Assembly and Manufacturing (IEEE ISAM 2009), 17-20, Suwon, Korea, pp. 310-315, November 2009.
  5. [5] [Accessed March 29, 2017]
  6. [6] T. Ende, S. Haddadin, S. Parusel, T. Wusthoff, M. Hassenzahl, and A. Albu-Schaffer, “A human-centered approach to robot gesture based communication within collaborative working processes,” IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, San Francisco, September 25-30, pp. 3367-3374, 2011.
  7. [7] F. Duan, M. Morioka, J. T. C Tan, and T. Arai, “Multi-Modal Assembly-Support System for Cell Production,” Int. J. Automation Technol., Vol.2, No.5, pp. 384-389, 2008.
  8. [8] [Accessed March 29, 2017]
  9. [9] O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. of Robotics Research, Vol.5, No.1, pp. 90-98, 1986.
  10. [10] Z. Kaneshige, S. Hasegawa, and K. Terashima, “The Development of an Autonomous Mobile Crane System Considering On-Line Obstacle Recognition and Path Planning,” Int. J. Automation Technol., Vol.2, No.2, pp. 131-140, 2008.
  11. [11] N. Asakawa and Y. Kanjo, “Collision Avoidance of a Welding Robot for a Large Structure (Application of a potential field),” Int. J. Automation Technol., Vol.7, No.2, pp. 190-195, 2013.
  12. [12] F. Flacco, T. Kröger, A. De Luca, and O. Khatib, “A depth space approach to human-robot collision avoidance,” IEEE Int. Conf. on Robotics and Automation, St. Paul, MN, Maggio 2012.
  13. [13] B. Lacevic and P. Rocco, “Kinetostatic danger field – a novel safety assessment for human-robot interaction,” 2010 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 2169-2174, Oct. 2010.
  14. [14] M. Parigi Polverini, A. M. Zanchettin, and P. Rocco, “Real-Time Collision Avoidance in Human-Robot Interaction Based on Kinetostatic Safety Field,” 2014 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS 2014), September 14-18, Chicago, IL, USA, 2014.
  15. [15] L. Balan and G. Bone, “Real-time 3D collision avoidance method for safe human and robot coexistence,” Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Beijing, PRC, pp. 276-282, October 2006.
  16. [16] G. Biondi, S. Mauro, T. Mohtar, S. Pastorelli, and M. Sorli, “A geometric method for estimating space debris center of mass position and orbital parameters from features tracking,” Proc. of 2nd IEEE Int. Workshop on Metrology for Aerospace, MetroAeroSpace 2015; Benevento; Italy; June 3-5, 2015.
  17. [17] G. Biondi, S. Mauro, T. Mohtar, S. Pastorelli, and M. Sorli, “Feature-based estimation of space debris angular rate via compressed sensing and Kalman filtering,” Proc. of 3rd IEEE Int. Workshop on Metrology for Aerospace, MetroAeroSpace 2016; Florence, Italy; pp. 22-23, June 2016.
  18. [18] G. Biondi, S. Mauro, S. Pastorelli, and M. Sorli, “Fault tolerant feature-based estimation of space debris rotational motion during active removal missions,” Proc. of 67th IAC – Int. Astronautical Congress 2016, Guadalajara (Mexico), pp. 26-30, Sept. 2016.
  19. [19] G. Biondi, S. Mauro, T. Mohtar, S. Pastorelli, and M. Sorli, “Attitude recovery from feature tracking for estimating angular rate of non-cooperative spacecraft,” Mechanical Systems and Signal Processing, Vol.83, No.15, pp. 321-336, January 2017.

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Last updated on Sep. 21, 2017