Transient Virtual Obstacles for Safe Robot Navigation in Indoor Environments

  • Abhijeet Ravankar Kitami Institute of Technology
  • Ankit Ravankar Hokkaido University
  • Yohei Hoshino Kitami Institute of Technology
  • Michiko Watanabe Kitami Institute of Technology
  • Arpit Rawankar Vidyalankar Insitute of Technology
Keywords: Path Planning, Robot Navigation, Virtual Obstacles

Abstract

Service robots are expected to work in real world scenarios which are dynamic in nature. The traversable and non-traversable passages of the environment might change at different times. For example, it is common for some of the passages or areas in the map to be inaccessible due to cleaning, repair works, or other reasons. On the other hand, some passages in the environment could have a high congestion of people compared to other passages. This requires a feature to alter the path of the robots to address these dynamic changes, instead of only relying on the shortest paths criterion. To this end, this paper proposes Transient Virtual Obstacles (TVO): a method to actively block desired paths by using virtual obstacles. The virtual obstacles can be placed or removed by a user anywhere in the map for programmable intervals. The robot’s plan their paths considering the virtual obstacles, thereby blocking certain paths, and planning the desired trajectories through specific paths. Another major advantage of the proposed TVO algorithm is that the existing robot path planner does not needs to be modified. The proposed TVO is tested in both simulation and real environments and results are discussed highlighting the merits of safe mobile robot navigation.

Author Biography

Abhijeet Ravankar, Kitami Institute of Technology

Assistant Professor
Facutly of Mechanical Engineering
Kitami Institute of Technology, Hokkaido, Japan

References

A. Ravankar, A. Ravankar, Y. Kobayashi, Y. Hoshino, and C.-C. Peng, “Path smoothing techniques in robot navigation: State-of-the-art, current and future challenges,” Sensors, vol. 18, no. 9, p. 3170, Sep 2018. [Online]. Available: http://dx.doi.org/10.3390/s18093170

A. Ravankar, A. Ravankar, A. Rawankar, Y. Hoshino, and Y. Kobayashi, “Itc: Infused tangential curves for smooth 2d and 3d navigation of mobile robots,” Sensors, vol. 19, no. 20, p. 4384, Oct 2019. [Online]. Available: http://dx.doi.org/10.3390/s19204384

S. B. Liu, H. Roehm, C. Heinzemann, I. Ltkebohle, J. Oehlerking, and M. Althoff, “Provably safe motion of mobile robots in human environments,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Sept 2017, pp. 1351–1357.

B. Penin, P. R. Giordano, and F. Chaumette, “Minimum-time trajectory planning under intermittent measurements,” IEEE Robotics and Automation Letters, vol. 4, no. 1, pp. 153–160, Jan 2019.

A. A. Ravankar, Y. Hoshino, A. Ravankar, L. Jixin, T. Emaru, and Y. Kobayashi, “Algorithms and a framework for indoor robot mapping in a noisy environment using clustering in spatial and hough domains,” International Journal of Advanced Robotic Systems, vol. 12, no. 3, p. 27, 2015.

A. Ravankar, A. A. Ravankar, Y. Hoshino, T. Emaru, and Y. Kobayashi, “On a hopping-points svd and hough transform based line detection algorithm for robot localization and mapping,” International Journal of Advanced Robotic Systems, vol. 13, no. 3, p. 98, 2016.

A. A. Ravankar, A. Ravankar, T. Emaru, and Y. Kobayashi, “Line segment extraction and polyline mapping for mobile robots in indoor structured environments using range sensors,” SICE Journal of Control, Measurement, and System Integration, vol. 13, no. 3, pp. 138–147, 2020.

T. Regev and V. Indelman, “Multi-robot decentralized belief space planning in unknown environments via efficient re-evaluation of impacted paths,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct 2016, pp. 5591–5598.

A. Ravankar, A. Ravankar, Y. Kobayashi, Y. Hoshino, C.-C. Peng, and M. Watanabe, “Hitchhiking based symbiotic multi-robot navigation in sensor networks,” Robotics, vol. 7, no. 3, p. 37, Jul 2018. [Online]. Available: http://dx.doi.org/10.3390/robotics7030037

A. Ravankar, A. A. Ravankar, Y. Kobayashi, and T. Emaru, “Can robots help each other to plan optimal paths in dynamic maps?” in 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Sep. 2017, pp. 317–320.

A. Ravankar, A. A. Ravankar, Y. Hoshino, and Y. Kobayashi, “On sharing spatial data with uncertainty integration amongst multiple robots having different maps,” Applied Sciences, vol. 9, no. 13, p. 2753, Jul 2019. [Online]. Available: http://dx.doi.org/10.3390/app9132753

A. Ravankar, A. A. Ravankar, Y. Kobayashi, and T. Emaru, “Avoiding blind leading the blind,” International Journal of Advanced Robotic Systems, vol. 13, no. 6, p. 1729881416666088, 2016.

M. Fujita, Y. Gotoy, N. Nidez, K. Satohx, and H. Hosobe, “Toward a robot that acquires logical recognition of space,” Information Engineering Express, vol. 3, no. 4, pp. 1–10, 2017.

D. Delling, P. Sanders, D. Schultes, and D. Wagner, “Engineering route planning algorithms,” in Algorithmics of Large and Complex Networks, ser. Lecture Notes in Computer Science, J. Lerner, D. Wagner, and K. Zweig, Eds. Springer Berlin Heidelberg, 2009, vol. 5515, pp. 117–139. [Online]. Available: http://dx.doi.org/10.1007/978-3-642-02094-0_7

S. M. LaValle, Planning Algorithms. Cambridge, U.K.: Cambridge University Press, 2006, available at http://planning.cs.uiuc.edu/ [Accessed: 11-02-2016].

J.-C. Latombe, Robot Motion Planning. Norwell, MA, USA: Kluwer Academic Publishers, 1991.

P. Hart, N. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” Systems Science and Cybernetics, IEEE Transactions on, vol. 4, no. 2, pp. 100–107, July 1968.

A. Stentz and I. C. Mellon, “Optimal and efficient path planning for unknown and dynamic environments,” International Journal of Robotics and Automation, vol. 10, pp. 89–100, 1993.

L. Kavraki, P. Svestka, J.-C. Latombe, and M. Overmars, “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” Robotics and Automation, IEEE Transactions on, vol. 12, no. 4, pp. 566–580, Aug 1996.

S. M. Lavalle, “Rapidly-exploring random trees: A new tool for path planning,” Tech. Rep., 1998.

Y. Hwang and N. Ahuja, “A potential field approach to path planning,” Robotics and Automation, IEEE Transactions on, vol. 8, no. 1, pp. 23–32, Feb 1992.

A. Aggarwal, A. Kukreja, and P. Chopra, “Vision based collision avoidance by plotting a virtual obstacle on depth map,” in The 2010 IEEE International Conference on Information and Automation, June 2010, pp. 532–536.

and M. H. Ang and H. K. and, “Virtual obstacle concept for local-minimum-recovery in potential-field based navigation,” in Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065), vol. 2, April 2000, pp. 983–988 vol.2.

C. Luo, S. X. Yang, H. Mo, and X. Li, “Safety aware robot coverage motion planning with virtual-obstacle-based navigation,” in 2015 IEEE International Conference on Information and Automation, Aug 2015, pp. 2110–2115.

K. Ueno, T. Kinoshita, K. Kobayashi, , and K. Watanabe, “Development of a robust path-planning algorithm using virtual obstacles for an autonomous mobile robot,” Journal of Robotics and Mechatronics, vol. 27, no. 3, pp. 286–292, 2015.

A. A. Ravankar, A. Ravankar, C. Peng, Y. Kobayashi, and T. Emaru, “Task coordination for multiple mobile robots considering semantic and topological information,” in 2018 IEEE International Conference on Applied System Invention (ICASI), April 2018, pp. 1088–1091.

S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). The MIT Press, 2005.

A. Ravankar, A. A. Ravankar, Y. Kobayashi, C. Peng, and T. Emaru, “Real-time multirobot path planning revisited as a caching problem,” in 2018 IEEE International Conference on Applied System Invention (ICASI), April 2018, pp. 350–353.

Published
2020-06-29
Section
Technical Papers (Artificial Intelligence)