Probability of Collision between Autonomous Mobile Robot with an Obstacle

The probability estimation problem of a collision between path tracking for an autonomous mobile robot with an obstacle is considered. We reviewed and analyzed methods for solving this problem. We show that reviewed methods use periodically updated grid maps (occupancy grids). The new method of probability estimation of the collision between the mobile robot with an obstacle is presented. This method based on the use of probabilistic grid map. Each cell of this map stores the estimated probability that the obstacle is located within. In addition, this map stores the conditional probability of occupying of the map cells by a robot, taking into account the possible lateral and angular deviation from the planned trajectory. This deviation caused by error connected with dynamic characteristics of the tracking system. To build the probabilistic occupancy grid, the dynamically updated multilayer grid map was used. Each layer of this map, except for the resulting output, has been filled with the data obtained from classifiers which process information incoming from sensory of the robot. This layer is the result of Bayesian inference from the layers laying below. The motion control system provides construction of the multilayered grid maps, probabilistic occupancy grids, coordinate estimations, path planning, motion tracking and the probability estimation for collision with obstacles. The method such estimation is implemented as an embedded module compatible with ROS (Robot Operating System). The description of experiments with the mobile robot in-nature (on the field) is given in the case when a motile obstacle appears intercepting the planned path. The estimated changes of probability for a collision between the mobile robot with obstacle are presented, interpretation of the obtained results is also given. Here we demonstrated the necessity of collision probability estimation for assessment of the risk as the main safety indicator of the given motion control system. Results of this work are considered and evaluated as a solution to the problem of ensuring the safety of motion tracking for autonomous mobile robots.

1. Liu S. B. et al. Provably Safe Motion of mobile robots in human environments, International Conference on Intelligent Robots and Systems (IROS-2017), 2017, pp. 1351—1357, DOI: 10.1109/IROS.2017.8202313.

2. Adam S., Larsen M., Jensen K., Schultz U. P. Rule-based dynamic safety monitoring for mobile robots, Journal of software Engineering, 2016, vol. 7, pp. 120—141.

3. Hafez O. A., Arana G. D., Spenko M. Integrity risk-based model predictive control for mobile robots, 2019 International conference on Robotics and Automation (ICRA), Montreal, Canada, 2019, pp. 5793—5799, DOI: 10.1109/ICRA.2019.8793521.

4. Gill J.S, Pisu P., Krovi V. N., Schmid M. J. Behavior identification and prediction for a probabilistic risk framework, arXiv:1905.08332v1, 2019, 10 p. DOI: 10.1115/DSCC2019-9097.

5. Blake A. et al. FPR — Fast path risk algorithm to evaluate collision probability, IEEE Robotics and Automation Letters, 2020, vol. 5, no. 1, pp. 1—7, DOI: 10.1109/LRA.2019.2943074.

6. Gindele T., Brechtel S., Dillmann R. A probabilistic model for estimating driver behaviors and vehicle trajectories in traffic environments, 13th International IEEE Conference on, IEEE, 2010, pp. 1625—1631, DOI: 10.1109/ITSC.2010.5625262

7. Xiangkun He, Yulong Liu, Chen Lv, Xuewu Ji, Yahui Liu. Emergency steering control of autonomous vehicle for collision avoidance and stabilization, Vehicle System Dynamics, 2019, vol. 57, iss. 8, pp. 1163—1187, DOI: 10.1080/00423114.2018.1537494.

8. Puphal T., Probst M., Eggert J. Probabilistic UncertaintyAware Risk Spot Detector for Naturalistic Driving, IEEE Transactions on Intelligent Vehicles, 2019, vol. 4, no. 3, pp. 406—415, DOI: 10.1109/TIV.2019.2919465.

9. Miura J., Negishi Y., Shirai Y. Adaptive robot speed control by considering map and motion uncertainty, Robotics and Autonomous Systems, 2006, vol. 54, pp. 110—117, DOI: 10.1016/j.robot.2005.09.020.

10. Agamennoni G., Nieto J. I., Nebot E. M. Estimation of multivehicle dynamics by considering contextual information, IEEE Transactions on robotics, August 2012, vol. 28, no. 4, DOI: 10.1109/TRO.2012.2195829.

11. Iakovlev D. S., Tachkov A. A. The task of assessing and ensuring the safety of ground-based robotic systems, Sbornik trudov 3-oj Mezhdunarodnoj nauchno-prakticheskoj konferencii "Fundamental’noprikladnye problemy bezopasnosti, zhivuchesti, nadyozhnosti, ustojchivosti i effektivnosti sistem", pp. 409—413 (in Russian).

12. Severtsev N. A., Dedkov V. K. System analysis and safety modeling, Moscow, Vysshaya shkola, 2006, 462 pp. (in Russian).

13. Rios-Martinez J., Spalanzani A. and Laugier C. Understanding human interaction for probabilistic autonomous navigation using Risk-RRT approach, IEEE Int. Conf. IROS, 2011, pp. 2014—2019, DOI: 10.1109/IROS.2011.6094496.

14. Fulgenzi C., Spalanzani A., Laugier C. Dynamic obstacle avoidance in uncertain environment combining PVOs and occupancy grid, Proceedings 2007 IEEE International Conference on Robotics and Automation, Roma, 2007, pp. 1610—1616, DOI: 10.1109/ROBOT.2007.363554.

15. Kuznetsov A. V. Models of movement, interaction and communication networks of mobile agents in hierarchical systems based on cellular automata, Doctor thesis in Physical and Mathematical Sciences, Voronezh State University, 2019, 268 p. (in Russian).

16. Elfes A. Using occupancy grids for mobile robot perception and navigation, Computer, June 1989, vol. 22, pp. 46—57.

17. Freda L. et al. 3D Multi-robot patrolling with two-level coordination strategy, Autonomous Robots, 2019, vol. 43, pp. 1747—1779, DOI: 10.1007/s10514-018-09822-3.

18. Lu D. V., Hershberger D., Smart W. D. Layered costmaps for context-sensitive navigation, IEEE International Conference on Intelligent Robots and Systems (IROS), 2014, pp. 709—715, DOI: 10.1109/IROS.2014.6942636.

19. Fankhauser P., Hutter M. A Universal Grid Map Library: Implementation and Use Case for Rough Terrain Navigation, Robot Operating System (ROS), Springer, Cham, 2016, vol. 1, pp. 99—120, DOI: 10.1007/978-3-319-26054-9_5.

20. Thrun S., Burgard W., Fox D. Probabilistic Robotics, MIT press, 2006, pp. 26—33.

21. Buravlev A. I. To the question of determining the reduced damage zone of objects, Vooruzhenie i ekonomika, 2018, vol. 2 (44), pp. 11—16.

22. Tachkov A. A., Vukolov A. Yu., Kozov A. V. Peculiarities of porting the Robot Operating System Framework onto Elbrus platform, Programmnye produkty i sistemy, 2019, vol. 32, no. 4, pp. 655—664. DOI: 10.15827 / 0236-235X.128.655-664 (in Russian).