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Duration of collision-free motion of unmanned vehicles in a confined area

Published online by Cambridge University Press:  13 June 2014

Qian Zhang ,
Gerard Leng  and
Vengatesan Govindaraju
Qian Zhang*
Affiliation:
Department of Mechanical Engineering, National University of Singapore, Singapore
Gerard Leng
Affiliation:
Department of Mechanical Engineering, National University of Singapore, Singapore
Vengatesan Govindaraju
Affiliation:
Department of Mechanical Engineering, National University of Singapore, Singapore
*
*Corresponding author. E-mail: [email protected]; [email protected]
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Summary

This paper provides a mathematical approach to study the duration of collision-free motion of multiple unmanned autonomous vehicles (UXVs) operating in confined areas. A simple geometric model of the UXVs is first proposed, and the dynamics of the model is shown. The expected time of first collision is then formulated using the concept of mean free path from molecular dynamics. Monte-Carlo simulation is performed to verify the theory developed. The expected time of first collision is a function of the number of UXVs, the UXV speed and the sensor field of view (FOV) for a given operational area and vehicle size. Furthermore, the critical number of UXVs, above which collision can be deemed to occur instantly, is obtained.

Keywords

Motion planningMulti-robot systemsMobile robotsPath planningSwarm robotics
Type
Articles
Information
Robotica , Volume 34 , Issue 2 , February 2016 , pp. 347 - 360
Copyright
Copyright © Cambridge University Press 2014 

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References

1.Moratuwage, M. D. P., Wijesoma, W. S., Kalyan, B., Patrikalakis, N. M. and Moghadam, P., “Collaborative Multi-Vehicle Localization and Mapping in High Clutter Environments,” Proceedings of the 2010 11th International Conference on Control Automation Robotics & Vision (ICARCV) (2010) pp. 1422–1427.Google Scholar
2.Hao, L. and Nashashibi, F., “Cooperative Multi-Vehicle Localization Using Split Covariance Intersection Filter,” Proceedings of the 2012 IEEE Intelligent Vehicles Symposium (IV) (2012) pp. 211–216.Google Scholar
3.Gautam, A. and Mohan, S., “A Review of Research in Multi-Robot Systems,” Proceedings of the 2012 7th IEEE International Conference on Industrial and Information Systems (ICIIS) (2012) pp. 1–5.Google Scholar
4.Xidias, E. K. and Aspragathos, N. A., “Motion planning for multiple non-holonomic robots: A geometric approach,” Robotica 26 (4), 525–536 (2008).CrossRefGoogle Scholar
5.Knepper, R. A. and Rus, D., “Pedestrian-Inspired Sampling-Based Multi-Robot Collision Avoidance,” Proceedings of the 2012 IEEE RO-MAN (2012) pp. 94–100.Google Scholar
6.Chengtao, C., Chunsheng, Y., Qidan, Z. and Yanhua, L., “Collision Avoidance in Multi-Robot Systems,” Proceedings of the International Conference on Mechatronics and Automation (ICMA'07) (2007) pp. 2795–2800.Google Scholar
7.Mohan, Y. and Ponnambalam, S., “An Extensive Review of Research in Swarm Robotics,” World Congress on Nature & Biologically Inspired Computing (NaBIC'09), IEEE (2009) pp. 140–145.Google Scholar
8.Altshuler, Y., Yanovsky, V., Wagner, I. A. and Bruckstein, A. M., “Efficient cooperative search of smart targets using UAV Swarms,” Robotica 26(Special Issue 04), 551–557 (2008).CrossRefGoogle Scholar
9.Şahin, E., “Swarm Robotics: From Sources of Inspiration to Domains of Application,” In: Swarm Robotics (Şahin, E. and Spears, W., eds.) (Springer, Berlin, Heidelberg, 2005) pp. 10–20.CrossRefGoogle Scholar
10.Walter, B., Sannier, A., Reiners, D. and Oliver, J., “UAV Swarm Control: Calculating Digital Pheromone Fields with the GPU,” J. Def. Model. Simul. Appl. Methodol. Technol. 3 (3), 167–176 (2006).Google Scholar
11.John, S., Robert, M., Joshua, R., John, M. and Stephanie, R., “Swarming Unmanned Air and Ground Systems for Surveillance and Base Protection,” American Institute of Aeronautics and Astronautics Infotech@Aerospace Conference. (AIAA, Seattle Washington, 2009).Google Scholar
12.Robert, C., David, S., Todd, N., Witwicki, S. and Robert, B., @Cooperating Unmanned Vehicles,” American Institute of Aeronautics and Astronautics 1st Intelligent Systems Technical Conference. (AIAA, Chicago, Illinois, 2004).Google Scholar
13.Schill, F. S., Distributed Communication in Swarms of Autonomous Underwater Vehicles (Department of Information Engineering Research School of Information Sciences and Engineering, The Australian National University, 2007).Google Scholar
14.Irvine, R., “A geometrical approach to conflict probability estimation,” Air Traffic Control Q. 10 (2), 85–113 (2002).CrossRefGoogle Scholar
15.Prandini, M., Hu, J., Lygeros, J. and Sastry, S., “A probabilistic approach to aircraft conflict detection,” IEEE Trans. Intell. Transp. Syst. 1 (4), 199–220 (2000).CrossRefGoogle Scholar
16.Park, S. H. and Lee, B. H., “Analysis of robot collision characteristics using the concept of the collision map,” Robotica 24 (03), 295–303 (2006).CrossRefGoogle Scholar
17.Fox, D., Burgard, W. and Thrun, S., “The dynamic window approach to collision avoidance,” IEEE Robot. Autom. Mag. 4 (1), 23–33 (1997).CrossRefGoogle Scholar
18.Fiorini, P. and Shiller, Z., “Motion planning in dynamic environments using velocity obstacles,” Int. J. Robot. Res. 17 (7), 760–772 (1998).CrossRefGoogle Scholar
19.van den Berg, J., Ming, L. and Manocha, D., “Reciprocal Velocity Obstacles for Real-Time Multi-Agent Navigation,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'08) (2008) pp. 1928–1935.Google Scholar
20.Berg, J., Guy, S., Lin, M. and Manocha, D., “Reciprocal n-Body Collision Avoidance,” In: Robotics Research (Pradalier, C., Siegwart, R. and Hirzinger, G., eds.) (Springer, Berlin, Heidelberg, 2011) pp. 3–19.CrossRefGoogle Scholar
21. J. van den Berg, Snape, J., Guy, S. J. and Manocha, D., “Reciprocal Collision Avoidance with Acceleration-Velocity Obstacles,” Proceedings of the 2011 IEEE International Conference on Robotics and Automation (ICRA) (2011) pp. 3475–3482.Google Scholar
22.Halliday, D., Resnick, R. and Walker, J., Principles of Physics (Wiley, 2011) pp. 514–517.Google Scholar
23.Windmeijer, F. A. G., Goodness of Fit in Linear and Qualitative-Choice Models (Amsterdam: Thesis Publishers, 1992).Google Scholar