System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization
- 1.8k Downloads
A complex system for control of swarms of micro aerial vehicles (MAV), in literature also called as unmanned aerial vehicles (UAV) or unmanned aerial systems (UAS), stabilized via an onboard visual relative localization is described in this paper. The main purpose of this work is to verify the possibility of self-stabilization of multi-MAV groups without an external global positioning system. This approach enables the deployment of MAV swarms outside laboratory conditions, and it may be considered an enabling technique for utilizing fleets of MAVs in real-world scenarios. The proposed visual-based stabilization approach has been designed for numerous different multi-UAV robotic applications (leader-follower UAV formation stabilization, UAV swarm stabilization and deployment in surveillance scenarios, cooperative UAV sensory measurement) in this paper. Deployment of the system in real-world scenarios truthfully verifies its operational constraints, given by limited onboard sensing suites and processing capabilities. The performance of the presented approach (MAV control, motion planning, MAV stabilization, and trajectory planning) in multi-MAV applications has been validated by experimental results in indoor as well as in challenging outdoor environments (e.g., in windy conditions and in a former pit mine).
KeywordsMicro aerial vehicles (MAVs) Unmanned aerial vehicles (UAVs) Formations Swarms Visual relative localization Stabilization Control Trajectory planning
This work has been supported by the Ministry of Education of the Czech Republic under project no. LH11053 and the experimental works required for paper revisions by Project No. HS 13167/830/8301616C000 founded by Khalifa University for the MBZIRC competition, both projects supporting the joint research of the Czech Technical University in Prague and the University of Pennsylvania. In addition, Martin Saska has been supported by the Grant Agency of the Czech Republic under postdoc Grant No. P103-12/P756. The work of Jan Faigl has been also partially supported by the Czech Science Foundations (GACR) under the research Project No. 13-18316P. Tomas Krajnik has been supported by the EU project STRANDS (ICT-600623). Tomas Baca has been supported by CTU grant no. SGS15/157/OHK3/2T/13. Final experiments done by Martin Saska for revisions of the paper have been supported by the Czech Science Foundations (GACR) under the research Project No. 16-24206S.
- Balch, T., & Hybinette, M. (2000). Social potentials for scalable multi-robot formations. In Proceedings of IEEE conference on robotics and automation.Google Scholar
- Barnes, L., Garcia, R., Fields, M., & Valavanis, K. (2008). Swarm formation control utilizing ground and aerial unmanned systems. In IEEE/RSJ international conference on intelligent robots and systems.Google Scholar
- Bennet, D. J., & McInnes, C. R. (2009). Verifiable control of a swarm of unmanned aerial vehicles. Journal of Aerospace Engineering, 223(7), 939–953.Google Scholar
- Cai, W., Yu, Q., & Wang, H. (2004). A fast contour-based approach to circle and ellipse detection. In 5th world congress on intelligent control and automation (WCICA).Google Scholar
- Carreras, M., Ridao, P., García, R., & Nicosevici, T. (2003). Vision-based localization of an underwater robot in a structured environment. In ICRA.Google Scholar
- Faigl, J., Krajník, T., Chudoba, J., Preucil, L., Saska, M. (2013). Low-cost embedded system for relative localization in robotic swarms. In Proceedings of IEEE international conference on robotics and automation.Google Scholar
- Faigl, J., Krajník, T., Vonásek, V., & Přeučil, L. (2012). On Localization Uncertainty in an Autonomous Inspection. In IEEE international conference on robotics and automation (ICRA).Google Scholar
- Holland, O., Woods, J., Nardi, R., & Clark, A. (2005). Beyond swarm intelligence: The UltraSwarm. In IEEE swarm intelligence symposium.Google Scholar
- Jia, L.-Q., Liu, H.-M., Wang, Z.-H., & Chen, H. (2011). An effective non-HT circle detection for centers and radii. In ICMLC.Google Scholar
- Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks (Vol. 4).Google Scholar
- Krajník, T., Nitsche, M., Faigl, J., Vanek, P., Saska, M., Přeučil, L., Duckett, T., & Mejail, M. (2014). A practical multirobot localization system. Journal of Intelligent & Robotic Systems, Online first, 2014.http://dx.doi.org/10.1007/s10846-014-0041-x.
- Lange, S., Sunderhauf, N., & Protzel, P. (2009). A vision based onboard approach for landing and position control of an autonomous multirotor uav in GPS-denied environments. In International conference on advanced robotics (ICAR).Google Scholar
- Lee, T., Leoky, M., & McClamroch, N. (2010). Geometric tracking control of a quadrotor UAV on se(3). In 49th IEEE conference on decision and control (CDC).Google Scholar
- Leonard, N., & Fiorelli, E. (2001). Virtual leaders, artificial potentials and coordinated control of groups. In Proceedings of the 40th IEEE Conference on Decision and Control.Google Scholar
- Masselli, A., & Zell, A. (2012). A novel marker based tracking method for position and attitude control of MAVs. In Proceedings of international micro air vehicle conference and flight competition.Google Scholar
- Multimedia. (2015). Various experiments with multi-MAV system verifying the proposed approach. http://mrs.felk.cvut.cz/data/mavgroups/ Retrieved from 8 August 2015.
- Rad, A. A., Faez, K., & Qaragozlou, N. (2003). Fast circle detection using gradient pair vectors. In DICTA.Google Scholar
- Saska, M., Chudoba, J., Preucil, L., Thomas, J., Loianno, G., Tresnak, A., Vonasek, V., & Kumar, V. (2014a). Autonomous deployment of swarms of micro-aerial vehicles in cooperative surveillance. In Proceedings of 2014 international conference on unmanned aircraft systems (ICUAS).Google Scholar
- Saska, M., Hess, M., & Schilling, K. (2007). Hierarchical spline path planning method for complex environments. In Proceedings of the 4th international conference on informatics in control, automation and robotics. Angers, France.Google Scholar
- Saska, M., Kasl, Z., Preucil, L. (2014b). Motion planning and control of formations of micro aerial vehicles. In Proceedings of the 19th world congress of the international federation of automatic control.Google Scholar
- Saska, M., Mejia, J. S., Stipanovic, D. M., Schilling, K. (2009). Control and navigation of formations of car-like robots on a receding horizon. In Proceedings of 3rd IEEE multi-conference on systems and control.Google Scholar
- Saska, M., Vonasek, V., & Preucil, L. (2010). Control of ad-hoc formations for autonomous airport snow shoveling. In IEEE/RSJ international conference on intelligent robots and systems (Vol. 1, pp. 4995–5000). Taipei: IEEE Industrial Electronics Society.Google Scholar
- Saska, M., Krajnik, T., Vonasek, V., Kasl, Z., Spurny, V., & Preucil, L. (2014c). Fault-tolerant formation driving mechanism designed for heterogeneous MAVs-UGVs groups. Journal of Intelligent and Robotic Systems, 73(1–4), 603–622.Google Scholar
- Saska, M., Vonasek, V., Krajnik, T., & Preucil, L. (2014d). Coordination and navigation of heterogeneous MAV&UGV formations localized by a hawk-eye-like approach under a model predictive control scheme. International Journal of Robotics Research, 33(10), 1393–1412.Google Scholar
- Teacy, W., Nie, J., McClean, S., & Parr, G. (2010). Maintaining connectivity in UAV swarm sensing. In IEEE GLOBECOM Workshops.Google Scholar
- Yang, S., Scherer, S. A., & Zell, A. (2012). An onboard monocular vision system for autonomous takeoff, hovering and landing of a micro aerial vehicle. In International conference on unmanned aircraft systems (ICUAS’12).Google Scholar
- Yang, S., Scherer, S., & Zell, A. (2012). An Onboard monocular vision system for autonomous takeoff, hovering and landing of a micro aerial vehicle. Journal of Intelligent & Robotic Systems, 69(1–4), 499–515.Google Scholar