Abstract
This work presents an unmanned aerial vehicle (UAV) planning algorithm for local defense of a system from enemy UAVs that come to attack or reconnoiter the system. Planning with non-cooperative moving targets often leads to difficulties in utilizing the widely-used path planning algorithms, since their intention and path plans are not known. Furthermore, because a destination of our UAVs path plan can be changed over time, a fast path planning algorithm which can deal with various obstacles is needed. To handle these problems, two key methods are adopted in this work: First, an informative planning is used for predicting each path of enemy UAVs. Second, the iterative linear quadratic regulator algorithm (iLQR) is utilized to derive feasible paths in a mission environment. Utilizing the two methods, the system predicts paths of invading UAVs and allocates friend UAVs to dominate enemies. Finally, each path for an allocated task is computed via iLQR.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Leahy K, Zhou D, Vasile CI, Oikonomopoulos K, Schwager M, Belta C (2016) Persistent surveillance for unmanned aerial vehicles subject to charging and temporal logic constraints. Auton Robots 40(8):1363–1378
Tang Z, Ozguner U (2005) Motion planning for multitarget surveillance with mobile sensor agents. IEEE Trans Robot 21(5):898–908
Farmani N, Sun L, Pack D (2014) Optimal UAV sensor management and path planning for tracking multiple mobile targets. In: ASME 2014 dynamic systems and control conference, American Society of Mechanical Engineers V002T25A003–V002T25A003
Smith SL, Schwager M, Rus D (2012) Persistent robotic tasks: monitoring and sweeping in changing environments. IEEE Trans Robot 28(2):410–426
Ha JS, Choi HL (2014) Periodic sensing trajectory generation for persistent monitoring. In: 2014 IEEE 53rd Annual conference on decision and control (CDC). IEEE, pp 1880–1886
Park SS, Ha JS, Cho DH, Choi HL (2018) A distributed ADMM approach to informative trajectory planning for multi-target tracking. arXiv preprint arXiv:1807.11068
Jacobson DH, Mayne DQ (1970) Differential dynamic programming
Todorov E, Li W (2005) A generalized iterative IQG method for locally-optimal feedback control of constrained nonlinear stochastic systems. In: Proceedings of the 2005 American control conference, 2005. IEEE, pp 300–306
Van Den Berg J, Patil S, Alterovitz R (2012) Motion planning under uncertainty using iterative local optimization in belief space. Int J Robot Res 31(11):1263–1278
Choi HL, Brunet L, How JP (2009) Consensus-based decentralized auctions for robust task allocation. IEEE Trans Robot 25(4):912–926
Acknowledgements
This research was sponsored by the Agency for Defense Development under the grant UD170016RD.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chae, HJ., Park, SS., Kim, HV., Ko, HS., Choi, HL. (2020). UAV Path Planning for Local Defense Systems. In: P. P. Abdul Majeed, A., Mat-Jizat, J., Hassan, M., Taha, Z., Choi, H., Kim, J. (eds) RITA 2018. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-8323-6_17
Download citation
DOI: https://doi.org/10.1007/978-981-13-8323-6_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8322-9
Online ISBN: 978-981-13-8323-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)