Abstract
This paper presents an original guidance system able to confer a tactical behavior to multi-rotor unmanned aerial vehicles (UAVs), such as quadcopters, that operate in potentially hostile, unknown, cluttered environments. By applying this guidance system, UAVs complete the assigned tasks, such as reaching a goal set, while minimizing both their exposure to opponents, whose location is unknown, and the predictability of their trajectories. A taxonomy of flight behaviors is provided to help users tuning those parameters that characterize the UAV’s level of cautiousness. This guidance system is supported by an original navigation system that exploits exclusively information gathered by onboard cameras and inertial measurement units. Numerical simulations and flight tests validate the applicability of the proposed guidance system in real-time, while performing all calculations aboard the UAV.
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Code Availability
The computer codes employed to perform the numerical simulations and flight tests presented in this paper will be disclosed at https://doi.org/http://laffitto.com/guidance_code.html upon acceptance for publication of this paper.
References
Al Marzouqi, M., Jarvis, R. A.: Robotic covert path planning; A survey. In: IEEE Conference on Robotics, Automation and Mechatronics, Beijing, China, pp 77–82 (2011)
Allgöwer, F., Zheng, A.: Nonlinear Model Predictive Control. Progress in Systems and Control Theory. Birkhäuser, Basel (2000)
Alonso-Mora, J., Baker, S., Rus, D.: Multi-robot formation control and object transport in dynamic environments via constrained optimization. Int. J. Robot. Res. 36(9), 1000–1021 (2017)
Anderson, J.: Computational Fluid Dynamics. Computational Fluid Dynamics: The Basics with Applications. McGraw-Hill Education, Upper Saddle Hill (1995)
Andert, F.: Drawing stereo disparity images into occupancy grids; Measurement model and fast implementation. In: International Conference on Intelligent Robots and Systems, St. Louis, MO, pp 5191–5197. IEEE (2009)
Ariens, D., Diehl, M., Ferreau, H. J., Houska, B., Logist, F., Quirynen, R., Vukov, M.: ACADO Toolkit User’s Manual, 1.2.1. KU Leuven, Leuven (2014)
Babel, L.: Coordinated target assignment and UAV path planning with timing constraints. J. Intell. Robot. Syst. 94(3-4), 857–869 (2019)
Bemporad, A., Patrinos, P.: Simple and certifiable quadratic programming algorithms for embedded linear model predictive control. IFAC Proc. Vol. 45(17), 14–20 (2012). https://doi.org/10.3182/20120823-5-NL-3013.00009. IFAC Conference on Nonlinear Model Predictive Control
Ben-Asher, J.: Optimal Control Theory with Aerospace Applications. AIAA education series American Institute of Aeronautics and Astronautics (2010)
Bernstein, D. S.: Matrix Mathematics; Theory, Facts, and Formulas, 2nd edn. Princeton University Press, Princeton (2009)
Blackmore, L., Ono, M., Williams, B. C.: Chance-constrained optimal path planning with obstacles. IEEE Trans. Robot. 27(6), 1080–1094 (2011)
Bohlin, R., Kavraki, L. E.: Path planning using lazy PRM. In: IEEE International Conference on Robotics and Automation, Paris, France, vol. 1, pp 521–528 (2000)
Boyd, S., El Ghaoui, L., Feron, E., Balakrishnan, V.: Linear matrix inequalities in system and control theory. SIAM, Philadelphia (1994)
Boyd, S. P., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)
Bresenham, J. E.: Algorithm for computer control of a digital plotter. IBM Syst. J. 4(1), 25–30 (1965)
Buijs, J., Ludlage, J., Brempt, W.V., Moor, B.D.: Quadratic programming in model predictive control for large scale systems. IFAC Proc. Vol. 35(1), 301–306 (2002). https://doi.org/10.3182/20020721-6-ES-1901.00300. IFAC World Congress
Chaudhry, A., Misovec, K., D’Andrea, R.: Low observability path planning for an unmanned air vehicle using mixed integer linear programming. In: IEEE Conference on Decision and Control, vol. 4, pp. 3823–3829. https://doi.org/10.1109/CDC.2004.1429334 (2004)
Chen, X., Chen, X.: The UAV dynamic path planning algorithm research based on voronoi diagram. In: Chinese Control and Decision Conference, Changsha, China, pp 1069–1071. IEEE (2014)
Chien, W.Y.: Stereo-camera occupancy grid mapping. Master’s thesis, Aerospace Engineering (2020)
Coutinho, W. P., Battarra, M., Fliege, J.: The unmanned aerial vehicle routing and trajectory optimisation problem, a taxonomic review. Comput. Ind. Eng. 120, 116–128 (2018)
Cui, J. Q., Lai, S., Dong, X., Chen, B. M.: Autonomous navigation of uav in foliage environment. J. Intell. Robot. Syst. 84(1), 259–276 (2016). https://doi.org/10.1007/s10846-015-0292-1
Davis, J., Perhinschi, M., Wilburn, B., Karas, O.: Development of a modified Voronoi algorithm for UAV path planning and obstacle avoidance. In: AIAA Guidance, Navigation, and Control Conference, Minneapolis, MN. https://doi.org/10.2514/6.2012-4904, pp 1–11 (2012)
De Filippis, L., Guglieri, G.: Advanced graph search algorithms for path planning of flight vehicles. pp. 157–192. Intech. https://doi.org/10.5772/37033 (2012)
Deits, R., Tedrake, R.: Computing large convex regions of obstacle-free space through semidefinite programming. In: Algorithmic Foundations of Robotics XI, pp. 109–124. Springer (2015)
Deits, R., Tedrake, R.: Efficient mixed-integer planning for UAVs in cluttered environments. In: International Conference on Robotics and Automation, pp. 42–49. IEEE (2015)
Deits, R.L.H., Tedrake, R.: IRIS-distro. https://github.com/rdeits/iris-distro.git. Last access; 01/20/2021 (2021)
Fujisawa, K., Kojima, M., Nakata, K., Yamashita, M.: SDPA SemiDefinite Programming Algorithm) user’s manual - version 6.2.0. In: Research Reports on Mathematical and Computing Sciences Series B; Operations Research, pp 1–32 (2002)
Geraerts, R., Schager, E.: Stealth-based path planning using corridor maps. In: Computer Animation and Social Agents (2010)
Han, L., Gao, F., Zhou, B., Shen, S.: FIESTA: Fast incremental Euclidean distance fields for online motion planning of aerial robots. In: International Conference on Intelligent Robots and Systems, pp. 4423–4430. https://doi.org/10.1109/IROS40897.2019.8968199 (2019)
Harabor, D. D., Grastien, A.: Online graph pruning for pathfinding on grid maps. In: AAAI Conference on Artificial Intelligence, San Francisco, CA, pp 1114–1119 (2011)
Heller, D. E.: Direct and iterative methods for block tridiagonal linear systems. Ph.D. thesis, Carnegie-Mellon University (1977)
Houska, B., Ferreau, H. J., Diehl, M.: ACADO toolkit. https://acado.github.io/ (2009)
Huang, H., Savkin, A. V., Ni, W.: A method for covert video surveillance of a car or a pedestrian by an autonomous aerial drone via trajectory planning. In: International Conference on Control, Automation and Robotics, pp 446–449. IEEE, Singapore (2020)
Isaacs, R.: Differential Games; A Mathematical Theory with Applications to Warfare and Pursuit, Control and Optimization. Mineola, NY (1999)
Jensen, S. P., Gray, S. J., Hurst, J. L.: How does habitat structure affect activity and use of space among house mice? Anim. Behav. 66(2), 239–250 (2003)
Johnson, S. G.: The NLopt nonlinear-optimization package. http://github.com/stevengj/nlopte (2020)
Kamel, M., Burri, M., Siegwart, R.: Linear vs nonlinear MPC for trajectory tracking applied to rotary wing micro aerial vehicles. IFAC-PapersOnLine 50(1), 3463–3469 (2017). https://doi.org/10.1016/j.ifacol.2017.08.849. IFAC World Congress
Kelly, M.: An introduction to trajectory optimization; How to do your own direct collocation. SIAM Rev. 59(4), 849–904 (2017). https://doi.org/10.1137/16M1062569
Koenig, S.: Likhachev, M.: D∗ lite. In: National conference on Artificial intelligence, vol. 15, pp 476–483. AAAI, Alberta (2002)
Koenig, S., Likhachev, M.: Fast replanning for navigation in unknown terrain. IEEE Trans. Robot. 21(3), 354–363 (2005)
Kögel, M., Findeisen, R.: A fast gradient method for embedded linear predictive control. IFAC Proc. Vol. 44(1), 1362–1367 (2011). https://doi.org/10.3182/20110828-6-IT-1002.03322. IFAC World Congress
Kothari, M., Postlethwaite, I.: A probabilistically robust path planning algorithm for UAVs using rapidly-exploring random trees. J. Intell. Robot. Syst. 71(2), 231–253 (2013)
Kreisselmeier, G., Steinhauser, R.: Systematic control design by optimizing a vector performance index. IFAC Proc. Vol. 12(7), 113–117 (1979). https://doi.org/10.1016/S1474-6670(17)65584-8. IFAC Symposium on computer Aided Design of Control Systems
Kwon, W., Han, S.: Receding Horizon Control; Model Predictive Control for State Models. Advanced Textbooks in Control and Signal Processing. Springer, London (2005)
L’Afflitto, A.: Differential games, continuous Lyapunov functions, and stabilisation of non-linear dynamical systems. IET Control Theory Appl. 11, 2486–2496 (2017)
L’Afflitto, A.: A Mathematical Perspective on Flight Dynamics and Control. Springer, London (2017)
L’Afflitto, A., Anderson, R.B., Mohammadi, K.: An introduction to nonlinear robust control for unmanned quadrotor aircraft. IEEE Control. Syst. Mag. 38(3), 102–121 (2018)
Landry, B., Deits, R., Florence, P. R., Tedrake, R.: Aggressive quadrotor flight through cluttered environments using mixed integer programming. In: IEEE International Conference on Robotics and Automation, pp. 1469–1475. https://doi.org/10.1109/ICRA.2016.7487282 (2016)
Latombe, J. C.: Robot Motion Planning, vol. 124. Springer, Berlin (2012)
Li, K., Wang, K., Zhang, K., Chen, B. M.: Aggressive maneuvers of a quadrotor MAV based on composite nonlinear feedback control. In: IEEE International Conference on Advanced Intelligent Mechatronics, pp. 513–518. https://doi.org/10.1109/AIM.2016.7576819 (2016)
Liu, S., Atanasov, N., Mohta, K., Kumar, V.: Search-based motion planning for quadrotors using linear quadratic minimum time control. In: International Conference on Intelligent Robots and Systems, pp 2872–2879. IEEE, Vancouver (2017)
Liu, S., Watterson, M., Mohta, K.: Decomputil. https://github.com/sikang/DecompUtil. Last access; 01/20/2021 (2021)
Liu, S., Watterson, M., Mohta, K., Sun, K., Bhattacharya, S., Taylor, C. J., Kumar, V.: Planning dynamically feasible trajectories for quadrotors using safe flight corridors in 3-D complex environments. IEEE Robot. Autom. Lett. 2(3), 1688–1695 (2017)
Liu, S., Watterson, M., Tang, S., Kumar, V.: High speed navigation for quadrotors with limited onboard sensing. In: IEEE International Conference on Robotics and Automation, Stockholm, Sweden, pp 1484–1491 (2016)
Maciejowski, J.: Predictive Control: With Constraints. Prentice Hall, Upper Saddle Hill (2002)
Madridano, A., Al-Kaff, A., Martin, D.: 3D trajectory planning method for UAVs swarm in building emergencies. Sensors 20(3), 642 (2020)
Marshall, J.A., Anderson, R.B., L’Afflitto, A.: A guidance system for a tactical autonomous unmanned aerial vehicle. https://youtu.be/6F5_QYwNJrE. Last accessed 09/04/2020 (2020)
Marzouqi, M., Jarvis, R. A.: Covert path planning for autonomous robot navigation in known environments. In: Australasian Conference on Robotics and Automation. Citeseer, Brisbane, Australia (2003)
Masehian, E., Amin-Naseri, M.: A Voronoi diagram-visibility graph-potential field compound algorithm for robot path planning. J. Robot. Syst. 21(6), 275–300 (2004)
Mattingley, J., Boyd, S.: CVXGen. https://cvxgen.com/docs/index.html. Last access; 04/19/2021 (2021)
Murty, K. G., Yu, F. T.: Linear complementarity linear and nonlinear programming, vol. 3. Heldermann, Ann Arbor (1988)
Nägeli, T., Meier, L., Domahidi, A., Alonso-Mora, J., Hilliges, O.: Real-time planning for automated multi-view drone cinematography. Trans. Graph. 36(4). https://doi.org/10.1145/3072959.3073712 (2017)
Nieuwenhuisen, M., Behnke, S.: Search-based 3D planning and trajectory optimization for safe micro aerial vehicle flight under sensor visibility constraints. In: International Conference on Robotics and Automation, pp. 9123–9129. https://doi.org/10.1109/ICRA.2019.8794086 (2019)
Niu, H., Lu, Y., Savvaris, A., Tsourdos, A.: An energy-efficient path planning algorithm for unmanned surface vehicles. Ocean Eng. 161, 308–321 (2018)
Nocedal, J., Bonnans, J.F., Mikosch, T.V., Wright, S.: Numerical Optimization. Springer, Berlin (2006)
Noreen, I., Khan, A., Ryu, H., Doh, N. L., Habib, Z.: Optimal path planning in cluttered environment using RRT∗-AB. Intell. Serv. Robot. 11(1), 41–52 (2018)
Nuske, S., Choudhury, S., Jain, S., Chambers, A., Yoder, L., Scherer, S., Chamberlain, L., Cover, H., Singh, S.: Autonomous exploration and motion planning for an unmanned aerial vehicle navigating rivers. J. Field Robot. 32(8), 1141–1162 (2015). https://doi.org/10.1002/rob.21596
Oleynikova, H., Baehnemann, R., Fehr, M., Millane, A., Lim, J., Ratnesh, M., Rosinol, T.: mav_voxblox_planning.https://github.com/ethz-asl/mav_voxblox_planning (2019)
Oleynikova, H., Lanegger, C., Taylor, Z., Pantic, M., Millane, A., Siegwart, R., Nieto, J.: An open-source system for vision-based micro-aerial vehicle mapping, planning, and flight in cluttered environments. J. Field Robot. 37(4), 642–666 (2020). https://doi.org/10.1002/rob.21950
Pehlivanoglu, Y. V.: A new vibrational genetic algorithm enhanced with a Voronoi diagram for path planning of autonomous UAV. Aerosp. Sci. Technol. 16(1), 47–55 (2012)
Pestana, J., Maurer, M., Muschick, D., Hofer, M., Fraundorfer, F.: Overview obstacle maps for obstacle-aware navigation of autonomous drones. J. Field Robot. 36(4), 734–762 (2019). https://doi.org/10.1002/rob.21863
Potra, F. A., Wright, S. J.: Interior-point methods. J. Comput. Appl. Math. 124(1), 281–302 (2000). https://doi.org/10.1016/S0377-0427(00)00433-7
Primatesta, S., Guglieri, G., Rizzo, A.: A risk-aware path planning strategy for UAVs in urban environments. J. Intell. Robot. Syst. 95(2), 629–643 (2019)
Prodan, I., Olaru, S., Bencatel, R., ao Borges de Sousa, J., Stoica, C., Niculescu, S.I.: Receding horizon flight control for trajectory tracking of autonomous aerial vehicles. Control Eng. Prac 21(10), 1334–1349 (2013). https://doi.org/10.1016/j.conengprac.2013.05.010
Radmanesh, M., Kumar, M., Guentert, P. H., Sarim, M.: Overview of path-planning and obstacle avoidance algorithms for UAVs; A comparative study. Unmanned Syst. 6(2), 95–118 (2018)
Rao, A. V.: Trajectory optimization: A survey. In: Optimization and Optimal Control in Automotive Systems, pp. 3–21. Springer (2014)
Richards, A.: Fast model predictive control with soft constraints. Eur. J. Control. 25, 51–59 (2015). https://doi.org/10.1016/j.ejcon.2015.05.003
Richter, C., Bry, A., Roy, N.: Polynomial trajectory planning for aggressive quadrotor flight in dense indoor environments. In: Robotics Research, pp. 649–666. Springer (2016)
Richter, S., Jones, C. N., Morari, M.: Real-time input-constrained MPC using fast gradient methods. In: IEEE Conference on Decision and Control, pp. 7387–7393. https://doi.org/10.1109/CDC.2009.5400619 (2009)
Richter, S., Morari, M., Jones, C. N.: Towards computational complexity certification for constrained MPC based on lagrange relaxation and the fast gradient method. In: IEEE Conference on Decision and Control and European Control Conference, pp. 5223–5229. https://doi.org/10.1109/CDC.2011.6160931 (2011)
Sahingoz, O. K.: Generation of Bezier curve-based flyable trajectories for multi-UAV systems with parallel genetic algorithm. J. Intell. Robot. Syst. 74(1-2), 499–511 (2014)
Sanchez-Lopez, J. L., Wang, M., Olivares-Mendez, M. A., Molina, M., Voos, H.: A real-time 3d path planning solution for collision-free navigation of multirotor aerial robots in dynamic environments. J. Intell. Robot. Syst. 93(1), 33–53 (2019). https://doi.org/10.1007/s10846-018-0809-5
Sethian, J.: Level Set Methods and Fast Marching Methods; Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Cambridge University Press, Cambridge (1999)
de Souza, J. P. C., Marcato, A. L. M., de Aguiar, E. P., Juca, M. A., Teixeira, A. M.: Autonomous landing of UAV based on artificial neural network supervised by fuzzy logic. J. Control Autom. Electric. Syst. 40(4), 522–531 (2019). https://doi.org/10.1007/s40313-019-00465-y
Spedicato, S., Notarstefano, G., Bülthoff, H.H., Franchi, A.: Aggressive maneuver regulation of a quadrotor UAV. In: Inaba, M., Corke, P. (eds.) Robotics Research; The 16th International Symposium ISRR, pp. 95–112. Springer. https://doi.org/10.1007/978-3-319-28872-7∖_6 (2016)
Spitzer, A., Yang, X., Yao, J., Dhawale, A., Goel, K., Dabhi, M., Collins, M., Boirum, C., Michael, N.: Fast and agile vision-based flight with teleoperation and collision avoidance on a multirotor. In: International Symposium on Experimental Robotics, pp. 524–535. Springer (2018)
Sun, W., Theodorou, E. A., Tsiotras, P.: Game theoretic continuous time differential dynamic programming. In: American Control Conference, pp. 5593–5598. https://doi.org/10.1109/ACC.2015.7172215 (2015)
Sun, W., Tsiotras, P.: Pursuit evasion game of two players under an external flow field. In: American Control Conference, pp. 5617–5622. https://doi.org/10.1109/ACC.2015.7172219 (2015)
Tal, E., Karaman, S.: Accurate tracking of aggressive quadrotor trajectories using incremental nonlinear dynamic inversion and differential flatness.IEEE Trans. Control Syst. Technol., 1–16. https://doi.org/10.1109/TCST.2020.3001117 (2020)
Tang, L., Wang, H., Liu, Z., Wang, Y.: A real-time quadrotor trajectory planning framework based on B-spline and nonuniform kinodynamic search. J. Field Robot. 38(3), 452–475 (2021). https://doi.org/10.1002/rob.21997
Thrun, S., Burgard, W., Fox, D., Arkin, R.: Probabilistic Robotic. MIT Press, Boston (2005)
Tordesillas, J., Lopez, B. T., How, J. P.: FASTER: Fast and safe trajectory planner for flights in unknown environments. In: International Conference on Intelligent Robots and Systems, pp. 1934–1940. https://doi.org/10.1109/IROS40897.2019.8968021 (2019)
Tsai, J. S. H., Huang, C. C., Guo, S. M., Shieh, L. S.: Continuous to discrete model conversion for the system with a singular system matrix based on matrix sign function. Appl. Math. Model. 35(8), 3893–3904 (2011). https://doi.org/10.1016/j.apm.2011.02.009
U.S. Army: An Infantyman’s guide to combat in built-up areas. Technical report (2013)
Vasile, M., De Pascale, P., Casotto, S.: On the optimality of a shape-based approach based on pseudo-equinoctial elements. Acta Astronaut. 61(1), 286–297 (2007). https://doi.org/10.1016/j.actaastro.2007.01.017
Verschueren, R., Frison, G., Kouzoupis, D., van Duijkeren, N., Zanelli, A., Quirynen, R., Diehl, M.: ACADOS toolkit. https://github.com/acados/acados (2018)
Verschueren, R., Frison, G., Kouzoupis, D., van Duijkeren, N., Zanelli, A., Quirynen, R., Diehl, M.: Towards a modular software package for embedded optimization. In: IFAC Conference on Nonlinear Model Predictive Control, vol. 51, pp. 374–380. https://doi.org/10.1016/j.ifacol.2018.11.062 (2018)
Votion, J., Cao, Y.: Diversity-based cooperative multivehicle path planning for risk management in costmap environments. IEEE Trans. Ind. Electron. 66(8), 6117–6127 (2018)
Wallace, R. J., Loffi, J. M.: How law enforcement unmanned aircraft systems (UAS) could improve tactical response to active shooter situations: The case of the 2017 Las Vegas shooting. Int. J. Aviat. Aeron. Aerosp. 4(4), 7 (2017)
Wang, Y., Boyd, S.: Fast model predictive control using online optimization. IEEE Trans. Control Syst. Technol. 18(2), 267–278 (2009)
Wang, Y., Boyd, S.: Fast MPC. https://web.stanford.edu/boyd/papers/fast_mpc.html (2009)
Wang, Z., Zhou, X., Xu, C., Chu, J., Gao, F.: Alternating minimization based trajectory generation for quadrotor aggressive flight. IEEE Robot. Autom. Lett. 5(3), 4836–4843 (2020). https://doi.org/10.1109/LRA.2020.3003871
Watterson, M., Liu, S., Sun, K., Smith, T., Kumar, V.: Trajectory optimization on manifolds with applications to quadrotor systems. Int. J. Robot. Res. 39(2-3), 303–320 (2020). https://doi.org/10.1177/0278364919891775
Wills, A. G., Heath, W. P.: Barrier function based model predictive control. Automatica 40 (8), 1415–1422 (2004). https://doi.org/10.1016/j.automatica.2004.03.002
Wright, S.J.: Efficient convex optimization for linear MPC. In: Raković, S.V., Levine, W.S. (eds.) Handbook of Model Predictive Control. https://doi.org/10.1007/978-3-319-77489-3∖_13, pp 287–303. Springer International Publishing, Cham (2019)
Yang, L., Qi, J., Song, D., Xiao, J., Han, J., Xia, Y.: Survey of robot 3D path planning algorithms. J. Control Sci. Eng. (2016)
Yang, Z., Fang, Z., Li, P.: Bio-inspired collision-free 4D trajectory generation for UAVs using tau strategy. J. Bionic Eng. 13(1), 84–97 (2016)
Zhang, X., Chen, J., Xin, B., Fang, H.: Online path planning for UAV using an improved differential evolution algorithm. IFAC Proc. 44(1), 6349–6354 (2011)
Zhang, Y., Cohen, J., Owens, J. D.: Fast tridiagonal solvers on the GPU. ACM Sigplan Notices 45(5), 127–136 (2010)
Zhang, Z., Wu, J., Dai, J., He, C.: A novel real-time penetration path planning algorithm for stealth UAV in 3D complex dynamic environment. IEEE Access 8, 122,757–122,771 (2020). https://doi.org/10.1109/ACCESS.2020.3007496
Zhou, B., Gao, F., Wang, L., Liu, C., Shen, S.: Robust and efficient quadrotor trajectory generation for fast autonomous flight. IEEE Robot. Autom. Lett. 4(4), 3529–3536 (2019)
Zhou, B., Pan, J., Gao, F., Shen, S.: RAPTOR: Robust and perception-aware trajectory replanning for quadrotor fast flight. IEEE Trans. Robot., 1–18. https://doi.org/10.1109/TRO.2021.3071527 (2021)
Zylberberg, J., DeWeese, M. R.: How should prey animals respond to uncertain threats? Front. Comput. Neurosci. 5, 20 (2011)
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This work was supported in part by DARPA under the Grant no. D18AP00069.
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Mr. J. A. Marshall primarily contributed to the literature review presented in Section 2, the coding of the trajectory planner presented in Section 4.2, the numerical simulations presented in Sections 7 and 9, and the flight tests presented in Section 8.
Dr. R. B. Anderson primarily contributed to the literature review presented in Section 2, the coding of the path planner presented in Section 4.1, some of the numerical simulations presented in Section 7 and some of the flight tests presented in Section 8.
Mr. W.-Y. Chien contributed to Section 5 and the coding of the navigation system employed in this work. Dr. E. N. Johnson supervised and contributed to the work performed by Mr. Chien.
Dr. A. L’Afflitto coordinated the work effort and primarily contributed to writing Section 1 and editing all other sections of this paper with the assistance of Mr. Marshall and Dr. Anderson.
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Marshall, J.A., Anderson, R.B., Chien, WY. et al. A Guidance System for Tactical Autonomous Unmanned Aerial Vehicles. J Intell Robot Syst 103, 71 (2021). https://doi.org/10.1007/s10846-021-01526-8
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DOI: https://doi.org/10.1007/s10846-021-01526-8