Abstract
The ability to generate feasible and safe trajectories is crucial for autonomous multicopter systems. These trajectories can ideally be generated on low-cost, embedded computational hardware and exploit the system’s full dynamic capabilities while satisfying constraints. As operations increasingly focus on operation at high speeds, or in dynamically changing environments, strategies are required that can rapidly plan and replan trajectories. This entry reviews typical approaches for trajectory generation of aerial robots, with a focus on multicopters, and discusses various approximations that may be used to make the problem more tractable. The strategy of planning in higher derivatives of the vehicle position (such as acceleration, jerk, and snap) is discussed in depth. We also discuss the related issue of expressing system limitations and constraints in these derivatives. Finally, possible future directions are discussed.
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Bibliography
Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge, England
Brescianini D, D’Andrea R (2018) An omni-directional multirotor vehicle. Mechatronics 55:76–93
Chen J, Liu T, Shen S (2016) Online generation of collision-free trajectories for quadrotor flight in unknown cluttered environments. In: 2016 IEEE international conference on robotics and automation (ICRA). IEEE, pp 1476–1483
Falanga D, Foehn P, Lu P, Scaramuzza D (2018) PAMPC: perception-aware model predictive control for quadrotors. In: 2018 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 1–8
Ferreau HJ, Almér S, Verschueren R, Diehl M, Frick D, Domahidi A, Jerez JL, Stathopoulos G, Jones C (2017) Embedded optimization methods for industrial automatic control. IFAC-PapersOnLine 50(1): 13194–13209
Fliess M, Lévine J, Martin P, Rouchon P (1995) Flatness and defect of non-linear systems: introductory theory and examples. Int J Control 61(6):1327–1361
Hehn M, D’Andrea R (2014) A frequency domain iterative learning algorithm for high-performance, periodic quadrocopter maneuvers. Mechatronics 24(8):954–965
Hehn M, Ritz R, D’Andrea R (2012) Performance benchmarking of quadrotor systems using time-optimal control. Auton Robots 33:69–88
Holda C, Ghalamchi B, Mueller MW (2018) Tilting multicopter rotors for increased power efficiency and yaw authority. IEEE, pp 143–148
Houska B, Ferreau H, Diehl M (2011) ACADO toolkit – an open source framework for automatic control and dynamic optimization. Optimal Control Appl Methods 32(3):298–312
Jiang G, Voyles R (2014) A nonparallel hexrotor UAV with faster response to disturbances for precision position keeping. In: 2014 IEEE international symposium on safety, security, and rescue robotics. IEEE, pp 1–5
Kalmár-Nagy T, D’Andrea R, Ganguly P (2004) Near-optimal dynamic trajectory generation and control of an omnidirectional vehicle. Robot Auton Syst 46(1):47–64
Mahony R, Kumar V, Corke P (2012) Aerial vehicles: modeling, estimation, and control of quadrotor. IEEE Robot Autom Magaz 19(3):20–32
Mellinger D, Kumar V (2011) Minimum snap trajectory generation and control for quadrotors. In: IEEE international conference on robotics and automation (ICRA), pp 2520–2525
Mueller MW, D’Andrea R (2013) A model predictive controller for quadrocopter state interception. In: European control conference, pp 1383–1389
Mueller MW, D’Andrea R (2016) Relaxed hover solutions for multicopters: application to algorithmic redundancy and novel vehicles. Int J Robot Res 35(8):873–889
Mueller MW, Hehn M, D’Andrea R (2015) A computationally efficient motion primitive for quadrocopter trajectory generation. IEEE Trans Robot 31(6): 1294–1310
Murray RM, Rathinam M, Sluis W (1995) Differential flatness of mechanical control systems: a catalog of prototype systems. In: ASME international mechanical engineering congress and exposition
Nägeli T, Alonso-Mora J, Domahidi A, Rus D, Hilliges O (2017) Real-time motion planning for aerial videography with dynamic obstacle avoidance and viewpoint optimization. IEEE Robot Autom Lett 2(3):1696–1703
Papachristos C, Khattak S, Alexis K (2017) Uncertainty-aware receding horizon exploration and mapping using aerial robots. In: 2017 IEEE international conference on robotics and automation (ICRA). IEEE, pp 4568–4575
Tagliabue A, Wu X, Mueller MW (2019) Model-free online motion adaptation for optimal range and endurance of multicopters. In: IEEE international conference on robotics and automation (ICRA)
Ware J, Roy N (2016) An analysis of wind field estimation and exploitation for quadrotor flight in the urban canopy layer. In: 2016 IEEE international conference on robotics and automation (ICRA). IEEE, pp 1507–1514
Zhou K, Doyle JC (1998) Essentials of robust control, vol 104. Prentice Hall, Upper Saddle River
Zhou S, Helwa MK, Schoellig AP (2017) Design of deep neural networks as add-on blocks for improving impromptu trajectory tracking. In: 2017 IEEE 56th annual conference on decision and control (CDC). IEEE, pp 5201–5207
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Mueller, M.W., D’Andrea, R. (2020). Trajectory Generation for Aerial Multicopters. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_100037-1
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DOI: https://doi.org/10.1007/978-1-4471-5102-9_100037-1
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