Near Time-Optimal Trajectory Generation for Multirotors using Numerical Optimization and Safe Corridors
Trajectory generation is a fundamental problem for every type of robot. In most applications, the robots should reach their goals in the minimum time possible. Time-optimal trajectory generation allows us to solve this problem. The generation of such trajectories for multirotors has gained traction...
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| Published in | Journal of intelligent & robotic systems Vol. 105; no. 1; p. 20 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.05.2022
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0921-0296 1573-0409 |
| DOI | 10.1007/s10846-022-01625-0 |
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| Abstract | Trajectory generation is a fundamental problem for every type of robot. In most applications, the robots should reach their goals in the minimum time possible. Time-optimal trajectory generation allows us to solve this problem. The generation of such trajectories for multirotors has gained traction with new applications in transport, delivery and search and rescue missions, as well as other applications in sports and entertainment such as drone racing. The current state-of-the-art is heavily based on polynomial methods and most methods choose a conservative approach when limiting the velocity or acceleration as a way to account for nonlinearities and guarantee feasibility, which limits time optimality and trajectory speed. We overcome this limitation by proposing a new formulation for multirotors trajectory generation that takes into account nonlinearities such as gravity and aerodynamic drag, It allows us to provide more time-optimal solutions then the state-of-the-art. We present an algorithm that uses our new formulation for near time-optimal trajectory generation for multirotors subject to obstacles/path constraints. We validate our approach using a state of the art simulator and compare it with other time-optimal trajectory generation methods. |
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| AbstractList | Trajectory generation is a fundamental problem for every type of robot. In most applications, the robots should reach their goals in the minimum time possible. Time-optimal trajectory generation allows us to solve this problem. The generation of such trajectories for multirotors has gained traction with new applications in transport, delivery and search and rescue missions, as well as other applications in sports and entertainment such as drone racing. The current state-of-the-art is heavily based on polynomial methods and most methods choose a conservative approach when limiting the velocity or acceleration as a way to account for nonlinearities and guarantee feasibility, which limits time optimality and trajectory speed. We overcome this limitation by proposing a new formulation for multirotors trajectory generation that takes into account nonlinearities such as gravity and aerodynamic drag, It allows us to provide more time-optimal solutions then the state-of-the-art. We present an algorithm that uses our new formulation for near time-optimal trajectory generation for multirotors subject to obstacles/path constraints. We validate our approach using a state of the art simulator and compare it with other time-optimal trajectory generation methods. |
| ArticleNumber | 20 |
| Author | Toumieh, Charbel Lambert, Alain |
| Author_xml | – sequence: 1 givenname: Charbel orcidid: 0000-0002-2669-6777 surname: Toumieh fullname: Toumieh, Charbel email: charbel.toumieh@universite-paris-saclay.fr organization: CNRS, Laboratoire de recherche en informatique, Université Paris-Saclay – sequence: 2 givenname: Alain surname: Lambert fullname: Lambert, Alain organization: CNRS, Laboratoire de recherche en informatique, Université Paris-Saclay |
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| Cites_doi | 10.1109/TCYB.2021.3091680 10.1109/TCST.2017.2709268 10.1109/LRA.2017.2663526 10.1109/IROS40897.2019.8968116 10.1007/978-3-319-27702-8_29 10.1109/IROS.2015.7353622 10.1016/j.ifacol.2018.11.062 10.1016/j.ifacol.2017.08.849 10.1007/0-387-30065-1_4 10.1109/ICRA.2016.7487281 10.3182/20110828-6-IT-1002.03178 10.1109/ICUAS.2016.7502532 10.1007/s12532-018-0139-4 10.1109/ICRA.2011.5980409 10.1109/IROS.2018.8593579 10.1109/IROS.2017.8206119 10.1007/978-3-319-28872-7_37 10.1007/978-3-319-67361-5_40 |
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| Copyright | The Author(s), under exclusive licence to Springer Nature B.V. 2022 Copyright Springer Nature B.V. May 2022 |
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| References_xml | – reference: Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation (In Press) (2018) – reference: Beul, M., Behnke, S.: Analytical time-optimal trajectory generation and control for multirotors. In: 2016 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 87–96. IEEE (2016) – reference: Verschueren, R., Frison, G., Kouzoupis, D., van Duijkeren, N., Zanelli, A., Quirynen, R., Diehl, M.: Towards a modular software package for embedded optimization. In: Proceedings of the IFAC Conference on Nonlinear Model Predictive Control (NMPC) (2018) – reference: 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) – reference: Bircher, A., Kamel, M., Alexis, K., Oleynikova, H., Siegwart, R.: Receding horizon path planning for 3d exploration and surface inspection. Auton. Robot., pp 1–16 (2016) – reference: Mellinger, D., Kumar, V.: Minimum snap trajectory generation and control for quadrotors. In: 2011 IEEE International Conference on Robotics and Automation, pp. 2520–2525. IEEE (2011) – reference: Madaan, R., Gyde, N., Vemprala, S., Brown, M., Nagami, K., Taubner, T., Cristofalo, E., Scaramuzza, D., Schwager, M., Kapoor, A.: Airsim drone racing lab (2020) – reference: Oettershagen, P., Stastny, T., Mantel, T., Melzer, A., Rudin, K., Gohl, P., Agamennoni, G., Alexis, K., Siegwart, R.: Long-endurance sensing and mapping using a hand-launchable solar-powered uav. Field and Service Robotics, pp. 441–454 (2016) – reference: Microsoft-Airsim: Game of drones competition. https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing, Accessed 2019-09-14 – reference: HehnMD’AndreaRQuadrocopter trajectory generation and controlIFAC Proceedings201144114851491 – reference: Bircher, A., Kamel, M., Alexis, K., Oleynikova, H., Siegwart, R: Receding horizon “next-best-view” planner for 3d exploration. IEEE International Conference on Robotics and Automation (ICRA) (2016) – reference: Foehn, P., Scaramuzza, D.: Cpc: Complementary progress constraints for time-optimal quadrotor trajectories. arXiv preprint arXiv:2007.06255 (2020) – reference: Nocedal, J.: Knitro: an Integrated Package for Nonlinear Optimization. In: Large-Scale Nonlinear Optimization, pp. 35–60. Springer (2006) – reference: Guerra, W., Tal, E., Murali, V., Ryou, G., Karaman, S.: Flightgoggles: Photorealistic sensor simulation for perception-driven robotics using photogrammetry and virtual reality. arXiv preprint arXiv:1905.11377 (2019) – reference: BonamiPLeeJBonmin user’s manualNumer. Math20074132 – reference: Shah, S., Dey, D., Lovett, C., Kapoor, A.: Airsim: High-fidelity visual and physical simulation for autonomous vehicles. In: Field and Service Robotics. https://arxiv.org/abs/1705.05065 (2017) – reference: LiuSWattersonMMohtaKSunKBhattacharyaSTaylorCJKumarVPlanning dynamically feasible trajectories for quadrotors using safe flight corridors in 3-d complex environmentsIEEE Robot. Autom. Lett.2017231688169510.1109/LRA.2017.2663526 – reference: Gao, F., Wu, W., Pan, J., Zhou, B., Shen, S.: Optimal time allocation for quadrotor trajectory generation. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4715–4722. IEEE (2018) – reference: SpedicatoSNotarstefanoGMinimum-time trajectory generation for quadrotors in constrained environmentsIEEE Trans. Control Syst. Technol.20172641335134410.1109/TCST.2017.2709268 – reference: Lockheed-Martin: Alphapilot – lockheed martin ai drone racing innovation challenge. https://www.herox.com/alphapilot, accessed 2019-09-14 – reference: Tutsoy, O., Barkana, D.E., Balikci, K.: A novel exploration-exploitation-based adaptive law for intelligent model-free control approaches. IEEE Trans. Cybern., pp. 1–9. https://doi.org/10.1109/TCYB.2021.3091680 (2021) – reference: KamelMBurriMSiegwartRLinear vs nonlinear mpc for trajectory tracking applied to rotary wing micro aerial vehiclesIFAC-PapersOnLine20175013463346910.1016/j.ifacol.2017.08.849 – reference: Burri, M., Oleynikova, H., Achtelik, M. W., Siegwart, R.: Real-time visual-inertial mapping, re-localization and planning onboard mavs in unknown environments. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1872–1878. IEEE (2015) – reference: Liu, S., Atanasov, N., Mohta, K., Kumar, V.: Search-based motion planning for quadrotors using linear quadratic minimum time control. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2872–2879. IEEE (2017) – reference: Wächter, A., Biegler, L.: Ipopt-an interior point optimizer (2009) – ident: 1625_CR14 – ident: 1625_CR23 doi: 10.1109/TCYB.2021.3091680 – volume: 26 start-page: 1335 issue: 4 year: 2017 ident: 1625_CR22 publication-title: IEEE Trans. Control Syst. Technol. doi: 10.1109/TCST.2017.2709268 – volume: 2 start-page: 1688 issue: 3 year: 2017 ident: 1625_CR13 publication-title: IEEE Robot. Autom. Lett. doi: 10.1109/LRA.2017.2663526 – ident: 1625_CR9 doi: 10.1109/IROS40897.2019.8968116 – ident: 1625_CR7 – ident: 1625_CR19 doi: 10.1007/978-3-319-27702-8_29 – volume: 4 start-page: 1 year: 2007 ident: 1625_CR5 publication-title: Numer. Math – ident: 1625_CR6 doi: 10.1109/IROS.2015.7353622 – ident: 1625_CR24 doi: 10.1016/j.ifacol.2018.11.062 – volume: 50 start-page: 3463 issue: 1 year: 2017 ident: 1625_CR11 publication-title: IFAC-PapersOnLine doi: 10.1016/j.ifacol.2017.08.849 – ident: 1625_CR18 doi: 10.1007/0-387-30065-1_4 – ident: 1625_CR3 doi: 10.1109/ICRA.2016.7487281 – volume: 44 start-page: 1485 issue: 1 year: 2011 ident: 1625_CR10 publication-title: IFAC Proceedings doi: 10.3182/20110828-6-IT-1002.03178 – ident: 1625_CR2 doi: 10.1109/ICUAS.2016.7502532 – ident: 1625_CR1 doi: 10.1007/s12532-018-0139-4 – ident: 1625_CR16 doi: 10.1109/ICRA.2011.5980409 – ident: 1625_CR8 doi: 10.1109/IROS.2018.8593579 – ident: 1625_CR12 doi: 10.1109/IROS.2017.8206119 – ident: 1625_CR25 – ident: 1625_CR17 – ident: 1625_CR15 – ident: 1625_CR20 doi: 10.1007/978-3-319-28872-7_37 – ident: 1625_CR21 doi: 10.1007/978-3-319-67361-5_40 – ident: 1625_CR4 doi: 10.1109/ICRA.2016.7487281 |
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| SubjectTerms | Acceleration Aerodynamic drag Algorithms Artificial Intelligence Control Electrical Engineering Engineering Evacuations & rescues Gravity Mechanical Engineering Mechatronics Nonlinearity Optimization Polynomials Racing Robotics Robots Search and rescue missions Short Paper Topical collection on Unmanned Systems Trajectory optimization Transportation corridors |
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| Title | Near Time-Optimal Trajectory Generation for Multirotors using Numerical Optimization and Safe Corridors |
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