Aerial Robotic Autonomy: Deep-dive Topics (2024)

Disclaimer: This is a partially biased literature. Certain aspects of it focus on work of the lab with the purpose to be able to provide course students with code implementations of the work. ​

Multirotor Modeling

  • Mahony, R., Kumar, V. and Corke, P., 2012. Multirotor aerial vehicles: Modeling, estimation, and control of quadrotor. IEEE robotics & automation magazine, 19(3), pp.20-32.
  • Papachristos, C., Dang, T., Khattak, S., Mascarich, F., Khedekar, N. and Alexis, K., 2018. Modeling, control, state estimation and path planning methods for autonomous multirotor aerial robots. Foundations and Trends® in Robotics, 7(3), pp.180-250.
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State Estimation

Prime resources for State Estimation for Robotics:
  • Barfoot, T.D., 2017. State estimation for robotics. Cambridge University Press.
  • Kay, S.M., 1993. Fundamentals of statistical signal processing: estimation theory. Prentice-Hall, Inc..
  • Särkkä, S., 2013. Bayesian filtering and smoothing (No. 3). Cambridge University Press.
  • Sola, J., Deray, J. and Atchuthan, D., 2018. A micro Lie theory for state estimation in robotics. arXiv preprint arXiv:1812.01537.
  • Chirikjian, G.S., 2009. Stochastic Models, Information Theory, and Lie Groups, Volume 1: Classical Results and Geometric Methods. Springer Science & Business Media.
  • Absil, P.A., Mahony, R. and Sepulchre, R., 2009. Optimization algorithms on matrix manifolds. Princeton University Press.
  • Koller, D. and Friedman, N., 2009. Probabilistic graphical models: principles and techniques. MIT press.
  • Dellaert, F. and Kaess, M., 2017. Factor graphs for robot perception. Foundations and Trends® in Robotics, 6(1-2), pp.1-139.
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Inertial Measurement Unit (IMU) Modeling

  • Trawny, N. and Roumeliotis, S.I., 2005. Indirect Kalman filter for 3D attitude estimation. University of Minnesota, Dept. of Comp. Sci. & Eng., Tech. Rep, 2, p.2005.
  • Woodman, O.J., 2007. An introduction to inertial navigation (No. UCAM-CL-TR-696). University of Cambridge, Computer Laboratory.
  • Goel, A., Islam, A.U., Ansari, A., Kouba, O. and Bernstein, D.S., 2021. An Introduction to Inertial Navigation From the Perspective of State Estimation [Focus on Education]. IEEE Control Systems Magazine, 41(5), pp.104-128.

Visual-Inertial Odometry

  • Sa, I., Kamel, M., Burri, M., Bloesch, M., Khanna, R., Popović, M., Nieto, J. and Siegwart, R., 2017. Build your own visual-inertial drone: A cost-effective and open-source autonomous drone. IEEE Robotics & Automation Magazine, 25(1), pp.89-103.
  • Von Stumberg, L., Usenko, V. and Cremers, D., 2018, May. Direct sparse visual-inertial odometry using dynamic marginalization. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2510-2517). IEEE.
  • Engel, J., Koltun, V. and Cremers, D., 2017. Direct sparse odometry. IEEE transactions on pattern analysis and machine intelligence, 40(3), pp.611-625.
  • Khattak, S., Papachristos, C. and Alexis, K., 2020. Keyframe‐based thermal–inertial odometry. Journal of Field Robotics, 37(4), pp.552-579.
  • Leutenegger, S., Lynen, S., Bosse, M., Siegwart, R. and Furgale, P., 2015. Keyframe-based visual–inertial odometry using nonlinear optimization. The International Journal of Robotics Research, 34(3), pp.314-334.
  • Bloesch, M., Omari, S., Hutter, M. and Siegwart, R., 2015, September. Robust visual inertial odometry using a direct EKF-based approach. In 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 298-304). IEEE.
  • Forster, C., Pizzoli, M. and Scaramuzza, D., 2014, May. SVO: Fast semi-direct monocular visual odometry. In 2014 IEEE international conference on robotics and automation (ICRA) (pp. 15-22). IEEE.
  • Scaramuzza, D. and Fraundorfer, F., 2011. Visual odometry [tutorial]. IEEE robotics & automation magazine, 18(4), pp.80-92.
  • Fraundorfer, F. and Scaramuzza, D., 2012. Visual odometry: Part ii: Matching, robustness, optimization, and applications. IEEE Robotics & Automation Magazine, 19(2), pp.78-90.

Control

  • Lee, T., Leok, M. and McClamroch, N.H., 2010, December. Geometric tracking control of a quadrotor UAV on SE (3). In 49th IEEE conference on decision and control (CDC) (pp. 5420-5425). IEEE.
  • Kamel, M., Alexis, K., Achtelik, M. and Siegwart, R., 2015, September. Fast nonlinear model predictive control for multicopter attitude tracking on SO (3). In 2015 IEEE conference on control applications (CCA) (pp. 1160-1166). IEEE.
  • Romero, A., Sun, S., Foehn, P. and Scaramuzza, D., 2022. Model predictive contouring control for time-optimal quadrotor flight. IEEE Transactions on Robotics, 38(6), pp.3340-3356.
  • Kamel, M., Stastny, T., Alexis, K. and Siegwart, R., 2017. Model predictive control for trajectory tracking of unmanned aerial vehicles using robot operating system. Robot Operating System (ROS) The Complete Reference (Volume 2), pp.3-39.


Robot Learning

  • François-Lavet, V., Henderson, P., Islam, R., Bellemare, M.G. and Pineau, J., 2018. An introduction to deep reinforcement learning. Foundations and Trends® in Machine Learning, 11(3-4), pp.219-354.
  • Kaufmann, E., Bauersfeld, L., Loquercio, A., Müller, M., Koltun, V. and Scaramuzza, D., 2023. Champion-level drone racing using deep reinforcement learning. Nature, 620(7976), pp.982-987.
  • Schulman, J., Wolski, F., Dhariwal, P., Radford, A. and Klimov, O., 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
  • Kulkarni, M. and Alexis, K., 2024. Reinforcement learning for collision-free flight exploiting deep collision encoding. arXiv preprint arXiv:2402.03947.
  • Kober, J., Bagnell, J.A. and Peters, J., 2013. Reinforcement learning in robotics: A survey. The International Journal of Robotics Research, 32(11), pp.1238-1274.
  • Brunke, L., Greeff, M., Hall, A.W., Yuan, Z., Zhou, S., Panerati, J. and Schoellig, A.P., 2022. Safe learning in robotics: From learning-based control to safe reinforcement learning. Annual Review of Control, Robotics, and Autonomous Systems, 5(1), pp.411-444.
  • Kulkarni, M., Nguyen, H. and Alexis, K., 2023, October. Semantically-enhanced deep collision prediction for autonomous navigation using aerial robots. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3056-3063). IEEE.
  • Nguyen, H., Andersen, R., Boukas, E. and Alexis, K., 2024. Uncertainty-aware visually-attentive navigation using deep neural networks. The International Journal of Robotics Research, 43(6), pp.840-872.
  • Harms, M., Kulkarni, M., Khedekar, N., Jacquet, M. and Alexis, K., 2024. Neural Control Barrier Functions for Safe Navigation. arXiv preprint arXiv:2407.19907.


Path Planning

  • LaValle, S., 1998. Rapidly-exploring random trees: A new tool for path planning. Research Report 9811.
  • Karaman, S. and Frazzoli, E., 2011. Incremental sampling-based algorithms for optimal motion planning.
  • Dang, T., Tranzatto, M., Khattak, S., Mascarich, F., Alexis, K. and Hutter, M., 2020. Graph‐based subterranean exploration path planning using aerial and legged robots. Journal of Field Robotics, 37(8), pp.1363-1388.
  • Kulkarni, M., Dharmadhikari, M., Tranzatto, M., Zimmermann, S., Reijgwart, V., De Petris, P., Nguyen, H., Khedekar, N., Papachristos, C., Ott, L. and Siegwart, R., 2022, May. Autonomous teamed exploration of subterranean environments using legged and aerial robots. In 2022 International Conference on Robotics and Automation (ICRA) (pp. 3306-3313). IEEE.
  • Dharmadhikari, M., Dang, T., Solanka, L., Loje, J., Nguyen, H., Khedekar, N. and Alexis, K., 2020, May. Motion primitives-based path planning for fast and agile exploration using aerial robots. In 2020 IEEE International Conference on Robotics and Automation (ICRA) (pp. 179-185). IEEE.
  • Bircher, A., Kamel, M., Alexis, K., Oleynikova, H. and Siegwart, R., 2016, May. Receding horizon" next-best-view" planner for 3d exploration. In 2016 IEEE international conference on robotics and automation (ICRA) (pp. 1462-1468). IEEE.


NB! The course material is currently being added!