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1. Freyja: A Full Multirotor System for Agile and Precise Outdoor Flights

   Shankar, A. ; Elbaum, S. ; Detweiler, C. ( May 2021 , IEEE International Conference on Robotics and Automation)

   null (Ed.)

   Several independent approaches exist for state estimation and control of multirotor unmanned aerial systems (UASs) that address specific and constrained operational conditions. This work presents a complete end-to-end pipeline that enables precise, aggressive and agile maneuvers for multirotor UASs under real and challenging outdoor environments. We leverage state-of-the-art optimal methods from the literature for trajectory planning and control, such that designing and executing dynamic paths is fast, robust and easy to customize for a particular application. The complete pipeline, built entirely using commercially available components, is made open-source and fully documented to facilitate adoption. We demonstrate its performance in a variety of operational settings, such as hovering at a spot under dynamic wind speeds of up to 5–6 m/s (12–15 mi/h) while staying within 12 cm of 3D error. We also characterize its capabilities in flying high-speed trajectories outdoors, and enabling fast aerial docking with a moving target with planning and interception occurring in under 8 s. 

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2. Multirotor Docking with an Airborne Platform

   https://doi.org/10.1007/978-3-030-71151-1_5  

   Shankar, A. ; Elbaum, S. ; Detweiler, C. ( March 2021 , Springer Proceedings in Advanced Robotics)

   Siciliano, B. ; Laschi, C. ; Khatib, O. (Ed.)

   Multirotor systems have traditionally been employed for missions that ensure minimal contact with the objects in their vicinity. However, their agile flight dynamics lets them sense, plan and react rapidly, and therefore perform highly dynamic missions. In this work, we push their operational envelope further by developing a complete framework that allows a multirotor to dock with a moving platform. Our approach builds on state-of-the-art and optimal methods for estimating and predicting the state of the moving platform, as well as for generating interception trajectories for the docking multirotor. Through a total of 25 field tests outdoors, we demonstrate the capabilities of our system in docking with a platform moving at different speeds and in various operating conditions. We also evaluate the quality of our system’s trajectory following at speeds over 2 m/s to effect docking within 10 s. 

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3. A High-Gain Observer Approach to Robust Trajectory Estimation and Tracking for a Multirotor Unmanned Aerial Vehicle

   https://doi.org/10.1115/1.4065758  

   Boss, Connor J ; Srivastava, Vaibhav ( January 2025 , Journal of Dynamic Systems, Measurement, and Control)

   Using the context of trajectory estimation and tracking for multirotor unmanned aerial vehicles (UAVs), we explore the challenges in applying high-gain observers to highly dynamic systems. The multirotor will operate in the presence of external disturbances and modeling errors. At the same time, the reference trajectory is unknown and generated from a reference system with unknown or partially known dynamics. We assume the only measurements that are available are the position and orientation of the multirotor and the position of the reference system. We adopt an extended high-gain observer (EHGO) estimation framework to estimate the unmeasured multirotor states, modeling errors, external disturbances, and the reference trajectory. We design a robust output feedback controller for trajectory tracking that comprises a feedback linearizing controller and the EHGO. The proposed control method is rigorously analyzed to establish its stability properties. Finally, we illustrate our theoretical results through numerical simulation and experimental validation in which a multirotor tracks a moving ground vehicle with an unknown trajectory and dynamics and successfully lands on the vehicle while in motion.

    

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4. MonoNav: MAV Navigation via Monocular Depth Estimation and Reconstruction

   Simon, Nathaniel ; Majumdar, Anirudha ( November 2023 , International Symposium on Experimental Robotics (ISER))

   A major challenge in deploying the smallest of Micro Aerial Vehicle (MAV) platforms (< 100 g) is their inability to carry sensors that provide high-resolution metric depth information (e.g., LiDAR or stereo cameras). Current systems rely on end-to-end learning or heuristic approaches that directly map images to control inputs, and struggle to fly fast in unknown environments. In this work, we ask the following question: using only a monocular camera, optical odometry, and offboard computation, can we create metrically accurate maps to leverage the powerful path planning and navigation approaches employed by larger state-of-the-art robotic systems to achieve robust autonomy in unknown environments? We present MonoNav: a fast 3D reconstruction and navigation stack for MAVs that leverages recent advances in depth prediction neural networks to enable metrically accurate 3D scene reconstruction from a stream of monocular images and poses. MonoNav uses off-the-shelf pre-trained monocular depth estimation and fusion techniques to construct a map, then searches over motion primitives to plan a collision-free trajectory to the goal. In extensive hardware experiments, we demonstrate how MonoNav enables the Crazyflie (a 37 g MAV) to navigate fast (0.5 m/s) in cluttered indoor environments. We evaluate MonoNav against a state-of-the-art end-to-end approach, and find that the collision rate in navigation is significantly reduced (by a factor of 4). This increased safety comes at the cost of conservatism in terms of a 22% reduction in goal completion. 

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5. Adaptive MPPI Architecture for Robust and Agile Control of Multirotors

   https://doi.org/10.1109/IROS45743.2020.9341154  

   Pravitra, Jintasit ; Ackerman, Kasey A. ; Cao, Chengyu ; Hovakimyan, Naira ; Theodorou, Evangelos A. ( October 2020 , IEEE International Workshop on Intelligent Robots and Systems)

   null (Ed.)

   This paper presents a multirotor control architecture, where Model Predictive Path Integral Control (MPPI) and ℒ 1 adaptive control are combined to achieve both fast model predictive trajectory planning and robust trajectory tracking. MPPI provides a framework to solve nonlinear MPC with complex cost functions in real-time. However, it often lacks robustness, especially when the simulated dynamics are different from the true dynamics. We show that the ℒ 1 adaptive controller robustifies the architecture, allowing the overall system to behave similar to the nominal system simulated with MPPI. The architecture is validated in a simulated multirotor racing environment. 

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