Challenges in traversing dynamic clutters lie mainly in the efficient perception of the environmental dynamics and the generation of evasive behaviors considering obstacle movement. Previous solutions have made progress in explicitly modeling the dynamic obstacle motion for avoidance, but this key dependency of decision-making is time-consuming and unreliable in highly dynamic scenarios with occlusions. On the contrary, without introducing object detection, tracking, and prediction, we empower the reinforcement learning (RL) with single LiDAR sensing to realize an autonomous flight system directly from point to motion. For exteroception, a depth sensing distance map achieving fixed-shape, low-resolution, and detail-safe is encoded from raw point clouds, and an environment change sensing point flow is adopted as motion features extracted from multi-frame observations. These two are integrated into a lightweight and easy-to-learn representation of complex dynamic environments. For action generation, the behavior of avoiding dynamic threats in advance is implicitly driven by the proposed change-aware sensing representation, where the policy optimization is indicated by the relative motion modulated distance field. With the deployment-friendly sensing simulation and dynamics model-free acceleration control, the proposed system shows a superior success rate and adaptability to alternatives, and the policy derived from the simulator can drive a real-world quadrotor with safe maneuvers.

Deploying robot learning methods to aerial robots in unstructured environments remains both challenging and promising. While recent advances in deep reinforcement learning (DRL) have enabled end-to-end flight control, the field still lacks systematic design guidelines and a unified infrastructure to support reproducible training and real-world deployment. We present a task-oriented framework for end-to-end DRL in quadrotors that integrates design principles for complex task specification and reveals the interdependencies among simulated task definition, training design principles, and physical deployment. Our framework involves software infrastructure, hardware platforms, and open-source firmware to support a full-stack learning infrastructure and workflow. Extensive empirical results demonstrate robust flight and sim-to-real generalization under real-world disturbances. By reducing the entry barrier for deploying learning-based controllers on aerial robots, our work lays a practical foundation for advancing autonomous flight in dynamic and unstructured environments.

Figure 1: LEARN is a lightweight, two-stage safety-guided reinforcement learning framework for multi-UA V navigation in cluttered indoor and outdoor spaces. All processes, including perception, localization, communication, planning, and control, run purely on an embedded single-core controller running at 168 MHz with 192 KB of RAM. A single policy is trained in simulation and duplicated across all quadrotors. During deployment, a minimum snap naive planner produces goal points for the encoder. Quadrotors obtain the two closest neighbor positions and velocities through radio; and obstacles are sensed using a low dimensional time-of-flight sensor. The policy generates individual normalized rotor thrusts that are sent directly to the motors. LEARN is zero-shot transferable to the real world with no fine-tuning. Experiments show that it scales up to 6 quadrotors in the real world and 24 in simulation. Abstract--Nano-UA V teams offer great agility yet face severe navigation challenges due to constrained onboard sensing, communication, and computation. Existing approaches rely on high-resolution vision or compute-intensive planners, rendering them infeasible for these platforms. All authors are with the University of Southern California. Our system combines low-resolution Time-of-Flight (T oF) sensors and a simple motion planner with a compact, attention-based RL policy. In simulation, LEARN outperforms two state-of-the-art planners by 10% while using substantially fewer resources. We demonstrate LEARN's viability on six Crazyflie quadro-tors, achieving fully onboard flight in diverse indoor and outdoor environments at speeds up to 2.0m/s and traversing 0.2m gaps. EDG-Team switches to a centralized and synchronous planner in dense environments [6]. Nmanned aerial vehicles (UA Vs) are increasingly used in domains such as surveillance [1], search and rescue [2], and planetary exploration [3]. The physics of flight impose stringent size, weight, and power (SWaP) constraints on these platforms, making efficient system design paramount. While autonomy in UA Vs has advanced significantly, many state-of-the-art navigation approaches fail to scale to resource-constrained platforms.

We present a Riccati-based framework for safety-critical nonlinear control that integrates the barrier states (BaS) methodology with the State-Dependent Riccati Equation (SDRE) approach. The BaS formulation embeds safety constraints into the system dynamics via auxiliary states, enabling safety to be treated as a control objective. To overcome the limited region of attraction in linear BaS controllers, we extend the framework to nonlinear systems using SDRE synthesis applied to the barrier-augmented dynamics and derive a matrix inequality condition that certifies forward invariance of a large region of attraction and guarantees asymptotic safe stabilization. The resulting controller is computed online via pointwise Riccati solutions. We validate the method on an unstable constrained system and cluttered quadrotor navigation tasks, demonstrating improved constraint handling, scalability, and robustness near safety boundaries. This framework offers a principled and computationally tractable solution for synthesizing nonlinear safe feedback in safety-critical environments.

This paper presents the design of a gimballed rotor mechanism as a modular and efficient solution for constructing omnidirectional quadrotors. Unlike conventional quadrotors, which are underactuated, this class of quadrotors achieves full actuation, enabling independent motion in all six degrees of freedom. While existing omnidirectional quadrotor designs often require significant structural modifications, the proposed gimballed rotor system maintains a lightweight and easy-to-integrate design by incorporating servo motors within the rotor platforms, allowing independent tilting of each rotor without major alterations to the central structure of a quadrotor. To accommodate this unconventional design, we develop a new control allocation scheme in PX4 Autopilot and present successful flight tests, validating the effectiveness of the proposed approach.

To prove Lemma 5.1, we just need to show that the Pontryagin's Maximum Principle for the auxiliary X, (S.3) and the following matrix trace properties: Tr(A) = Tr( A Since the above obtained PMP equations (S.2) are the same with the differential PMP in (13), we thus Based on Lemma 5.1 and its proof, we known that the PMP of the auxiliary control system, (S.2), is exactly the differential PMP equations (13). From (S.2c), we solve for U Proof by induction: (S.2d) shows that (S.8) holds for This completes the proof. 2 D Algorithms Details for Different Learning Modes SysID Mode, then use the learned dynamics as the initial guess in IRL/IOC Mode. In design of the quadrotor's control objective function, to achieve SE (3) maneuvering In Fig. S1, we show more detailed results of imitation loss versus iteration In Fig. S2, we show more detailed results of SysID loss versus iteration In Fig. S5, we use the On the cart-pole and robot-arm systems (in Figure 1a and Figure 1b), we learn a feedback policy by minimizing given control objective functions. In Fig. S3, we show the detailed results of control loss (i.e. the value of control objective S6, we have the following remarks. This can be seen in Fig. S3 and Fig. S6 (in Fig. S6, PDP results in a simulated trajectory which is closer to the optimal one than that This explains why PDP outperforms GPS in terms of having lower control cost (loss).

This paper explores the application of vision-based system identification techniques in quadrotor modeling and control. Through experiments and analysis, we address the complexities and limitations of quadrotor modeling, particularly in relation to thrust and drag coefficients. Grey-box modeling is employed to mitigate uncertainties, and the effectiveness of an onboard vision system is evaluated. An LQR controller is designed based on a system identification model using data from the onboard vision system. The results demonstrate consistent performance between the models, validating the efficacy of vision based system identification. This study highlights the potential of vision-based techniques in enhancing quadrotor modeling and control, contributing to improved performance and operational capabilities. Our findings provide insights into the usability and consistency of these techniques, paving the way for future research in quadrotor performance enhancement, fault detection, and decision-making processes.

Abstract-- Controller performance in quadrotor trajectory tracking depends heavily on parameter tuning, yet standard approaches often rely on fixed, manually tuned parameters that sacrifice task-specific performance. We present Trajectory-A ware Controller Optimization (T ACO), a framework that adapts controller parameters online based on the upcoming reference trajectory and current quadrotor state. T ACO employs a learned predictive model and a lightweight optimization scheme to optimize controller gains in real time with respect to a broad class of trajectories, and can also be used to adapt trajectories to improve dynamic feasibility while respecting smoothness constraints. T o enable large-scale training, we also introduce a parallelized quadrotor simulator supporting fast data collection on diverse trajectories. Experiments on a variety of trajectory types show that T ACO outperforms conventional, static parameter tuning while operating orders of magnitude faster than black-box optimization baselines, enabling practical real-time deployment on a physical quadrotor . Furthermore, we show that adapting trajectories using T ACO significantly reduces the tracking error obtained by the quadrotor .

Abstract--Visual navigation algorithms for quadrotors often exhibit a large variation in performance when transferred across different vehicle platforms and scene geometries, which increases the cost and risk of field deployment. T o support systematic early-stage evaluation, we introduce FL YINGTRUST, a high-fidelity, configurable benchmarking framework that measures how platform kinodynamics and scenario structure jointly affect navigation robustness. The benchmark pairs a diverse scenario library with a heterogeneous set of real and virtual platforms and prescribes a standardized evaluation protocol together with a composite scoring method that balances scenario importance, platform importance and performance stability. We use FL YINGTRUST to compare representative optimization-based and learning-based navigation approaches under identical conditions, performing repeated trials per platform-scenario combination and reporting uncertainty-aware metrics. The results reveal systematic patterns: navigation success depends predictably on platform capability and scene geometry, and different algorithms exhibit distinct preferences and failure modes across the evaluated conditions. These observations highlight the practical necessity of incorporating both platform capability and scenario structure into algorithm design, evaluation, and selection, and they motivate future work on methods that remain robust across diverse platforms and scenarios. NMANNED Aerial V ehicles (UA Vs) are aircraft operated without onboard human pilots, either by remote control or by preprogrammed flight plans [1]. By independently modulating the speeds of four motor-propeller units, a quadrotor can generate collective thrust for vertical motion and differential thrust and reaction torques for attitude control. These capabilities enable six degrees of freedom motion combined with fine low-speed control, which drive extensive adoption of quadrotors in precision agriculture, infrastructure inspection, high-resolution mapping, environmental monitoring and disaster response [2]-[11]. The benchmark of FL YINGTRUST is available at https://github.com/ The blue line represents the straight-line reference path, and the red curve is an example of a collision-free trajectory executed by a planner. Over the last decade, many high-performance visual navigation methods have been developed, ranging from classical optimization-based planners to recent learning-based approaches [12]-[15].

Over the past few decades, continuous quaternion-based attitude control has been proven highly effective for driving rotational systems that can be modeled as rigid bodies, such as satellites and drones. However, methods rooted in this approach do not enforce the existence of a unique closed-loop (CL) equilibrium attitude-error quaternion (AEQ); and, for rotational errors about the attitude-error Euler axis larger than πrad, their proportional-control effect diminishes as the system state moves away from the stable equilibrium of the CL rotational dynamics. In this paper, we introduce a new type of attitude control law that more effectively leverages the attitude-error Euler axis-angle information to guarantee a unique CL equilibrium AEQ and to provide greater flexibility in the use of proportional-control efforts. Furthermore, using two different control laws as examples-through the construction of a strict Lyapunov function for the CL dynamics-we demonstrate that the resulting unique equilibrium of the CL rotational system can be enforced to be uniformly asymptotically stable. To assess and demonstrate the functionality and performance of the proposed approach, we performed numerical simulations and executed dozens of real-time tumble-recovery maneuvers using a small quadrotor. These simulations and flight tests compellingly demonstrate that the proposed axis-angle-based method achieves superior flight performance-compared with that obtained using a high-performance quaternion-based controller-in terms of stabilization time.