Autonomous exploration in unknown environments is a crucial challenge for various applications of unmanned aerial vehicles (UAVs). However, in large-scale scenarios, existing methods suffer from inefficient environmental information acquisition, computationally expensive exploration planning, and inconsistent motion. In this work, we present a novel method for rapid UAV autonomous exploration in large-scale environments. We develop a surface frontier guided viewpoints generation strategy that supports efficient coverage of scenario. Besides, we introduce an incremental viewpoint clustering method to approximate distant viewpoints using fewer anchor points, decreasing the computational costs of exploration tour planning. Building upon this, we propose a history-informed tour planning method that incorporates information from previous tour into the optimization process, maintaining motion consistency. Extensive simulation experiments validate that our method outperforms existing state-of-the-art methods in terms of exploration time, travel distance, and run time. Various real-world experiments are conducted to indicate the practicality of our approach. The source code will be released to benefit the community1.

Autonomous exploration is essential for the effective deployment of quadrotors in various applications. However, existing approaches face significant challenges in large-scale environments, particularly in balancing global coverage efficiency and computational overhead. These limitations often result in poor adaptability to environmental changes and redundant revisits to previously explored areas, reducing overall exploration efficiency. To address these issues, we propose FASTEX, a fast UAV exploration framework designed for large-scale environments, using dynamically expanding grids and coverage paths to improve exploration efficiency. To support efficient exploration planning in large-scale scenarios, we introduce an efficient environment preprocessing method, including a dynamic grid expansion mechanism and a sparse roadmap. Furthermore, we present a hierarchical exploration planning framework that integrates an incremental global planner with a local planner, ensuring high coverage and computational efficiency. Extensive simulation tests demonstrate the superior performance and robustness of the proposed method compared to the state-of-the-art methods. In addition, we conduct various real-world experiments to validate the feasibility of our autonomous exploration system.

In agile quadrotor flight, accurately modeling the varying aerodynamic drag forces encountered at different speeds is critical. These drag forces significantly impact the performance and maneuverability of the UAV, especially during high-speed maneuvers. Traditional control models based on first principles struggle to capture these dynamics due to the complexity and variability of aerodynamic effects, which are challenging to model accurately. To address these challenges, this study proposes a meta-learning-based control strategy for accurately modeling quadrotor dynamics under varying speeds, treating each velocity condition as an independent learning task with a specifically trained neural network to ensure precise dynamic predictions. The meta-learning framework rapidly generates task-specific parameters adapted to speed variations by solving an optimization problem and employs an online incremental learning strategy to integrate real-time data for continuous model updates, enhancing system robustness. Regularization is introduced to prevent overfitting and improve generalizability. The integration of the meta-learned model into Model Predictive Contouring Control (MPCC) allows the system to achieve optimal control across different velocity levels, ensuring efficient and accurate flight control even during sharp turns and high-speed maneuvers. Extensive simulations and real-world experiments confirm that the proposed algorithm maintains a high level of control precision despite the nonlinear effects of rapid speed changes, complex flight trajectories and wind disturbances. The results highlight the advantages of combining meta-learning with adaptive control strategies, providing a robust framework for quadrotors operating in diverse and dynamic environments.

The article presents an air-assisted ground robotic autonomous exploration framework, which leverages the high mobility and wide aerial perspective of unmanned aerial vehicles (UAVs) to assist unmanned ground vehicles (UGVs) in detailed exploration, enhancing exploration efficiency and improving the quality of point cloud collection in regions of interest in large-scale, unknown environments. In this framework, the UAV, equipped with an onboard RGB camera, rapidly surveys large unknown areas and generates a bird's eye view (BEV) to identify critical zones for UGV exploration. With prior information about the unexplored area's outline from the real-time shared BEV, the UGV can carry out more efficient and informed exploration from a global perspective. To maximize the utility of this prior information and optimize point cloud collection, a hierarchical exploration strategy and an attention mechanism are incorporated to guide the UGV's focus toward areas requiring detailed mapping, rather than broad, featureless regions. Real-world experiments validate the effectiveness of the framework, demonstrating significant improvements in exploration efficiency and point cloud collection compared to state-of-the-art methods. The results further show that even with a coarse BEV, the UGV's exploration efficiency is greatly enhanced.

Autonomous exploration plays a crucial role in robotics applications like rescue and scene reconstruction. This work addresses the challenges of autonomous exploration in intricate unknown environments by presenting a novel UAV autonomous exploration method based on a new concept of the view-region. Our proposed approach leverages the view-region to replace the conventional viewpoint generation and selection process, streamlining the planning process for exploration. Simultaneously, we model the problem of maximizing frontier coverage within the field of view during exploration, and jointly optimize it with the exploration path optimization problem. This approach ensures exploration path safety and effectiveness while being aggressive. Additionally, a gimbal is incorporated beneath the camera, with an associated optimization problem designed to minimize UAV self-rotation and enhance exploration efficiency. Simulations and real-world experiments demonstrate that the proposed method outperforms existing state-of-the-art methods in terms of runtime and distance traveled.

This paper presents a safe learning control strategy aimed at ensuring the accurate tracking of multiple unmanned aerial vehicles (UAVs) along their predetermined trajectories while also guaranteeing safety under uncertain environments such as trajectory conflict, airflow interference between UAVs, and external disturbances. The proposed control framework employs a high-level learning-based feedback linearization control combined with model predictive control (LB-FBL-MPC), coupled with a low-level safety barrier certificates and control Lyapunov function-based quadratic programs (SC), for nonlinear multiple-UAV systems. The high-level LB-FBL-MPC uses incremental Gaussian processes (IGPs) to learn uncertain disturbances online, and feedback linearization is applied to approximate the linear system. The MPC optimizes the reference trajectory based on the linearized dynamical model to enhance the adaptivity of the system. Furthermore, the low-level SC guarantees the safety and asymptotic stability of the multi-UAV system by using the prediction distribution of the IGPs. Ablation and benchmark comparison experiments demonstrate the efficacy of the proposed tracking control strategy.Note to Practitioners—Controlling multiple unmanned systems to achieve precision and safety in complex environmental disturbances is a challenge. Existing machine learning-based control frameworks are mostly limited by low learning efficiency and poor interpretability, making it difficult to deploy them in practical robot systems. This article introduces a machine learning and control theory combined framework for the safe control of multiple unmanned aerial vehicles. On the one hand, the framework enables UAVs to quickly learn uncertain environmental disturbances without the need for any pre-collected data. The use of a linearized system model greatly reduces computation time and provides a new approach for practical engineering deployment. On the other hand, by designing separate quadratic programming, we prove the stability and safety of the system. Extensive experiments demonstrate that the designed control strategy significantly improves system control performance while ensuring system safety.