Multirotors are usually desired to enter confined narrow tunnels that are barely accessible to humans in various applications including inspection, search and rescue, and so on. This task is extremely challenging since the lack of geometric features and illuminations, together with the limited field of view, cause problems in perception; the restricted space and significant ego airflow disturbances induce control issues. This article introduces an autonomous aerial system designed for navigation through tunnels as narrow as 0.5 m in diameter. The real-time and online system includes a virtual omni-directional perception module tailored for the mission and a novel motion planner that incorporates perception and ego airflow disturbance factors modeled using camera projections and computational fluid dynamics analyses, respectively. Extensive flight experiments on a custom-designed quadrotor are conducted in multiple realistic narrow tunnels to validate the superior performance of the system, even over human pilots, proving its potential for real applications. In addition, a deployment pipeline on other multirotor platforms is outlined and open-source packages are provided for future developments.

Adopting omnidirectional Field of View (FoV) cameras in aerial robots vastly improves perception ability, significantly advancing aerial robotics’s capabilities in inspection, reconstruction, and rescue tasks. However, such sensors also elevate system complexity, e.g., hardware design, and corresponding algorithm, which limits researchers from utilizing aerial robots with omnidirectional FoV in their research. To bridge this gap, we propose OmniNxt, a fully open-source aerial robotics platform with omnidirectional perception. We design a high-performance flight controller Nxt-FC and a multi-fisheye camera set for OmniNxt. Meanwhile, the compatible software is carefully devised, which empowers OmniNxt to achieve accurate localization and real-time dense mapping with limited computation resource occupancy. We conducted extensive real-world experiments to validate the superior performance of OmniNxt in practical applications. All the hardware and software are open-access at3, and we provide docker images of each crucial module in the proposed system. Project page: https://hkust-aerial-robotics.github.io/OmniNxt.

Collaborative simultaneous localization and mapping (CSLAM) is essential for autonomous aerial swarms, laying the foundation for downstream algorithms, such as planning and control. To address existing CSLAM systems' limitations in relative localization accuracy, crucial for close-range UAV collaboration, this article introduces$D^{2}$SLAM—a novel decentralized and distributed CSLAM system.$D^{2}$SLAM innovatively manages near-field estimation for precise relative state estimation in proximity and far-field estimation for consistent global trajectories. Its adaptable front-end supports both stereo and omnidirectional cameras, catering to various operational needs and overcoming field-of-view challenges in aerial swarms. Experiments demonstrate$D^{2}$SLAM's effectiveness in accurate ego-motion estimation, relative localization, and global consistency. Enhanced by distributed optimization algorithms,$D^{2}$SLAM exhibits remarkable scalability and resilience to network delays, making it well suited for a wide range of real-world aerial swarm applications. We believe the adaptability and proven performance of$D^{2}$SLAM signify a notable advancement in autonomous aerial swarm technology.

Although the use of multiple unmanned aerial vehicles (UAVs) has great potential for fast autonomous exploration, it has received far too little attention. In this article, we present a RApid Collaborative ExploRation (RACER) approach using a fleet of decentralized UAVs. To effectively dispatch the UAVs, a pairwise interaction based on an online hgrid space decomposition is used. It ensures that all UAVs simultaneously explore distinct regions, using only asynchronous and limited communication. Furthermore, we optimize the coverage paths of unknown space and balance the workloads partitioned to each UAV with a capacitated vehicle routing problem formulation. Given the task allocation, each UAV constantly updates the coverage path and incrementally extracts crucial information to support the exploration planning. A hierarchical planner finds exploration paths, refines local viewpoints, and generates minimum-time trajectories in sequence to explore the unknown space agilely and safely. The proposed approach is evaluated extensively, showing high exploration efficiency, scalability, and robustness to limited communication. Furthermore, for the first time, we achieve fully decentralized collaborative exploration with multiple UAVs in the real world. We will release our implementation as an open-source package.

Decentralized state estimation is one of the most fundamental components of autonomous aerial swarm systems in GPS-denied areas; yet, it remains a highly challenging research topic. Omni-swarm, a decentralized omnidirectional visual–inertial–ultrawideband (UWB) state estimation system for aerial swarms, is proposed in this article to address this research niche. To solve the issues of observability, complicated initialization, insufficient accuracy, and lack of global consistency, we introduce an omnidirectional perception front end in Omni-swarm. It consists of stereo wide-field-of-view cameras and UWB sensors, visual–inertial odometry, multidrone map-based localization, and visual drone tracking algorithms. The measurements from the front end are fused with graph-based optimization in the back end. The proposed method achieves centimeter-level relative state estimation accuracy while guaranteeing global consistency in the aerial swarm, as evidenced by the experimental results. Moreover, supported by Omni-swarm, interdrone collision avoidance can be accomplished without any external devices, demonstrating the potential of Omni-swarm as the foundation of autonomous aerial swarms.

The collaboration of unmanned aerial vehicles (UAVs) has become a popular research topic for its practicability in multiple scenarios. The collaboration of multiple UAVs, which is also known as aerial swarm is a highly complex system, which still lacks a state-of-art decentralized relative state estimation method. In this paper, we present a novel fully decentralized visual-inertial-UWB fusion framework for relative state estimation and demonstrate the practicability by performing extensive aerial swarm flight experiments. The comparison result with ground truth data from the motion capture system shows the centimeter-level precision which outperforms all the Ultra-WideBand (UWB) and even vision based method. The system is not limited by the field of view (FoV) of the camera or Global Positioning System (GPS), meanwhile on account of its estimation consistency, we believe that the proposed relative state estimation framework has the potential to be prevalently adopted by aerial swarm applications in different scenarios in multiple scales.