Aerial swarm systems possess immense potential in various aspects, such as cooperative exploration, target tracking, and search and rescue. Efficient accurate self- and mutual state estimation are the critical preconditions for completing these swarm tasks, which remain challenging research topics. This article proposes Swarm-LIO2, a fully decentralized, plug-and-play, computationally efficient, and bandwidth-efficient light detection and ranging (LiDAR)-inertial odometry for aerial swarm systems. Swarm-LIO2 uses a decentralized plug-and-play network as the communication infrastructure. Only bandwidth-efficient and low-dimensional information is exchanged, including identity, ego state, mutual observation measurements, and global extrinsic transformations. To support the plug and play of new teammate participants, Swarm-LIO2 detects potential teammate autonomous aerial vehicles (AAVs) and initializes the temporal offset and global extrinsic transformation all automatically. To enhance the initialization efficiency, novel reflectivity-based AAV detection, trajectory matching, and factor graph optimization methods are proposed. For state estimation, Swarm-LIO2 fuses LiDAR, inertial measurement units, and mutual observation measurements within an efficient error state iterated Kalman filter (ESIKF) framework, with careful compensation of temporal delay and modeling of measurements to enhance the accuracy and consistency. Moreover, the proposed ESIKF framework leverages the global extrinsic for ego state estimation in the case of LiDAR degeneration or refines the global extrinsic along with the ego state estimation otherwise. To enhance the scalability, Swarm-LIO2 introduces a novel marginalization method in the ESIKF, which prevents the growth of computational time with swarm size. Extensive simulation and real-world experiments demonstrate the broad adaptability to large-scale aerial swarm systems and complicated scenarios, including GPS-denied scenes and degenerated scenes for cameras or LiDARs. The experimental results showcase the centimeter-level localization accuracy, which outperforms other state-of-the-art LiDAR-inertial odometry for a single-AAV system. Furthermore, diverse applications demonstrate the potential of Swarm-LIO2 to serve as a reliable infrastructure for various aerial swarm missions.

Robot swarms are increasingly being applied in various domains. However, due to the inherent limitation imposed by low real-time communication bandwidth, the synchronization of environmental information among multiple robots remains a persistent and challenging problem in practical applications. In response to this challenge, we introduce a comprehensive framework for synchronizing occupancy grid maps (OGMs) in practical multi-robot systems that operate under communication bandwidth constraints. In our research, we elaborately design the data structure of transmitted local OGMs and employ the Hilbert space-filling curve for voxel sorting. By adopting this approach, data redundancy is effectively increased, resulting in lower information entropy for compression and significantly reducing the volume of communication data. Finally, our framework outperforms the benchmark method by reducing the average and maximum bandwidth usage by more than 10 times in high-resolution scenarios. Moreover, our method has been successfully applied in the multi-UAV autonomous navigation application, demonstrating its real-time and bandwidth-efficient nature, as well as its practical value.

Occupancy mapping is a fundamental component of robotic systems to reason about the unknown and known regions of the environment. This article presents an efficient occupancy mapping framework for high-resolution light detection and ranging (LiDAR) sensors, termed D-Map. The framework introduces three main novelties to address the computational efficiency challenges of occupancy mapping. First, we use a depth image to determine the occupancy state of regions instead of the traditional ray-casting method. Second, we introduce an efficient on-tree update strategy on a tree-based map structure. These two techniques avoid redundant visits to small cells, significantly reducing the number of cells to be updated. Third, we remove known cells from the map at each update by leveraging the low false alarm rate of LiDAR sensors. This approach not only enhances our framework's update efficiency by reducing map size but also endows it with an interesting decremental property, which we have named D-Map. To support our design, we provide theoretical analyzes of the accuracy of the depth image projection and time complexity of occupancy updates. Furthermore, we conduct extensive benchmark experiments on various LiDAR sensors in both public and private datasets. Our framework demonstrates superior efficiency in comparison with other state-of-the-art methods while maintaining comparable mapping accuracy and high memory efficiency. We demonstrate two real-world applications of D-Map for real-time occupancy mapping on a handheld device and an aerial platform carrying a high-resolution LiDAR.

Accurate self and relative state estimation are the critical preconditions for completing swarm tasks, e.g., collaborative autonomous exploration, target tracking, search and rescue. This paper proposes Swarm-LIO: a fully decentralized state estimation method for aerial swarm systems, in which each drone performs precise ego-state estimation, exchanges ego-state and mutual observation information by wireless communication, and estimates relative state with respect to (w.r.t.) the rest of UAVs, all in real-time and only based on LiDAR-inertial measurements. A novel 3D LiDAR-based drone detection, identification and tracking method is proposed to obtain observations of teammate drones. The mutual observation measurements are then tightly-coupled with IMU and LiDAR measurements to perform real-time and accurate estimation of ego-state and relative state jointly. Extensive real-world experiments show the broad adaptability to complicated scenarios, including GPS-denied scenes, degenerate scenes for camera (dark night) or LiDAR (facing a single wall). Compared with ground-truth provided by motion capture system, the result shows the centimeter-level localization accuracy which outperforms other state-of-the-art LiDAR-inertial odometry for single UAV system.

In this paper, we propose a novel swashplateless-elevon actuation (SEA) for dual-rotor tail-sitter vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs). In contrast to the conventional elevon actuation (CEA) which controls both pitch and yaw using elevons, the SEA adopts swash-plateless mechanisms to generate an extra moment through motor speed modulation to control pitch and uses elevons solely for controlling yaw, without requiring additional actuators. This decoupled control strategy mitigates the saturation of elevons' deflection needed for large pitch and yaw control actions, thus improving the UAV's control performance on trajectory tracking and disturbance rejection performance in the presence of large external disturbances. Furthermore, the SEA overcomes the actuation degradation issues experienced by the CEA when the UAV is in close proximity to the ground, leading to a smoother and more stable take-off process. We validate and compare the performances of the SEA and the CEA in various real-world flight conditions, including take-off, trajectory tracking, and hover flight and position steps under external disturbance. Experimental results demonstrate that the SEA has better performances than the CEA. Moreover, we verify the SEA's feasibility in the attitude transition process and fixed-wing-mode flight of the VTOL UAV. The results indicate that the SEA can accurately control pitch in the presence of high-speed incoming airflow and maintain a stable attitude during fixed-wing mode flight. Video of all experiments can be found in youtube.com/watch?v=Sx9Rk4Zf7sQ

This paper presents a novel uncrewed aerial vehicle (UAV) design named HALO, which stands for “harmless aerial limber robot”. HALO uses a swashplateless mechanism to generate a moment for pitch and roll control without requiring additional actuators such as servo, reducing the number of components needed for control and enhancing reliability. Its reduced weight and number of actuators improve payload capacity and maneuverability. Meanwhile, HALO's coaxial duct design improves safety and aerodynamic efficiency. Experimental tests, including figure-of-eight trajectory tracking, wind gust and stick poking disturbances, hover efficiency comparison, and actual flight with collision is conducted to confirm HALO's robustness and exceptional safety characteristics, suggesting it as a promising design for various applications.

Autonomous exploration is a crucial aspect of robotics that has numerous applications. Most of the existing methods greedily choose goals that maximize immediate reward. This strategy is computationally efficient but insufficient for overall exploration efficiency. In recent years, some state-of-the-art methods are proposed, which generate a global coverage path and significantly improve overall exploration efficiency. However, global optimization produces high computational overhead, leading to low-frequency planner updates and inconsistent planning motion. In this work, we propose a novel method to support fast UAV exploration in large-scale and cluttered 3-D environments. We introduce a computationally low-cost viewpoints generation method using occlusion-free spheres. Additionally, we combine greedy strategy with global optimization, which considers both computational and exploration efficiency. We benchmark our method against state-of-the-art methods to showcase its superiority in terms of exploration efficiency and computational time. We conduct various real-world experiments to demonstrate the excellent performance of our method in large-scale and cluttered environments.

Aerial tracking with multiple unmanned aerial vehicles (UAVs) has wide potential in various applications. However, the existing works for swarm tracking typically lack the capability of maintaining high target visibility in cluttered environments. To address this deficiency, we present a decentralized planner that maximizes target visibility while ensuring collision-free maneuvers for swarm tracking. In this paper, each drone's tracking performance is first analyzed by a decentralized kinodynamic searching front-end, which renders an optimal guiding path to initialize safe flight corridors and visible sectors. Afterwards, a polynomial trajectory satisfying the corridor constraints is generated by a spatial-temporal optimizer. Inter-vehicle collision and occlusion avoidance are also incorporated into the optimization objectives. The advantages of our methods are verified by extensive benchmark comparisons against other cutting-edge works. Integrated with an autonomous LiDAR-based swarm system, the proposed planner demonstrates its efficiency and robustness in real-world experiments with unknown cluttered surroundings.