Navigating an arbitrary-shaped ground robot safely in cluttered environments remains a challenging problem. The existing trajectory planners that account for the robot’s physical geometry severely suffer from the intractable runtime. To achieve both computational efficiency and Continuous Collision Avoidance (CCA) of arbitrary-shaped ground robot planning, we proposed a novel coarse-to-fine navigation framework that significantly accelerates planning. In the first stage, a sampling-based method selectively generates distinct topological paths that guarantee a minimum inflated margin. In the second stage, a geometry-aware front-end strategy is designed to discretize these topologies into full-state robot motion sequences while concurrently partitioning the paths into SE(2) sub-problems and simpler ℝ2sub-problems for back-end optimization. In the final stage, an SVSDF-based optimizer generates trajectories tailored to these sub-problems and seamlessly splices them into a continuous final motion plan. Extensive benchmark comparisons show that the proposed method is one to several orders of magnitude faster than the cutting-edge methods in runtime while maintaining a high planning success rate and ensuring CCA.

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.

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.

Formation flight has a vast potential for aerial robot swarms in various applications. However, the existing methods lack the capability to achieve fully autonomous large-scale formation flight in dense environments. To bridge the gap, we present a complete formation flight system that effectively integrates real-world constraints into aerial formation navigation. This article proposes a differentiable graph-based metric to quantify the overall similarity error between formations. This metric is invariant to rotation, translation, and scaling, providing more freedom for formation coordination. We design a distributed trajectory optimization framework that considers formation similarity, obstacle avoidance, and dynamic feasibility. The optimization is decoupled to make large-scale formation flights computationally feasible. To improve the elasticity of formation navigation in highly constrained scenes, we present a swarm reorganization method that adaptively adjusts the formation parameters and task assignments by generating local navigation goals. A novel swarm agreement strategy called global-remap-local-replan and a formation-level path planner is proposed in this article to coordinate the global planning and local trajectory optimizations.To validate the proposed method, we design comprehensive benchmarks and simulations with other cutting-edge works in terms of adaptability, predictability, elasticity, resilience, and efficiency. Finally, integrated with palm-sized swarm platforms with onboard computers and sensors, the proposed method demonstrates its efficiency and robustness by achieving the largest scale formation flight in dense outdoor environments.

For aerial swarms, navigation in a prescribed formation is widely practiced in various scenarios. However, the associated planning strategies typically lack the capability of avoiding obstacles in cluttered environments. To address this deficiency, we present an optimization-based method that ensures collision-free trajectory generation for formation flight. In this paper, a novel differentiable metric is proposed to quantify the overall similarity distance between formations. We then formulate this metric into an optimization framework, which achieves spatial-temporal planning using polynomial trajectories. Minimization over collision penalty is also incorporated into the framework, so that formation preservation and obstacle avoidance can be handled simultaneously. To validate the efficiency of our method, we conduct benchmark comparisons with other cutting-edge works. Integrated with an autonomous distributed aerial swarm system, the proposed method demonstrates its efficiency and robustness in real-world experiments with obstacle-rich surroundings11https://www.youtube.com/watch?v=lFumtOrJci4. We will release the source code for the reference of the community22https://github.com/ZJU-FAST-Lab/Swarm-Formation.