Achieving large-scale aerial swarms is challenging due to the inherent contradictions in balancing computational efficiency and scalability. This paper introducesPrimitive-Swarm, an ultra-lightweight and scalable planner designed specifically for large-scale autonomous aerial swarms. The proposed approach adopts a decentralized and asynchronous replanning strategy. Within it is a novel motion primitive library consisting of time-optimal and dynamically feasible trajectories. They are generated utlizing a novel time-optimial path parameterization algorithm based on reachability analysis (TOPP-RA). Then, a rapid collision checking mechanism is developed by associating the motion primitives with the discrete surrounding space according to conflicts. By considering both spatial and temporal conflicts, the mechanism handles robot-obstacle and robot-robot collisions simultaneously. Then, during a replanning process, each robot selects the safe and minimum cost trajectory from the library based on user-defined requirements. Both the time-optimal motion primitive library and the occupancy information are computed offline, turning a time-consuming optimization problem into a linear-complexity selection problem. This enables the planner to comprehensively explore the non-convex, discontinuous 3-D safe space filled with numerous obstacles and robots, effectively identifying the best hidden path. Benchmark comparisons demonstrate that our method achieves the shortest flight time and traveled distance with a computation time of less than 1 ms in dense environments. Super large-scale swarm simulations, involving up to 1000 robots, running in real-time, verify the scalability of our method. Real-world experiments validate the feasibility and robustness of our approach. The code will be released to foster community collaboration.

In many situations such as city delivery and wild inspection, quadrotors are often required to follow a predefined reference trajectory. However, these reference trajectories cannot be perfectly safe, resulting in conflicts between tracking the reference precisely, flying safely, and finishing the mission timely. This paper proposes to solve the above problem, by introducing a replanning framework that first generates a topological consistent collision-free initial path and then flexibly optimizes the rejoin point and trajectory duration to generate a smooth and safe local rejoining trajectory. To avoid local trajectory switching in different directions during high-frequency replanning, we propose a topology-preserving path search algorithm based on kinodynamic RRT*. To satisfy dynamic constraints, avoid delays, and achieve a smooth rejoin of the reference trajectory, we propose an optimization-based approach to refine the initial trajectory. The simulation results confirm that our proposed topological consistency and flexible optimization methods can reduce the risk of local trajectory and decrease obstacle avoidance delay for tracking reference trajectory. We also conduct real-world experiments in challenging environments and verify the effectiveness of our method.

The development of autonomous rotary-wing UAVs has shown an evident tendency in miniaturization. However, the side effects brought by miniaturization, such as decreased load capability, shorter flight duration and reduced autonomous ability, seriously hinder its process. In this paper, we first investigate the configurations of different rotary-wing aircraft and optimize the configuration selection. Afterward, with several elaborate mechanisms contributing to the miniaturization, we present the hardware design and control strategy of a mini coaxial helicopter, which is 62% smaller than the state-of-the-art autonomous mini quadrotor so far in collision area [1]. Meanwhile, abundant experiments reveal that it achieves impressive traversability and is capable of conducting autonomous tasks in unknown dense scenarios, while maintaining satisfactory performance regarding loadability and flight duration.

This paper proposes Elastic Tracker, a flexible trajectory planning framework that can deal with challenging tracking tasks with guaranteed safety and visibility. Firstly, an object detection and intension-free motion prediction method is designed. Then an occlusion-aware path finding method is proposed to provide a proper topology. A smart safe flight corridor generation strategy is designed with the guiding path. An analytical occlusion cost is evaluated. Finally, an effective trajectory optimization approach enables to generate a spatio-temporal optimal trajectory within the resultant flight corridor. Particular formulations are designed to guarantee both safety and visibility, with all the above requirements optimized jointly. The experimental results show that our method works more robustly but with less computation than the existing methods, even in some challenging tracking tasks.

For real-time multirotor kinodynamic planning, the efficiency of sampling-based methods is usually hindered by difficult-to-sample homotopy classes like narrow passages. In this paper, we address this issue by a hybrid scheme. We firstly propose a fast regional optimizer exploiting the information of local environments and then integrate it into a bidirectional global sampling process. The incorporation of the local optimization shows significantly improved success rates and less planning time in various types of challenging environments. We further present a refinement module utilizing the same framework as the regional optimizer. It comprehensively investigates the resulting trajectory of the global sampling and improves its smoothness with nearly negligible computation effort. Benchmark results illustrate that our proposed method can better exploit a previous trajectory compared to the state-of-the-art ones. The planning methods are applied to generate trajectories for a quadrotor system in simulation and real-world, and their capability is validated in real-time applications.

This paper proposes a systematic solution that uses an unmanned aerial vehicle (UAV) to aggressively and safely track an agile target. It properly handles the challenging situations where the intent of the target and the dense environments are unknown. Our work is divided into two parts: target motion prediction and tracking trajectory planning. The target motion prediction method utilizes target observations to reliably predict its future motion. The tracking trajectory planner follows the hierarchical workflow. A target informed kinody-namic searching method is adopted as the front-end, which heuristically searches for a safe tracking trajectory. The back- end optimizer then refines it into a spatial-temporal optimal trajectory. The proposed solution is integrated into an onboard quadrotor system. We fully test the system in challenging real-world tracking missions. Moreover, benchmark comparisons validate that the proposed method surpasses the cutting-edge methods on time efficiency and tracking effectiveness.