Swarm of micro flying robots in the wild

By Xin Zhou, Xiangyong Wen, Zhepei Wang, Yuman Gao, Haojia Li, Qianhao Wang, Tiankai Yang, Haojian Lu, Yanjun Cao, Chao Xu and 1 more. Science Robotics, 2022

528 citations recorded by the source corpus.

Aerial robots are widely deployed, but highly cluttered environments such as dense forests remain inaccessible to drones and even more so to swarms of drones. In these scenarios, previously unknown surroundings and narrow corridors combined with requirements of swarm coordination can create challenges. To enable swarm navigation in the wild, we develop miniature but fully autonomous drones with a trajectory planner that can function in a timely and accurate manner based on limited information from onboard sensors. The planning problem satisfies various task requirements including flight efficiency, obstacle avoidance, and inter-robot collision avoidance, dynamical feasibility, swarm coordination, and so on, thus realizing an extensible planner. Furthermore, the proposed planner deforms trajectory shapes and adjusts time allocation synchronously based on spatial-temporal joint optimization. A high-quality trajectory thus can be obtained after exhaustively exploiting the solution space within only a few milliseconds, even in the most constrained environment. The planner is finally integrated into the developed palm-sized swarm platform with onboard perception, localization, and control. Benchmark comparisons validate the superior performance of the planner in trajectory quality and computing time. Various real-world field experiments demonstrate the extensibility of our system. Our approach evolves aerial robotics in three aspects: capability of cluttered environment navigation, extensibility to diverse task requirements, and coordination as a swarm without external facilities. Description A fully autonomous swarm composed of palm-sized drones with versatile task extensibility in the wild is realized.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/11c762b2f0b22273a0fde8498eb5349fcd38e1d7

Citations in the corpus

• Aligning Cyber Space With Physical World: A Comprehensive Survey on Embodied AI (2024, unknown): “Traditional vSLAM uses image data and multi-view geometry to estimate a robot’s pose and construct low-level maps (e.g., sparse, semi-dense, or dense point clouds) through methods like filter-based approaches (e.g., MonoSLAM [58]), keyframe-based methods (e.g., ORB-SLAM [59]), and direct…”