Swarm of micro flying robots in the wild.

Journal: Science robotics
Published Date:

Abstract

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.

Authors

  • Xin Zhou
    School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China.
  • Xiangyong Wen
    State Key Laboratory of Industrial Control and Technology, Zhejiang University, Hangzhou, China.
  • Zhepei Wang
    State Key Laboratory of Industrial Control and Technology, Zhejiang University, Hangzhou, China.
  • Yuman Gao
    State Key Laboratory of Industrial Control and Technology, Zhejiang University, Hangzhou, China.
  • Haojia Li
    Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hongkong, China.
  • Qianhao Wang
    State Key Laboratory of Industrial Control and Technology, Zhejiang University, Hangzhou, China.
  • Tiankai Yang
    State Key Laboratory of Industrial Control and Technology, Zhejiang University, Hangzhou, China.
  • Haojian Lu
    State Key Laboratory of Industrial Control and Technology, Zhejiang University, 310027, Hangzhou, China. luhaojian@zju.edu.cn.
  • Yanjun Cao
    Huzhou Institute of Zhejiang University, Huzhou, China.
  • Chao Xu
    Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China;Department of Emergency, Zhejiang Hospital, Hangzhou 310013, China.
  • Fei Gao
    College of Biological Sciences, China Agricultural University, Beijing 100193, China.