3-D Decentralized Prioritized Motion Planning and Coordination for High-Density Operations of Micro Aerial Vehicles.

Abstract

This paper presents a decentralized motion planning method for multiple aerial vehicles moving among 3-D polygonal obstacles resembling an urbanlike environment. The algorithm combines a prioritized $A^\star$ algorithm for high-level planning, along with a coordination method based on barrier functions for low-level trajectory generation and vehicle control. To this end, we extend the barrier functions method developed in our earlier work so that it treats 2-D and 3-D polygonal obstacles, and generates collision-free trajectories for the multiagent system. We furthermore augment the low-level trajectory generation and control with a prioritized $A^\star$ path planning algorithm, in order to compute waypoints and paths that force the agents of lower priority to avoid the paths of the agents of higher priority, reducing thus congestion. This feature enhances further the performance of the barrier-based coordination, and results in shorter paths and time to the goal destinations. We finally extend the proposed control design to the agents of constrained double-integrator dynamics, compared with the single-integrator case in our earlier work. We assume that the obstacles are known to the agents, and that each agent knows the state of other agents lying in its sensing area. Simulation results in 2-D and 3-D polygonal environments, as well as experimental results with micro aerial vehicles (quadrotors) in an indoor lab environment demonstrate the efficacy of the proposed approach.

Bib

@article{DBLP:journals/tcst/MaJWP18,
  author       = {Xiaobai Ma and
                  Ziyuan Jiao and
                  Zhenkai Wang and
                  Dimitra Panagou},
  title        = {3-D Decentralized Prioritized Motion Planning and Coordination for
                  High-Density Operations of Micro Aerial Vehicles},
  journal      = {{IEEE} Trans. Control. Syst. Technol.},
  volume       = {26},
  number       = {3},
  pages        = {939--953},
  year         = {2018},
  url          = {https://doi.org/10.1109/TCST.2017.2699165},
  doi          = {10.1109/TCST.2017.2699165},
  timestamp    = {Mon, 08 Jun 2020 22:20:35 +0200},
  biburl       = {https://dblp.org/rec/journals/tcst/MaJWP18.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}