A Planning Framework for Persistent, Multi-UAV Coverage with Global Deconfliction

EasyChair Preprint 1468

Abstract

Planning for multi-robot coverage seeks to determine collision-free pathsfor a fleet of robots, enabling them to collectively observe points of interest in an en-vironment. Persistent coverage is a variant of traditional coverage, where coverage-levels in the environment decay over time. Thus robots have to continuously revisitparts of the environment to maintain a desired coverage-level. Facilitating this in thereal world demands us to tackle numerous subproblems. While there exist standardsolutions for these subproblems, there is no complete framework that addresses allof their individual challenges as a whole in a practical setting. We adapt and com-bine such solutions to present a planning framework for persistent coverage withmultiple unmanned aerial vehicles (UAVs). More specifically, we run a persistentloop of goal-assignment and globally deconflicting, kinodynamic path-planning formultiple UAVs. We evaluate our framework in simulation as well as the real world.Specifically, we demonstrate that (i) our framework exhibits the desirable traits ofgraceful degradation—given sufficient resources, we maintain persistent coverage,whereas while resources decrease (i.e., environment size increases or number ofUAVs decreases) coverage-levels decay slowly and (ii) ensuring global deconflic-tion in our framework incurs a negligibly higher price compared to other, weakercollision-checking schemes.

Keyphrases: Robotics, coverage, multi-robot, planning, search

@booklet{EasyChair:1468,
  author    = {Tushar Kusnur and Shohin Mukherjee and Dhruv Saxena and Tomoya Fukami and Takayuki Koyama and Oren Salzman and Maxim Likhachev},
  title     = {A Planning Framework for Persistent, Multi-UAV Coverage with Global Deconfliction},
  howpublished = {EasyChair Preprint 1468},
  year      = {EasyChair, 2019}}