Autonomous Navigation of an Aerial Drone to Observe a Group of Wild Animals With Reduced Visual Disturbance

Xiaohui Li, Hailong Huang, Andrey V. Savkin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Protection of wild animals relies on understanding the interaction between the animals and their environment. With the ability to rapidly access rugged areas, aerial monitoring by drones is fast becoming a viable tool for ecologists to monitor wild animals. Unfortunately, this approach results in significant disturbance to different species of wild animals. Inspired by motion camouflage, this article explores a navigation method for a drone to covertly observe a group of animals and their habitat. Unlike existing motion-camouflage navigation approaches that deceive a single target, we introduce a sliding-mode-based method that reactively navigates the drone to induce less optical flow on multiple targets’ visual system. The proposed method is computationally simple and suitable for a drone to closely observe a group of moving animals with reduced visual disturbance. Computer simulations are conducted to demonstrate the performance of the proposed method.

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalIEEE Systems Journal
DOIs
Publication statusAccepted/In press - 26 Jan 2022

Keywords

  • Aerial drones
  • Animals
  • autonomous navigation
  • Biomedical monitoring
  • biomimetics
  • covert monitoring
  • drone navigation
  • Drones
  • Monitoring
  • motion-camouflage guidance
  • Navigation
  • unmanned aerial vehicle (UAV)
  • Visualization
  • wild animals
  • Wildlife

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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