CDDQN based efficient path planning for Aerial surveillance in high wind scenarios

Sagar Dalai, Eoin O'Connell, Thomas Newe, Petar Trslic, Manduhu Manduhu, Mahammad Irfan, James Riordan, Gerard Dooly

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Robotic surveillance, monitoring, and maintenance problem are open-for-research domains required by the military, industrial facilities, ports, airports, and various indoor and outdoor venues each having different needs. Recent work in path planning of aerial robotics is an emerging field of the surveillance problem, particularly for unstructured or unexplored areas. The nature of path planning problems with different foreign elements like wind, rain, and others escalate the cost of complex computation and power consumption. Due to constraints in payload and endurance, algorithms based on pose-graph, both from the run-time and solution point of view become inefficient when working with unstructured spaces. We propose a simple but effective Clipped Double Q-learning [1] based deep reinforcement learning algorithm (CDDQN)for efficient path planning under the influence of wind and with improved computational efficiency for surveillance in a port area. In the proposed algorithm we have formulated a dense reward structure in consideration of wind's effect on power consumption and time to reach the destination which led to a robust path planning system for high wind scenarios.

Original languageEnglish
Title of host publicationOCEANS 2023 - Limerick, OCEANS Limerick 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350332261
DOIs
Publication statusPublished - 2023
Event2023 OCEANS Limerick, OCEANS Limerick 2023 - Limerick, Ireland
Duration: 5 Jun 20238 Jun 2023

Publication series

NameOCEANS 2023 - Limerick, OCEANS Limerick 2023

Conference

Conference2023 OCEANS Limerick, OCEANS Limerick 2023
Country/TerritoryIreland
CityLimerick
Period5/06/238/06/23

Keywords

  • Clipped Double Reinforcement Learning
  • Deep Reinforcement Learning
  • Path Planning
  • Port Infrastructure Inspection
  • Surveillance
  • UAVs

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