REPlanner: Efficient UAV Trajectory-Planning using Economic Reinforcement Learning

Alvi Ataur Khalil, Alexander J. Byrne, Mohammad Ashiqur Rahman, Mohammad Hossein Manshaei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Advances in the unmanned aerial vehicle (UAV) design and capability, as well as decreases in the manufacturing cost, have opened up applications of UAVs in various fields, including surveillance, firefighting, cellular networks, and delivery purposes. The uniqueness of UAVs in systems creates a novel set of trajectory or path planning and coordination problems. Environments include many more points of interest (POIs) than UAVs, with obstacles and no-fly zones. We introduce REPlanner, a novel multi-agent reinforcement learning algorithm inspired by economic transactions to distribute tasks among UAVs. This system revolves around an economic theory, in particular an auction mechanism where UAVs trade assigned POIs. We formulate the path planning problem as a multi-agent economic game, where agents can cooperate and compete for resources. We then translate the problem into a partially observable Markov decision process (POMDP), which is solved using a reinforcement learning (RL) model deployed on each agent. As the system computes task distributions via UAV cooperation, it is highly resilient to any change in the swarm size. Our proposed network and economic game architecture can effectively coordinate the swarm as an emergent phenomenon while maintaining the swarm's operation. Evaluation results prove that REPlanner efficiently outperforms conventional RL-based trajectory search.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE International Conference on Smart Computing, SMARTCOMP 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages153-160
Number of pages8
ISBN (Electronic)9781665412520
DOIs
StatePublished - Aug 2021
Externally publishedYes
Event7th IEEE International Conference on Smart Computing, SMARTCOMP 2021 - Virtual, Irvine, United States
Duration: Aug 23 2021Aug 27 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Smart Computing, SMARTCOMP 2021

Conference

Conference7th IEEE International Conference on Smart Computing, SMARTCOMP 2021
Country/TerritoryUnited States
CityVirtual, Irvine
Period8/23/218/27/21

Keywords

  • path planning
  • reinforcement learning
  • swarm robotics
  • trajectory optimization
  • Unmanned aerial vehicles

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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