Sampling-Based Path Planning for Cooperative Autonomous Maritime Vehicles to Reduce Uncertainty in Range-Only Localization

Jonatan Scharff Willners, Lachlan Toohey, Yvan Petillot

Research output: Contribution to journalArticlepeer-review


The following letter presents an adaptive path planning algorithm for cooperative localization in the maritime environment. It considers the scenario where an Autonomous Surface Vehicle (ASV) acts as a Communication and Navigation Aid (CNA) to support Autonomous Underwater Vehicles (AUVs) with range measurements. As AUVs have no access to GPS while submerged, range measurements can bind the otherwise continuously growing navigational error. This can be done by methods such as range-only Extended Kalman Filter (EKF). In such methods, the resulting uncertainty and positional error depends on the geometry between transmitter (CNA) and receiver (AUV). This letter proposes a planning algorithm that combines priority based expansion of a search tree with random sampling-based exploration to position the CNA at strategic positions to transmit ranging messages at optimal times to reduce the uncertainty and error at the AUVs’ position. The approach is validated and shows an increased confidence for AUVs’ localization in simulated environments as well as real experiments using a dataset gathered from AUV Sirius.
Original languageEnglish
Pages (from-to)3987-3994
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number4
Early online date4 Jul 2019
Publication statusPublished - Oct 2019


  • Acoustic communication
  • cooperating robots
  • localization
  • marine robotics
  • motion and path planning

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