Heuristics-based Adaptive Biased Random Walk Algorithm for Chemical Source Localization using AUVs

We draw inspiration from the behavior of single-celled organisms to present a chemotaxis-inspired Adaptive Biased Random Walk (ABRW) guidance-control law for an Autonomous Underwater Vehicle (AUV). We build on previous results available in the literature to derive a random-walk based guidance-control law for an AUV to track-in and localize a potential chemical source in a turbulence-dominated environment. The ABRW-Strategy makes use of common plume-tracking and heuristic schemes for real-time path planning of the AUV. We further draw out a more comprehensive study of the guidance-strategy and extend the work for implementation in the Medusa class of vehicles that are developed in-house by Instituto Superior Tecnico (IST). The performance of the system is assessed via Hardware-in-the-loop (HIL) simulations to illustrate the viability of using random-walk for chemical source localization. The results obtained are encouraging for in-water tests with an autonomous vehicle of the Medusa class aiming at the validation of the proposed guidance-strategy in real-time experiments.