Model Predictive Control For Multiple Castaway Tracking with an Autonomous Aerial Agent

TL;DR

This paper presents a Model Predictive Control framework for autonomous UAVs to track multiple drifting castaways in maritime rescue scenarios, integrating radar data and CNN-based detection with extensive real-world testing.

Contribution

It introduces a novel MPC-based method for multi-target tracking in maritime rescue, combining radar measurements, CNN detection, and real-world validation.

Findings

Effective tracking of multiple castaways demonstrated

CNN detection probability quantified from real data

Proposed approach outperforms baseline methods

Abstract

Over the past few years, a plethora of advancements in Unmanned Areal Vehicle (UAV) technology has paved the way for UAV-based search and rescue operations with transformative impact to the outcome of critical life-saving missions. This paper dives into the challenging task of multiple castaway tracking using an autonomous UAV agent. Leveraging on the computing power of the modern embedded devices, we propose a Model Predictive Control (MPC) framework for tracking multiple castaways assumed to drift afloat in the aftermath of a maritime accident. We consider a stationary radar sensor that is responsible for signaling the search mission by providing noisy measurements of each castaway's initial state. The UAV agent aims at detecting and tracking the moving targets with its equipped onboard camera sensor that has limited sensing range. In this work, we also experimentally determine the probability of target detection from real-world data by training and evaluating various Convolutional Neural Networks (CNNs). Extensive qualitative and quantitative evaluations demonstrate the performance of the proposed approach.