Multi-vehicle Dynamic Water Surface Monitoring

TL;DR

This paper presents a multi-vehicle planning approach for dynamic water surface monitoring, modeling spatio-temporal rewards to optimize object detection and tracking in real-time scenarios.

Contribution

It introduces a novel spatio-temporal reward model and a model predictive control framework for coordinated multi-vehicle water surface exploration.

Findings

Improves solutions to kinematic and team orienteering problems in water monitoring.

Demonstrates effectiveness through experimental verification.

Enhances real-time object detection and tracking capabilities.

Abstract

Repeated exploration of a water surface to detect objects of interest and their subsequent monitoring is important in search-and-rescue or ocean clean-up operations. Since the location of any detected object is dynamic, we propose to address the combined surface exploration and monitoring of the detected objects by modeling spatio-temporal reward states and coordinating a team of vehicles to collect the rewards. The model characterizes the dynamics of the water surface and enables the planner to predict future system states. The state reward value relevant to the particular water surface cell increases over time and is nullified by being in a sensor range of a vehicle. Thus, the proposed multi-vehicle planning approach is to minimize the collective value of the dynamic model reward states. The purpose is to address vehicles' motion constraints by using model predictive control on receding horizon, thus fully exploiting the utilized vehicles' motion capabilities. Based on the evaluation results, the approach indicates improvement in a solution to the kinematic orienteering problem and the team orienteering problem in the monitoring task compared to the existing solutions. The proposed approach has been experimentally verified, supporting its feasibility in real-world monitoring tasks.