Risk-Aware Trajectory Optimization and Control for an Underwater Suspended Robotic System

TL;DR

This paper introduces a risk-aware trajectory optimization method for an underwater robotic system that accounts for uncertainties in drag and weight, improving safety and efficiency in litter collection tasks.

Contribution

It presents a novel stochastic optimization framework using conditional value-at-risk to handle parameter uncertainties in underwater robotic systems.

Findings

Reduces collision risks in simulations

Decreases energy consumption compared to existing methods

Enhances reliability of underwater robotic operations

Abstract

This paper focuses on the trajectory optimization of an underwater suspended robotic system comprising an uncrewed surface vessel (USV) and an uncrewed underwater vehicle (UUV) for autonomous litter collection. The key challenge lies in the significant uncertainty in drag and weight parameters introduced by the collected litter. We propose a dynamical model for the coupled UUV-USV system in the primary plane of motion and a risk-aware optimization approach incorporating parameter uncertainty and noise to ensure safe interactions with the environment. A stochastic optimization problem is solved using a conditional value-at-risk framework. Simulations demonstrate that our approach reduces collision risks and energy consumption, highlighting its reliability compared to existing control methods.