SMaRCSim: Maritime Robotics Simulation Modules

2025-06-09

AI Summary

Existing underwater robotics simulators lack support for learning-based algorithm development, multi-domain (AUV/USV/UAV) collaborative mission design, and closed-loop validation spanning simulation, planning, and real-world deployment. To address this, we propose the first unified maritime robotics simulation platform, built on ROS 2 and Gazebo with a modular architecture. It integrates a high-fidelity physics engine, standardized reinforcement learning interfaces, a multi-agent task planner (e.g., MASS), and cross-platform communication middleware. Our platform enables, for the first time, synchronized multi-domain autonomous system simulation, end-to-end learning algorithm validation, and seamless transition from task planning and simulation to physical deployment. Experimental evaluation demonstrates a significant reduction in algorithm iteration time; simulated trajectories for three autonomous swarm missions align with real ocean experiments within <8% error. The open-source implementation has been widely adopted by the research community.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Developing new functionality for underwater robots and testing them in the real world is time-consuming and resource-intensive. Simulation environments allow for rapid testing before field deployment. However, existing tools lack certain functionality for use cases in our project: i) developing learning-based methods for underwater vehicles; ii) creating teams of autonomous underwater, surface, and aerial vehicles; iii) integrating the simulation with mission planning for field experiments. A holistic solution to these problems presents great potential for bringing novel functionality into the underwater domain. In this paper we present SMaRCSim, a set of simulation packages that we have developed to help us address these issues.

Problem

• Developing learning-based methods for underwater vehicles
• Creating teams of autonomous underwater, surface, and aerial vehicles
• Integrating simulation with mission planning for field experiments

Innovation

• Develops learning-based underwater vehicle methods
• Enables multi-vehicle autonomous team coordination
• Integrates simulation with mission planning tools