LOTUSim-Energy: A Maritime Simulator for Human-Drone Interaction in Autonomous Offshore Operation & Maintenance

Simulation is essential for maritime robotics, supporting operator training, mission rehearsal, and human-vehicle interaction in environments where real-world testing is costly or hazardous. Existing simulators focus primarily on autonomy systems and often lack human-in-the-loop interaction and realistic environmental physics. This paper introduces LOTUSim, an open-source, real-time maritime simulator supporting multi-user interaction across aerial, surface, and underwater robotic systems for coordinated naval-style operations. The first contribution of this work is enabling real-time interactive performance for users while ensuring scalability to large fleets operating within a shared interactive simulation environment. Validation demonstrates robust human-in-the-loop performance, maintaining strict real-time execution and high visual fidelity while scaling to large heterogeneous maritime drone swarms. The second contribution is a computationally efficient, Ekman-inspired layered, underwater current model that captures wind-driven, depth-dependent flow dynamics with sufficient physical fidelity for large-scale simulations. Validation against ocean reanalysis data demonstrates substantially improved accuracy compared to commonly used stochastic Gauss-Markov current models. These results confirm LOTUSim's suitability as a simulation platform for operatorin-the-loop maritime robotics research.

Large language models (LLMs) provide a promising interface for high-level robotic task planning, but their use in multi-UAV collaboration remains difficult to evaluate systematically. Existing UAV simulators mainly emphasize dynamics, perception, or low-level control, while existing LLM-agent benchmarks rarely capture aerial-robotics constraints such as partial observability, spatial coverage, UAV assignment, and multi-vehicle coordination. To bridge this gap, we present MultiUAV-Plat, a lightweight, easy-to-use, LLM-agent-oriented simulation platform for multi-UAV collaborative task planning. The platform exposes concise RESTful APIs, agent-facing observations, role-based information access, hidden validation logic, and optional 2D/3D visualization, allowing agents to solve missions through realistic tool interaction rather than privileged simulator access. Built on this platform, the MultiUAV-Plat Benchmark contains 75 mission sessions, 1500 natural-language tasks, and 9396 validation checks across target assignment, area search, and area assignment and patrol scenarios. We further propose Agent4Drone, a task-specific LLM agent framework that structures multi-UAV behavior into memory, observation, task understanding, planning, execution, and verification. In a full paired benchmark comparison, Agent4Drone achieves a 57.9% task pass rate, a 74.6% average task check pass rate, and a 72.0% global check pass rate, substantially outperforming a ReAct baseline at 30.6%, 47.9%, and 43.1%, respectively. Agent4Drone also reduces the total failed task rate from 32.4% to 12.9%. These results demonstrate that MultiUAV-Plat and MultiUAV-Plat Benchmark provide a reproducible foundation for studying LLM-driven multi-UAV autonomy under realistic information and execution constraints.

As autonomous drone deployments scale from individual units to coordinated swarms, the human operator's role shifts from direct piloting to high-level supervision. Current interfaces often treat multi-drone control as a scaled-up version of single-drone operation. We instead investigate how reframing fleet supervision as spatial interaction can better support the spatial, temporal, and safety demands of complex missions. We present FleetScape, a Mixed Reality (MR) sandtable system that externalizes layered real-time mission, safety, and environmental data while enabling fluid transitions between manual intervention and autonomous supervision. We developed a high-fidelity building inspection simulation that generates and streams synchronized multi-drone and environmental data for MR visualizations. We used this prototype to conduct a user study with six experienced drone pilots managing fleets of up to 15 drones. Our findings show that FleetScape supports situational awareness through layered spatial representations and clarifies control mode transitions. However, a limit to situational awareness was observed as fleet size increases, leading to different supervisory strategies. Finally, we derive design implications for supporting scalable drone fleet supervision.