Papers for

maritime robotics teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Autonomous vehicle teams flexibly share maritime survey tasks

Tunable Spatial Task Allocation with Application to Maritime Swarms

Abstract: In this chapter, we explore how a team of autonomous agents can flexibly reconfigure the distribution of its members across many spatially embedded tasks using a task allocation strategy grounded in a generalization of the recently proposed Nonlinear Opinion Dynamics model. Motivated by an adaptive maritime survey application, we couple opinion dynamics directly to spatial movement and examine how the emergent macroscopic allocation is shaped by the interplay between environmental geometry, nature of social interactions, and task demands. We illustrate in simulation how social coupling in the model tunes group cohesion, producing macroscopic allocation patterns ranging from maximally dispersed states, to intermediate semi-dispersed states with a smaller number of distinct subgroups, to maximally cohesive consensus states, in distinct and interpretable parameter regimes. Furthermore, we demonstrate with illustrative examples how a team can reconfigure itself in real time under changing task demands. For allocation scenarios with distance-based task preference and matched number of agents and tasks, we relate numerically the final allocation in the group to the Hungarian optimum, illustrating close-to-optimal performance. We illustrate performance of this method under realistic communication constraints in the MOOS-IvP-Multi simulation environment with simulated autonomous underwater vehicles (AUVs). Together, these examples show how a common set of local interaction rules can support a large range of interpretable macroscopic allocation states and flexible transitions between them.

Wed 28 OctMultiagent Systems
The gist
This work looks at how groups of autonomous agents, like underwater robots, can decide who does which tasks spread out in space. The authors connect how the agents influence each other to where they move, showing different team patterns from spread out to closely grouped. Their system can change how tasks are divided quickly when the needs change. They tested this approach in simulations with underwater vehicles and found it often matches or comes close to the best possible task division.
Open → 2610.39018v1

Multi-robot teams learn to handle uncertain maritime tasks efficiently

Reinforcement Learning Based Heterogeneous Task Allocation Under Spatio-Temporal Environmental Uncertainty for Multi-Robot Teams

Abstract: Autonomous Surface Vehicles (ASVs) have been increasingly deployed to perform different kinds of maritime tasks in recent years, which motivates the need for a reliable task allocation policy that can adapt to heterogeneous tasks and maritime disturbances. In this work, we formulate a multi-robot task allocation problem in which a team of ASVs with heterogeneous capabilities needs to complete tasks of different types and appearance times under unknown stochastic spatio-temporal uncertainties from maritime disturbances. We propose a decentralized Reinforcement Learning (RL) based policy network that is trained to be robust towards uncertainty-induced observation deviations, and is able to perform individual decision making that leads to efficient execution behaviors toward heterogeneous task requirements. We evaluate the proposed policy and four baseline methods in simulated experiments with varied proportions of different tasks types, numbers of tasks, robot species and robots per species. Experiment results show that our proposed policy achieves the lowest makespan and can be deployed to address scenarios with 100 robots and 400 tasks.

Sun 11 OctRobotics
The gist
Maritime robots called Autonomous Surface Vehicles (ASVs) can do many different tasks, but unpredictable conditions like waves and wind make deciding who does what tricky. The authors teach a group of these robots to decide on tasks by themselves using a learning method that helps them handle surprises in the environment. Their approach lets the robots work well together to finish many different kinds of tasks faster and better than other methods. They tested this in computer simulations with up to 100 robots and 400 tasks.
Open → 2610.15237v1