Autonomous vehicle teams flexibly share maritime survey tasks
Tunable Spatial Task Allocation with Application to Maritime Swarms
Multiagent Systems
Summary
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.
What this means in practice
- •For maritime robotics teams: Coordinate autonomous underwater vehicles to dynamically assign survey tasks based on spatial locations and group interactions.
- •For drone fleet operators: Manage task distribution among drones working in complex environments with communication limits using local interaction rules.
Authors
Vishnudatta Thota, Tyler Paine, Anastasia Bizyaeva
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.