Speed-Weighted Adaptive Flocking for Sailing Swarms under Dynamic Environmental Forcing

2026-05-19

AI Summary

This study addresses the coordination challenges faced by heterogeneous fleets of wind-powered sailboat robots in dynamic wind fields, where disparities in speed and maneuverability hinder collective behavior. To overcome this, the authors propose a velocity-weighted social interaction mechanism grounded in the Couzin model, which amplifies the social influence of slower agents while respecting instantaneous motion constraints, thereby enabling adaptive formation control. Key contributions include SailSwarmSwIM—the first simulation platform for sailboat swarms incorporating wind-dependent dynamics—as well as simplified-order modeling, wind-aware maneuverability constraints, tacking behavior emulation, and the velocity-weighted social force algorithm. Experimental results demonstrate that the proposed approach significantly enhances swarm polarization and stability under both steady and gusty wind conditions, underscoring the critical role of slower individuals in promoting group cohesion.

Abstract

Collective behavior models, such as aggregation and flocking, usually assume self-propelled robots that can directly execute their desired speed and direction of motion without fundamental constraints. However, autonomous sailing robots violate this assumption. Their motion is shaped by wind-dependent propulsion, restricted headings, and spatially varying wind conditions. In particular, maneuverability is coupled to wind speed: in weak wind, sailboats may turn only slowly or not at all, whereas stronger wind enables faster turns. This introduces transient heterogeneity in speed and maneuverability across the flock. We focus on this fast-slow coordination problem in sailing robot flocks. To study this problem, we introduce SailSwarmSwIM, a reduced-order simulator for autonomous sailing robot swarms that captures wind-dependent speed and maneuverability, no-go zones, tacking behavior, and steady or gusty wind fields. To design our novel flocking technique, we start from the Couzin model and introduce a speed-weighted social interaction rule that accounts for each robot's transient motion constraints. A key result is that increasing the social influence of slower robots improves polarization and reduces close encounters. This effect arises from a balance between attraction to fast neighbors, which helps maintain movement, and cohesion around slow neighbors, which prevents the flock from fragmenting. Together, our simulator, SailSwarmSwIM, and the speed-weighted interaction rule provide a modeling framework for studying adaptive collective behavior in robotic fleets whose motion capabilities are continuously shaped by wind.

Problem

• sailing swarms
• dynamic environmental forcing
• speed heterogeneity
• maneuverability constraints
• collective coordination

Innovation

• speed-weighted interaction
• sailing robot swarms
• wind-dependent maneuverability
• adaptive flocking
• heterogeneous coordination