Agree to Disagree: Consensus-Free Flocking under Constraints

2026-01-27

AI Summary

This work addresses the limitations of conventional swarm control methods, which rely on homogeneous inter-agent spacing assumptions and fail in scenarios involving partially aligned or conflicting objectives, lack of trust, and absence of secure communication. To overcome these challenges, the paper introduces a novel constrained collective potential function that enables agents to negotiate heterogeneous spacing and constraint parameters using only local observations—without requiring explicit communication or global information. This approach achieves consensus-free swarm motion, extending Reynolds’ classic flocking principles (cohesion, alignment, separation) beyond uniform spacing and shared constraints. The proposed method supports heterogeneous coordination even in semi-trusted environments. Extensive simulations demonstrate its effectiveness and robustness under conditions of objective conflict and complete absence of inter-agent communication.

Technology Category

• Multiagent Systems: Agent Communication
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: The sharing economy
• Responsible Web: Consent frameworks and practices on the web

Abstract

Robots sometimes have to work together with a mixture of partially-aligned or conflicting goals. Flocking - coordinated motion through cohesion, alignment, and separation - traditionally assumes uniform desired inter-agent distances. Many practical applications demand greater flexibility, as the diversity of types and configurations grows with the popularity of multi-agent systems in society. Moreover, agents often operate without guarantees of trust or secure communication. Motivated by these challenges we update well-established frameworks by relaxing this assumption of shared inter-agent distances and constraints. Through a new form of constrained collective potential function, we introduce a solution that permits negotiation of these parameters. In the spirit of the traditional flocking control canon, this negotiation is achieved purely through local observations and does not require any global information or inter-agent communication. The approach is robust to semi-trust scenarios, where neighbouring agents pursue conflicting goals. We validate the effectiveness of the approach through a series of simulations.

Problem

• flocking
• multi-agent systems
• conflicting goals
• consensus-free
• constrained coordination

Innovation

• consensus-free flocking
• constrained collective potential
• local negotiation
• multi-agent systems
• robust coordination