Agree to Disagree: Consensus-Free Flocking under Constraints

TL;DR

The paper tackles flocking with heterogeneous agents and partial trust by relaxing the assumption of a shared inter-agent distance. It introduces a constrained collective potential that enables local negotiation of $d_{ij}(t)$ based on observations, using a filter for neighbor dynamics and a custom bump function to enforce bounds without communication. Key contributions include a custom bump function, a per-pair constrained potential $V^d_{ij}$, and a pinning-based navigation integration, with demonstrations in both homogeneous and heterogeneous assemblies showing stable convergence and improved connectivity. This approach enables robust, scalable, and communication-free coordination in semi-trust multi-agent systems, relevant to mixed robot fleets and real-world deployments.

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.

Figures (8)

• Figure 1: Agents unable to reach equilibrium when configured with different separation distances ($d_{ij}$).
• Figure 2: Bump function with $d_{i,\min}=5$ and $d_{i,\max}=15$.
• Figure 3: Gradient of bump function with $d_{i,\min}=5$ and $d_{i,\max}=15$.
• Figure 4: Assembly of 30 agents into flock with $d_{ij}=10$.
• Figure 5: Agents converge and improve connectivity over time.

Theorems & Definitions (1)

• Remark II.2