Flock navigation with dynamic hierarchy and subjective weights using nonlinear MPC

TL;DR

This paper presents a novel MPC-based flock control method that incorporates dynamic hierarchy, subjective weights, and obstacle avoidance, enhancing navigation capabilities by leveraging future trajectory predictions and adaptive information credibility.

Contribution

It introduces a new flock control framework with dynamic hierarchy and subjective weighting in nonlinear MPC, improving navigation and obstacle avoidance in leader-follower systems.

Findings

Enhanced flock navigation with obstacle avoidance.

Effective use of future trajectory predictions.

Adaptive weighting improves flock cohesion and alignment.

Abstract

We propose a model predictive control (MPC) based approach to a flock control problem with obstacle avoidance capability in a leader-follower framework, utilizing the future trajectory prediction computed by each agent. We employ the traditional Reynolds' flocking rules (cohesion, separation, and alignment) as a basis, and tailor the model to fit a navigation (as opposed to formation) purpose. In particular, we introduce several concepts such as the credibility and the importance of the gathered information from neighbors, and dynamic trade-offs between references. They are based on the observations that near-future predictions are more reliable, agents closer to leaders are implicit carriers of more educated information, and the predominance of either cohesion or alignment is dictated by the distance between the agent and its neighbors. These features are incorporated in the MPC formulation, and their advantages are discussed through numerical simulations.