Bearing-Distance Flocking with Zone-Based Interactions in Constrained Dynamic Environments (2409.10047v4)

Abstract: This paper presents a novel zone-based flocking control approach suitable for dynamic multi-agent systems (MAS). Inspired by Reynolds behavioral rules for $boids$, flocking behavioral rules with the zones of repulsion, conflict, attraction, and surveillance are introduced. For each agent, using only bearing and distance measurements, behavioral contribution vectors quantify the local separation, local and global flock velocity alignment, local cohesion, obstacle avoidance and boundary conditions, and strategic separation for avoiding alien agents. The control strategy uses the local perception-based behavioral contribution vectors to guide each agent's motion. Additionally, the control strategy incorporates a directionally aware obstacle avoidance mechanism that prioritizes obstacles in the agent's forward path. Simulation results validate the effectiveness of the model in creating flexible, adaptable, and scalable flocking behavior. Asymptotic stability and convergence to a stable flocking configuration for any initial conditions provided the interaction graph is a spanning tree are demonstrated. The flocking model's reliance on locally sensed bearing and distance measurements ensures scalability and robustness, particularly in scenarios where communication is unreliable or resource-intensive. This makes it well-suited for real-world applications demanding seamless operation in highly dynamic and distributed environments.

• The paper introduces a novel flocking model that integrates four interaction zones—repulsion, conflict, attraction, and surveillance—using local bearing and distance measurements.
• The methodology leverages simulations demonstrating that the approach maintains safe inter-agent distances and velocity alignment for fluid collective motion in complex environments.
• The study incorporates an innovative triangular obstacle avoidance mechanism, offering improved navigation precision and scalability for autonomous multi-agent systems.

Bearing-Distance Based Flocking with Zone-Based Interactions

This paper introduces a novel approach to multi-agent system (MAS) flocking control by leveraging a zone-based interaction model informed by Reynolds’ behavioral rules for boids. The authors present a nuanced paper distinguishing four concentric interaction zones: repulsion, conflict, attraction, and surveillance. The core contribution lies in the redefinition of these flocking principles using local perceptual data, namely bearing and distance measurements, to guide individual agent behavior within MAS without relying on centralized communication networks or global positioning systems.

The proposed model departs from traditional dichotomous attraction-repulsion frameworks by incorporating a conflict zone interposed between the zones of repulsion and attraction. Within this conflict zone, agents experience both repulsive and attractive forces, thereby facilitating a dynamic balance akin to fluid motion in natural flocking scenarios. The surveillance zone ensures strategic separation from alien agents while supporting obstacle avoidance and boundary constraints vital for practical MAS implementations. The proposed model addresses several limitations of earlier flocking strategies, such as rigidity, scalability challenges, and dependency on high-frequency communications.

Numerically, the conducted simulations validate the model's capacity for achieving flexible, scalable, and cohesive flocking behavior even in scenarios with complex environments and obstacles. The performance metrics captured in the simulations evidence that inter-agent distances remain above critical thresholds, ensuring collision avoidance while supporting fluid collective movement. Furthermore, the alignment of velocities corroborates the model's ability to achieve consensus without requiring lattice formations or explicit velocity constraints, as observed in alternative models like the Vicsek or Cucker-Smale paradigms.

Intriguingly, the authors introduce an innovative obstacle avoidance mechanism rooted in triangular zones. This method aligns with each agent's velocity vector, optimizing spatial negotiations in environments where obstacles and boundaries present critical confinement factors. Such an approach overcomes the traditional limitations of symmetrical buffer zones, offering tighter navigation paths and more precise motion coordination.

The paper also discusses simplified perception-based flocking models without partitioned zones, catering to scenarios where computational efficiency is paramount. These simplified models derive from the more complex zone-based interactions but maintain core flocking principles through integrated separation-cohesion and alignment dynamics, advantageous for real-time control in robotics applications.

Future developments are anticipated in the integration of stochastic elements to simulate more natural, life-like flocking patterns, accommodating random perturbations. The authors highlight the model's robustness for potential real-world implementations, underscoring scalability and adaptability within autonomous systems, particularly in industries relying on drone and mobile robot swarms.

In conclusion, this research extends the frontier of MAS flocking control by ingeniously circumventing traditional dependency on external coordinates or high-rate communications. It offers a robust framework, empowered by local sensing, which efficiently replicates natural flocking behavior. This novel strategy harmonizes the principles of spatial separation, velocity alignment, and agent cohesion, thereby contributing significantly to the theoretical and practical advancement of MAS flocking systems.