Autonomous Multiagent Target Enclosing with Collision Avoidance and Self-Organizing Behavior

Core Concepts

The authors propose a decentralized guidance law for multiagent systems to safely enclose a stationary target while exhibiting self-organizing behavior, using only relative information between agents and the target.

Abstract

The paper introduces an approach to address the target enclosing problem using non-holonomic multiagent systems, where agents autonomously self-organize themselves in the desired formation around a fixed target. The key highlights are: The proposed approach combines global enclosing behavior and local collision avoidance mechanisms by devising a novel potential function and sliding manifold. Agents independently move toward the desired enclosing geometry when apart and activate the collision avoidance mechanism when a collision is imminent, thereby guaranteeing inter-agent safety. The authors show that an agent only needs to ensure safety with its nearest colliding agent, rather than all other agents, to avoid collisions in the entire swarm. The design eliminates the need for a fixed or pre-established agent arrangement around the target and requires only relative information between an agent and the target, reducing communication requirements. Simulation results are presented to validate the efficacy of the proposed method in achieving self-organizing multiagent target enclosing with inherent safety guarantees.

Stats

"The motion of the ith pursuer is governed by: ẋi = v cos χi, ẏi = v sin χi, χ̇i = ai/v" "The engagement geometry between the vehicles in the relative frame of reference is described by: ṙiT = -v cos σi, θ̇iT = -v sin σi/riT"

Quotes

"Our approach combines global enclosing behavior and local collision avoidance mechanisms by devising a novel potential function and sliding manifold." "We rigorously show that an agent does not need to ensure safety with every other agent and put forth a concept of the nearest colliding agent (for any arbitrary agent) with whom ensuring safety is sufficient to avoid collisions in the entire swarm." "The proposed control eliminates the need for a fixed or pre-established agent arrangement around the target and requires only relative information between an agent and the target."

Key Insights Distilled From

Self-organizing Multiagent Target Enclosing under Limited Information and Safety Guarantees

by Praveen Kuma... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04497.pdf

Deeper Inquiries

How can the proposed approach be extended to handle a moving target scenario

To extend the proposed approach to handle a moving target scenario, the potential function and guidance law need to be modified to account for the target's dynamic nature. One way to achieve this is by incorporating predictive modeling or estimation techniques to anticipate the target's future positions. By predicting the target's trajectory, the pursuers can adjust their movements to enclose the moving target effectively. Additionally, the pursuers may need to adapt their speeds and trajectories dynamically to keep up with the target's motion. This adaptation could involve real-time communication between the pursuers to coordinate their actions and ensure efficient target enclosing despite the target's movement.

What are the potential limitations or drawbacks of the self-organizing behavior in terms of convergence speed or enclosing geometry

While the self-organizing behavior offers advantages in terms of autonomy and adaptability, there are potential limitations to consider. One drawback could be the convergence speed of the pursuers towards the desired enclosing geometry. The decentralized nature of the approach, where each pursuer independently adjusts its trajectory based on local information, may result in slower convergence compared to centralized control strategies. Additionally, the self-organizing behavior may lead to variations in the enclosing geometry, especially when dealing with a large number of pursuers. Maintaining a consistent and precise enclosing formation could be challenging, impacting the overall efficiency of the target enclosing task.

How can the potential function be further optimized to achieve faster convergence while maintaining safety guarantees

To optimize the potential function for faster convergence while maintaining safety guarantees, several strategies can be implemented. One approach is to fine-tune the parameters in the potential function, such as adjusting the scaling constants 𝜆𝑖, 𝜂𝑖, and Δ𝑖 to influence the balance between attractive and repulsive forces. By optimizing these parameters, the potential function can be tailored to encourage quicker convergence towards the target while still ensuring inter-agent safety. Additionally, incorporating adaptive algorithms or machine learning techniques to dynamically adjust the potential function based on real-time feedback can enhance the convergence speed. This adaptive optimization can help the pursuers navigate more efficiently towards the target while avoiding collisions with neighboring agents.