Decentralized Safe and Scalable Multi-Agent Control under Limited Actuation (2409.09573v1)

Abstract: To deploy safe and agile robots in cluttered environments, there is a need to develop fully decentralized controllers that guarantee safety, respect actuation limits, prevent deadlocks, and scale to thousands of agents. Current approaches fall short of meeting all these goals: optimization-based methods ensure safety but lack scalability, while learning-based methods scale but do not guarantee safety. We propose a novel algorithm to achieve safe and scalable control for multiple agents under limited actuation. Specifically, our approach includes: $(i)$ learning a decentralized neural Integral Control Barrier function (neural ICBF) for scalable, input-constrained control, $(ii)$ embedding a lightweight decentralized Model Predictive Control-based Integral Control Barrier Function (MPC-ICBF) into the neural network policy to ensure safety while maintaining scalability, and $(iii)$ introducing a novel method to minimize deadlocks based on gradient-based optimization techniques from machine learning to address local minima in deadlocks. Our numerical simulations show that this approach outperforms state-of-the-art multi-agent control algorithms in terms of safety, input constraint satisfaction, and minimizing deadlocks. Additionally, we demonstrate strong generalization across scenarios with varying agent counts, scaling up to 1000 agents.

• The paper introduces MA-ICBF, a novel framework that integrates neural network policies with control barrier functions for safe and scalable multi-agent control.
• It embeds actuator limits using an MPC-based ICBF, ensuring all agents operate within input constraints while reducing computational overhead.
• The paper employs gradient-based optimization to resolve deadlocks, achieving 100% collision avoidance and improving navigation in congested environments.

Decentralized Safe and Scalable Multi-Agent Control under Limited Actuation

The paper "Decentralized Safe and Scalable Multi-Agent Control under Limited Actuation", presented by Zinage et al., tackles the complex problem of multi-agent control in settings with limited actuation, while ensuring safety and scalability. This research is particularly relevant for applications that involve a large number of agents operating in crowded environments, such as autonomous vehicles, warehouse automation, and drone swarms.

Main Contributions

The primary contributions of this work are threefold:

1. Safety and Scalability: The authors introduce a novel learning-based framework named MA-ICBF (Multi-Agent Integral Control Barrier Functions), which enhances both safety and scalability. Traditional CBFs are extended through the integration of neural network policies and decentralized operations.
2. Limited Actuation: Recognizing that real-world robots must operate within specific actuator limits, the proposed framework incorporates input constraints directly into the control design. A Model Predictive Control-based Integral Control Barrier Function (MPC-ICBF) is embedded within the neural network policy to ensure rigorous adherence to actuation limits.
3. Deadlock Resolution: A new method to minimize deadlocks is proposed, utilizing gradient-based optimization techniques from machine learning. This approach addresses local minima issues that commonly result in deadlocks in multi-agent systems.

Key Components and Methodology

The methodology can be divided into two main steps, combining learning-based methods with optimization techniques:

1. Learning Framework:
   • The framework involves learning a decentralized neural ICBF and a safe control policy for each agent.
   • The ICBF serves as a certificate ensuring the safety and operation within the input constraints.
   • A loss function is structured to jointly train the neural networks, ensuring they satisfy the ICBF conditions. The training data is collected using online strategies to dynamically adapt the controllers based on current system behaviors.
2. Execution Framework:
   • During execution, the learned neural policy is employed for decentralized control.
   • The MPC-ICBF is activated when potential collisions are predicted. This transformation of multiple collision constraints into a single constraint through the use of a combined ICBF representation significantly reduces computational overhead.
   • Deadlock issues are addressed by drawing parallels with gradient descent methods for non-convex optimization, enabling agents to escape local minima and continue towards their goals.

Numerical Simulations and Results

The proposed approach was evaluated through comprehensive simulations involving various numbers of agents in both empty and complex maze environments. The results were noteworthy:

• Collision Avoidance: MA-ICBF achieved 100% collision avoidance across all tested scenarios, scaling up to 1024 agents, outperforming state-of-the-art approaches like MA-CBF, GCBF, and MARL methods which showed a decline in safety as the number of agents increased.
• Input Constraint Satisfaction: The approach successfully maintained input constraints for all agents. Baseline methods demonstrated significant violations of these constraints, highlighting the importance of integrating actuation limits directly into the control design.
• Deadlock Minimization: MA-ICBF also reduced the number of deadlocks substantially as the number of agents increased, demonstrating effective navigation even in congested and dynamically challenging environments.

Implications and Future Directions

The implications of this work extend beyond just theoretical advancements. Practically, the MA-ICBF framework can be immediately applied to scenarios like robotic swarms in search-and-rescue missions, autonomous systems in smart cities, and intelligent traffic management, where ensuring safety amidst a large number of entities is critical.

Furthermore, the paper opens up several avenues for future research, including:

• Extending to Unknown Dynamics: The current assumption of known system dynamics might be relaxed using techniques for system identification or adaptive control.
• Real-world Implementation: Transitioning from simulations to real-world implementations will provide further validation and uncover new challenges.
• Dynamic Obstacles: Enhancing the framework to handle dynamic obstacles and more unpredictable environments will broaden its applicability.

This paper provides a robust framework that pushes the boundaries of decentralized multi-agent control, addressing critical issues of safety, scalability, and actuation constraints. With continued research and development, such frameworks can substantially contribute to advanced, real-world autonomous systems.