2 research outputs found
Distributed Hierarchical Distribution Control for Very-Large-Scale Clustered Multi-Agent Systems
As the scale and complexity of multi-agent robotic systems are subject to a
continuous increase, this paper considers a class of systems labeled as
Very-Large-Scale Multi-Agent Systems (VLMAS) with dimensionality that can scale
up to the order of millions of agents. In particular, we consider the problem
of steering the state distributions of all agents of a VLMAS to prescribed
target distributions while satisfying probabilistic safety guarantees. Based on
the key assumption that such systems often admit a multi-level hierarchical
clustered structure - where the agents are organized into cliques of different
levels - we associate the control of such cliques with the control of
distributions, and introduce the Distributed Hierarchical Distribution Control
(DHDC) framework. The proposed approach consists of two sub-frameworks. The
first one, Distributed Hierarchical Distribution Estimation (DHDE), is a
bottom-up hierarchical decentralized algorithm which links the initial and
target configurations of the cliques of all levels with suitable Gaussian
distributions. The second part, Distributed Hierarchical Distribution Steering
(DHDS), is a top-down hierarchical distributed method that steers the
distributions of all cliques and agents from the initial to the targets ones
assigned by DHDE. Simulation results that scale up to two million agents
demonstrate the effectiveness and scalability of the proposed framework. The
increased computational efficiency and safety performance of DHDC against
related methods is also illustrated. The results of this work indicate the
importance of hierarchical distribution control approaches towards achieving
safe and scalable solutions for the control of VLMAS. A video with all results
is available in https://youtu.be/0QPyR4bD2q0 .Comment: Accepted at Robotics: Science and Systems 202
Sampling-Based Optimization for Multi-Agent Model Predictive Control
We systematically review the Variational Optimization, Variational Inference
and Stochastic Search perspectives on sampling-based dynamic optimization and
discuss their connections to state-of-the-art optimizers and Stochastic Optimal
Control (SOC) theory. A general convergence and sample complexity analysis on
the three perspectives is provided through the unifying Stochastic Search
perspective. We then extend these frameworks to their distributed versions for
multi-agent control by combining them with consensus Alternating Direction
Method of Multipliers (ADMM) to decouple the full problem into local
neighborhood-level ones that can be solved in parallel. Model Predictive
Control (MPC) algorithms are then developed based on these frameworks, leading
to fully decentralized sampling-based dynamic optimizers. The capabilities of
the proposed algorithms framework are demonstrated on multiple complex
multi-agent tasks for vehicle and quadcopter systems in simulation. The results
compare different distributed sampling-based optimizers and their centralized
counterparts using unimodal Gaussian, mixture of Gaussians, and stein
variational policies. The scalability of the proposed distributed algorithms is
demonstrated on a 196-vehicle scenario where a direct application of
centralized sampling-based methods is shown to be prohibitive