1,390 research outputs found
Fast Discrete Consensus Based on Gossip for Makespan Minimization in Networked Systems
In this paper we propose a novel algorithm to solve the discrete consensus problem, i.e., the problem of distributing evenly a set of tokens of arbitrary weight among the nodes of a networked system. Tokens are tasks to be executed by the nodes and the proposed distributed algorithm minimizes monotonically the makespan of the assigned tasks. The algorithm is based on gossip-like asynchronous local interactions between the nodes. The convergence time of the proposed algorithm is superior with respect to the state of the art of discrete and quantized consensus by at least a factor O(n) in both theoretical and empirical comparisons
An Overview of Recent Progress in the Study of Distributed Multi-agent Coordination
This article reviews some main results and progress in distributed
multi-agent coordination, focusing on papers published in major control systems
and robotics journals since 2006. Distributed coordination of multiple
vehicles, including unmanned aerial vehicles, unmanned ground vehicles and
unmanned underwater vehicles, has been a very active research subject studied
extensively by the systems and control community. The recent results in this
area are categorized into several directions, such as consensus, formation
control, optimization, task assignment, and estimation. After the review, a
short discussion section is included to summarize the existing research and to
propose several promising research directions along with some open problems
that are deemed important for further investigations
Probabilistic and Distributed Control of a Large-Scale Swarm of Autonomous Agents
We present a novel method for guiding a large-scale swarm of autonomous
agents into a desired formation shape in a distributed and scalable manner. Our
Probabilistic Swarm Guidance using Inhomogeneous Markov Chains (PSG-IMC)
algorithm adopts an Eulerian framework, where the physical space is partitioned
into bins and the swarm's density distribution over each bin is controlled.
Each agent determines its bin transition probabilities using a
time-inhomogeneous Markov chain. These time-varying Markov matrices are
constructed by each agent in real-time using the feedback from the current
swarm distribution, which is estimated in a distributed manner. The PSG-IMC
algorithm minimizes the expected cost of the transitions per time instant,
required to achieve and maintain the desired formation shape, even when agents
are added to or removed from the swarm. The algorithm scales well with a large
number of agents and complex formation shapes, and can also be adapted for area
exploration applications. We demonstrate the effectiveness of this proposed
swarm guidance algorithm by using results of numerical simulations and hardware
experiments with multiple quadrotors.Comment: Submitted to IEEE Transactions on Robotic
Search Tracker: Human-derived object tracking in-the-wild through large-scale search and retrieval
Humans use context and scene knowledge to easily localize moving objects in
conditions of complex illumination changes, scene clutter and occlusions. In
this paper, we present a method to leverage human knowledge in the form of
annotated video libraries in a novel search and retrieval based setting to
track objects in unseen video sequences. For every video sequence, a document
that represents motion information is generated. Documents of the unseen video
are queried against the library at multiple scales to find videos with similar
motion characteristics. This provides us with coarse localization of objects in
the unseen video. We further adapt these retrieved object locations to the new
video using an efficient warping scheme. The proposed method is validated on
in-the-wild video surveillance datasets where we outperform state-of-the-art
appearance-based trackers. We also introduce a new challenging dataset with
complex object appearance changes.Comment: Under review with the IEEE Transactions on Circuits and Systems for
Video Technolog
A Tutorial on Distributed Optimization for Cooperative Robotics: from Setups and Algorithms to Toolboxes and Research Directions
Several interesting problems in multi-robot systems can be cast in the
framework of distributed optimization. Examples include multi-robot task
allocation, vehicle routing, target protection and surveillance. While the
theoretical analysis of distributed optimization algorithms has received
significant attention, its application to cooperative robotics has not been
investigated in detail. In this paper, we show how notable scenarios in
cooperative robotics can be addressed by suitable distributed optimization
setups. Specifically, after a brief introduction on the widely investigated
consensus optimization (most suited for data analytics) and on the
partition-based setup (matching the graph structure in the optimization), we
focus on two distributed settings modeling several scenarios in cooperative
robotics, i.e., the so-called constraint-coupled and aggregative optimization
frameworks. For each one, we consider use-case applications, and we discuss
tailored distributed algorithms with their convergence properties. Then, we
revise state-of-the-art toolboxes allowing for the implementation of
distributed schemes on real networks of robots without central coordinators.
For each use case, we discuss their implementation in these toolboxes and
provide simulations and real experiments on networks of heterogeneous robots
Learning to Collaborate by Grouping: a Consensus-oriented Strategy for Multi-agent Reinforcement Learning
Multi-agent systems require effective coordination between groups and
individuals to achieve common goals. However, current multi-agent reinforcement
learning (MARL) methods primarily focus on improving individual policies and do
not adequately address group-level policies, which leads to weak cooperation.
To address this issue, we propose a novel Consensus-oriented Strategy (CoS)
that emphasizes group and individual policies simultaneously. Specifically, CoS
comprises two main components: (a) the vector quantized group consensus module,
which extracts discrete latent embeddings that represent the stable and
discriminative group consensus, and (b) the group consensus-oriented strategy,
which integrates the group policy using a hypernet and the individual policies
using the group consensus, thereby promoting coordination at both the group and
individual levels. Through empirical experiments on cooperative navigation
tasks with both discrete and continuous spaces, as well as Google research
football, we demonstrate that CoS outperforms state-of-the-art MARL algorithms
and achieves better collaboration, thus providing a promising solution for
achieving effective coordination in multi-agent systems
Consensus Algorithms for Estimation and Discrete Averaging in Networked Control Systems
In this thesis several topics on consensus and gossip algorithms for multi-agent systems are addressed. An agent is a dynamical system that can be fully described by a state-space representation of its dynamics. A multi-agent system is a network of agents whose pattern of interactions or couplings is described by a graph. Consensus problems in multi-agent systems consist in the study of local interaction rules between the agents such that as global emergent behavior the network converges to the so called "consensus" or "agreement" state where the value of each agent's state is the same and it is possibly a function of the initial network state, for instance the average. A consensus algorithm is thus a set of local interaction rules that solve the consensus problem under some assumptions on the network topology.
A gossip algorithm is a set of local state update rules between the agents that, disregarding their objective, are supposed to be implemented in a totally asynchronous way between pairs of neighboring agents, thus resembling the act of "gossiping" in a crowd of people.
In this thesis several algorithms based on gossip that solve the consensus and other related problems are presented.
In the �first part, several solutions to the consensus problem based on gossip under different sets of assumptions are proposed. In the fi�rst case, it is assumed that the state of the agents is discretized and represents a collection of tasks of different size. In the second case,
under the same discretization assumptions of the �rst case, it is assumed that the network is represented by a Hamiltonian graph and it is shown how under this assumption the convergence speed can be improved. In the third case, a solution for the consensus problem for networks represented by arbitrary strongly connected directed graphs is proposed, assuming that the state of the agents is a real number.
In the fourth case, a coordinate-free consensus algorithm based on gossip is designed and applied to a network of vehicles able to sense the relative distance between each other but with no access to absolute position information or to a common coordinate system. The proposed algorithm is then used to build in a decentralized way a common reference frame for the network of vehicles.
In the second part, a novel local interaction rule based on the consensus equation is proposed together with an algorithm to estimate in a decentralized way the spectrum of the Laplacian matrix that encodes the network topology. As emergent behavior, each agent's state oscillates only at frequencies corresponding to the eigenvalues of the
Laplacian matrix thus mapping the spectrum estimation problem into a signal processing problem solvable using the Fourier Transform. It is further shown that the constant component of the emergent behavior in the frequency domain solves the consensus on the average problem.
The spectrum estimation algorithm is then applied to leader-follower networks of mobile vehicles to infer in a decentralized way properties such as controllability, osservability and other topological features of
the network such as its topology.
Finally, a fault detection and recovery technique for sensor networks based on the so called motion-probes is presented to address the inherent lack of robustness against outlier agents in networks implementing
consensus algorithms to solve the distributed averaging problem
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