657 research outputs found
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
MARLAS: Multi Agent Reinforcement Learning for cooperated Adaptive Sampling
The multi-robot adaptive sampling problem aims at finding trajectories for a
team of robots to efficiently sample the phenomenon of interest within a given
endurance budget of the robots. In this paper, we propose a robust and scalable
approach using decentralized Multi-Agent Reinforcement Learning for cooperated
Adaptive Sampling (MARLAS) of quasi-static environmental processes. Given a
prior on the field being sampled, the proposed method learns decentralized
policies for a team of robots to sample high-utility regions within a fixed
budget. The multi-robot adaptive sampling problem requires the robots to
coordinate with each other to avoid overlapping sampling trajectories.
Therefore, we encode the estimates of neighbor positions and intermittent
communication between robots into the learning process. We evaluated MARLAS
over multiple performance metrics and found it to outperform other baseline
multi-robot sampling techniques. We further demonstrate robustness to
communication failures and scalability with both the size of the robot team and
the size of the region being sampled. The experimental evaluations are
conducted both in simulations on real data and in real robot experiments on
demo environmental setup
Distributed estimation over a low-cost sensor network: a review of state-of-the-art
Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked systems enable the employment of multiple sensors. Distributed estimation provides a scalable and fault-robust fusion framework with a peer-to-peer communication architecture. For this reason, there seems to be a real need for a critical review of existing and, more importantly, recent advances in the domain of distributed estimation over a low-cost sensor network. This paper presents a comprehensive review of the state-of-the-art solutions in this research area, exploring their characteristics, advantages, and challenging issues. Additionally, several open problems and future avenues of research are highlighted
Decision-Making for Search and Classification using Multiple Autonomous Vehicles over Large-Scale Domains
This dissertation focuses on real-time decision-making for large-scale domain search and object classification using Multiple Autonomous Vehicles (MAV). In recent years, MAV systems have attracted considerable attention and have been widely utilized. Of particular interest is their application to search and classification under limited sensory capabilities. Since search requires sensor mobility and classification requires a sensor to stay within the vicinity of an object, search and classification are two competing tasks. Therefore, there is a need to develop real-time sensor allocation decision-making strategies to guarantee task accomplishment. These decisions are especially crucial when the domain is much larger than the field-of-view of a sensor, or when the number of objects to be found and classified is much larger than that of available sensors. In this work, the search problem is formulated as a coverage control problem, which aims at collecting enough data at every point within the domain to construct an awareness map. The object classification problem seeks to satisfactorily categorize the property of each found object of interest. The decision-making strategies include both sensor allocation decisions and vehicle motion control. The awareness-, Bayesian-, and risk-based decision-making strategies are developed in sequence. The awareness-based approach is developed under a deterministic framework, while the latter two are developed under a probabilistic framework where uncertainty in sensor measurement is taken into account. The risk-based decision-making strategy also analyzes the effect of measurement cost. It is further extended to an integrated detection and estimation problem with applications in optimal sensor management. Simulation-based studies are performed to confirm the effectiveness of the proposed algorithms
Aeronautical Engineering: A continuing bibliography, supplement 120
This bibliography contains abstracts for 297 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980
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