4,704 research outputs found
Task-driven multi-formation control for coordinated UAV/UGV ISR missions
The report describes the development of a theoretical framework for coordination and control of combined teams of UAVs and UGVs for coordinated ISR missions. We consider the mission as a composition of an ordered sequence of subtasks, each to be performed by a different team. We design continuous cooperative controllers that enable each team to perform a given subtask and we develop a discrete strategy for interleaving the action of teams on different subtasks. The overall multi-agent coordination architecture is captured by a hybrid automaton, stability is studied using Lyapunov tools, and performance is evaluated through numerical simulations
Distributed Information-based Source Seeking
In this paper, we design an information-based multi-robot source seeking
algorithm where a group of mobile sensors localizes and moves close to a single
source using only local range-based measurements. In the algorithm, the mobile
sensors perform source identification/localization to estimate the source
location; meanwhile, they move to new locations to maximize the Fisher
information about the source contained in the sensor measurements. In doing so,
they improve the source location estimate and move closer to the source. Our
algorithm is superior in convergence speed compared with traditional field
climbing algorithms, is flexible in the measurement model and the choice of
information metric, and is robust to measurement model errors. Moreover, we
provide a fully distributed version of our algorithm, where each sensor decides
its own actions and only shares information with its neighbors through a sparse
communication network. We perform intensive simulation experiments to test our
algorithms on large-scale systems and physical experiments on small ground
vehicles with light sensors, demonstrating success in seeking a light source
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
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
Coordinated Multi-Agent Imitation Learning
We study the problem of imitation learning from demonstrations of multiple
coordinating agents. One key challenge in this setting is that learning a good
model of coordination can be difficult, since coordination is often implicit in
the demonstrations and must be inferred as a latent variable. We propose a
joint approach that simultaneously learns a latent coordination model along
with the individual policies. In particular, our method integrates unsupervised
structure learning with conventional imitation learning. We illustrate the
power of our approach on a difficult problem of learning multiple policies for
fine-grained behavior modeling in team sports, where different players occupy
different roles in the coordinated team strategy. We show that having a
coordination model to infer the roles of players yields substantially improved
imitation loss compared to conventional baselines.Comment: International Conference on Machine Learning 201
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