924 research outputs found
An Experimental Platform for Multi-spacecraft Phase-Array Communications
The emergence of small satellites and CubeSats for interplanetary exploration
will mean hundreds if not thousands of spacecraft exploring every corner of the
solar-system. Current methods for communication and tracking of deep space
probes use ground based systems such as the Deep Space Network (DSN). However,
the increased communication demand will require radically new methods to ease
communication congestion. Networks of communication relay satellites located at
strategic locations such as geostationary orbit and Lagrange points are
potential solutions. Instead of one large communication relay satellite, we
could have scores of small satellites that utilize phase arrays to effectively
operate as one large satellite. Excess payload capacity on rockets can be used
to warehouse more small satellites in the communication network. The advantage
of this network is that even if one or a few of the satellites are damaged or
destroyed, the network still operates but with degraded performance. The
satellite network would operate in a distributed architecture and some
satellites maybe dynamically repurposed to split and communicate with multiple
targets at once. The potential for this alternate communication architecture is
significant, but this requires development of satellite formation flying and
networking technologies. Our research has found neural-network control
approaches such as the Artificial Neural Tissue can be effectively used to
control multirobot/multi-spacecraft systems and can produce human competitive
controllers. We have been developing a laboratory experiment platform called
Athena to develop critical spacecraft control algorithms and cognitive
communication methods. We briefly report on the development of the platform and
our plans to gain insight into communication phase arrays for space.Comment: 4 pages, 10 figures, IEEE Cognitive Communications for Aerospace
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Distributed bees algorithm for task allocation in swarm of robots
In this paper, we propose the distributed bees algorithm (DBA) for task allocation in a swarm of robots. In the proposed scenario, task allocation consists in assigning the robots to the found targets in a 2-D arena. The expected distribution is obtained from the targets' qualities that are represented as scalar values. Decision-making mechanism is distributed and robots autonomously choose their assignments taking into account targets' qualities and distances. We tested the scalability of the proposed DBA algorithm in terms of number of robots and number of targets. For that, the experiments were performed in the simulator for various sets of parameters, including number of robots, number of targets, and targets' utilities. Control parameters inherent to DBA were tuned to test how they affect the final robot distribution. The simulation results show that by increasing the robot swarm size, the distribution error decreased
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Anytime Planning for Decentralized Multirobot Active Information Gathering
A Software Suite for the Control and the Monitoring of Adaptive Robotic Ecologies
Adaptive robotic ecologies are networks of heterogeneous robotic devices (sensors, actuators, automated appliances) pervasively embedded in everyday environments, where they learn to cooperate towards the achievement of complex tasks. While their flexibility makes them an increasingly popular way to improve a system’s reliability, scalability, robustness and autonomy, their effective realisation demands integrated control and software solutions for the specification, integration and management of their highly heterogeneous and computational constrained components. In this extended abstract we briefly illustrate the characteristic requirements dictated by robotic ecologies, discuss our experience in developing adaptive robotic ecologies, and provide an overview of the specific solutions developed as part of the EU FP7 RUBICON Project
Decentralized dynamic task allocation for UAVs with limited communication range
We present the Limited-range Online Routing Problem (LORP), which involves a
team of Unmanned Aerial Vehicles (UAVs) with limited communication range that
must autonomously coordinate to service task requests. We first show a general
approach to cast this dynamic problem as a sequence of decentralized task
allocation problems. Then we present two solutions both based on modeling the
allocation task as a Markov Random Field to subsequently assess decisions by
means of the decentralized Max-Sum algorithm. Our first solution assumes
independence between requests, whereas our second solution also considers the
UAVs' workloads. A thorough empirical evaluation shows that our workload-based
solution consistently outperforms current state-of-the-art methods in a wide
range of scenarios, lowering the average service time up to 16%. In the
best-case scenario there is no gap between our decentralized solution and
centralized techniques. In the worst-case scenario we manage to reduce by 25%
the gap between current decentralized and centralized techniques. Thus, our
solution becomes the method of choice for our problem
A Survey and Analysis of Multi-Robot Coordination
International audienceIn the field of mobile robotics, the study of multi-robot systems (MRSs) has grown significantly in size and importance in recent years. Having made great progress in the development of the basic problems concerning single-robot control, many researchers shifted their focus to the study of multi-robot coordination. This paper presents a systematic survey and analysis of the existing literature on coordination, especially in multiple mobile robot systems (MMRSs). A series of related problems have been reviewed, which include a communication mechanism, a planning strategy and a decision-making structure. A brief conclusion and further research perspectives are given at the end of the paper
Planning for Decentralized Control of Multiple Robots Under Uncertainty
We describe a probabilistic framework for synthesizing control policies for
general multi-robot systems, given environment and sensor models and a cost
function. Decentralized, partially observable Markov decision processes
(Dec-POMDPs) are a general model of decision processes where a team of agents
must cooperate to optimize some objective (specified by a shared reward or cost
function) in the presence of uncertainty, but where communication limitations
mean that the agents cannot share their state, so execution must proceed in a
decentralized fashion. While Dec-POMDPs are typically intractable to solve for
real-world problems, recent research on the use of macro-actions in Dec-POMDPs
has significantly increased the size of problem that can be practically solved
as a Dec-POMDP. We describe this general model, and show how, in contrast to
most existing methods that are specialized to a particular problem class, it
can synthesize control policies that use whatever opportunities for
coordination are present in the problem, while balancing off uncertainty in
outcomes, sensor information, and information about other agents. We use three
variations on a warehouse task to show that a single planner of this type can
generate cooperative behavior using task allocation, direct communication, and
signaling, as appropriate
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