2,991 research outputs found
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
Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions
This paper presents a data-driven approach for multi-robot coordination in
partially-observable domains based on Decentralized Partially Observable Markov
Decision Processes (Dec-POMDPs) and macro-actions (MAs). Dec-POMDPs provide a
general framework for cooperative sequential decision making under uncertainty
and MAs allow temporally extended and asynchronous action execution. To date,
most methods assume the underlying Dec-POMDP model is known a priori or a full
simulator is available during planning time. Previous methods which aim to
address these issues suffer from local optimality and sensitivity to initial
conditions. Additionally, few hardware demonstrations involving a large team of
heterogeneous robots and with long planning horizons exist. This work addresses
these gaps by proposing an iterative sampling based Expectation-Maximization
algorithm (iSEM) to learn polices using only trajectory data containing
observations, MAs, and rewards. Our experiments show the algorithm is able to
achieve better solution quality than the state-of-the-art learning-based
methods. We implement two variants of multi-robot Search and Rescue (SAR)
domains (with and without obstacles) on hardware to demonstrate the learned
policies can effectively control a team of distributed robots to cooperate in a
partially observable stochastic environment.Comment: Accepted to the 2017 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2017
The Viability of Domain Constrained Coalition Formation for Robotic Collectives
Applications, such as military and disaster response, can benefit from
robotic collectives' ability to perform multiple cooperative tasks (e.g.,
surveillance, damage assessments) efficiently across a large spatial area.
Coalition formation algorithms can potentially facilitate collective robots'
assignment to appropriate task teams; however, most coalition formation
algorithms were designed for smaller multiple robot systems (i.e., 2-50
robots). Collectives' scale and domain-relevant constraints (i.e.,
distribution, near real-time, minimal communication) make coalition formation
more challenging. This manuscript identifies the challenges inherent to
designing coalition formation algorithms for very large collectives (e.g., 1000
robots). A survey of multiple robot coalition formation algorithms finds that
most are unable to transfer directly to collectives, due to the identified
system differences; however, auctions and hedonic games may be the most
transferable. A simulation-based evaluation of three auction and hedonic game
algorithms, applied to homogeneous and heterogeneous collectives, demonstrates
that there are collective compositions for which no existing algorithm is
viable; however, the experimental results and literature survey suggest paths
forward.Comment: 46 pages, 9 figures, Swarm Intelligence (under review
An Auction-based Coordination Strategy for Task-Constrained Multi-Agent Stochastic Planning with Submodular Rewards
In many domains such as transportation and logistics, search and rescue, or
cooperative surveillance, tasks are pending to be allocated with the
consideration of possible execution uncertainties. Existing task coordination
algorithms either ignore the stochastic process or suffer from the
computational intensity. Taking advantage of the weakly coupled feature of the
problem and the opportunity for coordination in advance, we propose a
decentralized auction-based coordination strategy using a newly formulated
score function which is generated by forming the problem into task-constrained
Markov decision processes (MDPs). The proposed method guarantees convergence
and at least 50% optimality in the premise of a submodular reward function.
Furthermore, for the implementation on large-scale applications, an approximate
variant of the proposed method, namely Deep Auction, is also suggested with the
use of neural networks, which is evasive of the troublesome for constructing
MDPs. Inspired by the well-known actor-critic architecture, two Transformers
are used to map observations to action probabilities and cumulative rewards
respectively. Finally, we demonstrate the performance of the two proposed
approaches in the context of drone deliveries, where the stochastic planning
for the drone league is cast into a stochastic price-collecting Vehicle Routing
Problem (VRP) with time windows. Simulation results are compared with
state-of-the-art methods in terms of solution quality, planning efficiency and
scalability.Comment: 17 pages, 5 figure
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