11,754 research outputs found
Coordination of Mobile Mules via Facility Location Strategies
In this paper, we study the problem of wireless sensor network (WSN)
maintenance using mobile entities called mules. The mules are deployed in the
area of the WSN in such a way that would minimize the time it takes them to
reach a failed sensor and fix it. The mules must constantly optimize their
collective deployment to account for occupied mules. The objective is to define
the optimal deployment and task allocation strategy for the mules, so that the
sensors' downtime and the mules' traveling distance are minimized. Our
solutions are inspired by research in the field of computational geometry and
the design of our algorithms is based on state of the art approximation
algorithms for the classical problem of facility location. Our empirical
results demonstrate how cooperation enhances the team's performance, and
indicate that a combination of k-Median based deployment with closest-available
task allocation provides the best results in terms of minimizing the sensors'
downtime but is inefficient in terms of the mules' travel distance. A
k-Centroid based deployment produces good results in both criteria.Comment: 12 pages, 6 figures, conferenc
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
Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning
Swarm systems constitute a challenging problem for reinforcement learning
(RL) as the algorithm needs to learn decentralized control policies that can
cope with limited local sensing and communication abilities of the agents.
While it is often difficult to directly define the behavior of the agents,
simple communication protocols can be defined more easily using prior knowledge
about the given task. In this paper, we propose a number of simple
communication protocols that can be exploited by deep reinforcement learning to
find decentralized control policies in a multi-robot swarm environment. The
protocols are based on histograms that encode the local neighborhood relations
of the agents and can also transmit task-specific information, such as the
shortest distance and direction to a desired target. In our framework, we use
an adaptation of Trust Region Policy Optimization to learn complex
collaborative tasks, such as formation building and building a communication
link. We evaluate our findings in a simulated 2D-physics environment, and
compare the implications of different communication protocols.Comment: 13 pages, 4 figures, version 2, accepted at ANTS 201
Analysis of Dynamic Task Allocation in Multi-Robot Systems
Dynamic task allocation is an essential requirement for multi-robot systems
operating in unknown dynamic environments. It allows robots to change their
behavior in response to environmental changes or actions of other robots in
order to improve overall system performance. Emergent coordination algorithms
for task allocation that use only local sensing and no direct communication
between robots are attractive because they are robust and scalable. However, a
lack of formal analysis tools makes emergent coordination algorithms difficult
to design. In this paper we present a mathematical model of a general dynamic
task allocation mechanism. Robots using this mechanism have to choose between
two types of task, and the goal is to achieve a desired task division in the
absence of explicit communication and global knowledge. Robots estimate the
state of the environment from repeated local observations and decide which task
to choose based on these observations. We model the robots and observations as
stochastic processes and study the dynamics of the collective behavior.
Specifically, we analyze the effect that the number of observations and the
choice of the decision function have on the performance of the system. The
mathematical models are validated in a multi-robot multi-foraging scenario. The
model's predictions agree very closely with experimental results from
sensor-based simulations.Comment: Preprint version of the paper published in International Journal of
Robotics, March 2006, Volume 25, pp. 225-24
Managing a Fleet of Autonomous Mobile Robots (AMR) using Cloud Robotics Platform
In this paper, we provide details of implementing a system for managing a
fleet of autonomous mobile robots (AMR) operating in a factory or a warehouse
premise. While the robots are themselves autonomous in its motion and obstacle
avoidance capability, the target destination for each robot is provided by a
global planner. The global planner and the ground vehicles (robots) constitute
a multi agent system (MAS) which communicate with each other over a wireless
network. Three different approaches are explored for implementation. The first
two approaches make use of the distributed computing based Networked Robotics
architecture and communication framework of Robot Operating System (ROS) itself
while the third approach uses Rapyuta Cloud Robotics framework for this
implementation. The comparative performance of these approaches are analyzed
through simulation as well as real world experiment with actual robots. These
analyses provide an in-depth understanding of the inner working of the Cloud
Robotics Platform in contrast to the usual ROS framework. The insight gained
through this exercise will be valuable for students as well as practicing
engineers interested in implementing similar systems else where. In the
process, we also identify few critical limitations of the current Rapyuta
platform and provide suggestions to overcome them.Comment: 14 pages, 15 figures, journal pape
- …