1,380 research outputs found
Ants: Mobile Finite State Machines
Consider the Ants Nearby Treasure Search (ANTS) problem introduced by
Feinerman, Korman, Lotker, and Sereni (PODC 2012), where mobile agents,
initially placed at the origin of an infinite grid, collaboratively search for
an adversarially hidden treasure. In this paper, the model of Feinerman et al.
is adapted such that the agents are controlled by a (randomized) finite state
machine: they possess a constant-size memory and are able to communicate with
each other through constant-size messages. Despite the restriction to
constant-size memory, we show that their collaborative performance remains the
same by presenting a distributed algorithm that matches a lower bound
established by Feinerman et al. on the run-time of any ANTS algorithm
Collaborative search on the plane without communication
We generalize the classical cow-path problem [7, 14, 38, 39] into a question
that is relevant for collective foraging in animal groups. Specifically, we
consider a setting in which k identical (probabilistic) agents, initially
placed at some central location, collectively search for a treasure in the
two-dimensional plane. The treasure is placed at a target location by an
adversary and the goal is to find it as fast as possible as a function of both
k and D, where D is the distance between the central location and the target.
This is biologically motivated by cooperative, central place foraging such as
performed by ants around their nest. In this type of search there is a strong
preference to locate nearby food sources before those that are further away.
Our focus is on trying to find what can be achieved if communication is limited
or altogether absent. Indeed, to avoid overlaps agents must be highly dispersed
making communication difficult. Furthermore, if agents do not commence the
search in synchrony then even initial communication is problematic. This holds,
in particular, with respect to the question of whether the agents can
communicate and conclude their total number, k. It turns out that the knowledge
of k by the individual agents is crucial for performance. Indeed, it is a
straightforward observation that the time required for finding the treasure is
(D + D 2 /k), and we show in this paper that this bound can be matched
if the agents have knowledge of k up to some constant approximation. We present
an almost tight bound for the competitive penalty that must be paid, in the
running time, if agents have no information about k. Specifically, on the
negative side, we show that in such a case, there is no algorithm whose
competitiveness is O(log k). On the other hand, we show that for every constant
\epsilon \textgreater{} 0, there exists a rather simple uniform search
algorithm which is -competitive. In addition, we give
a lower bound for the setting in which agents are given some estimation of k.
As a special case, this lower bound implies that for any constant \epsilon
\textgreater{} 0, if each agent is given a (one-sided)
-approximation to k, then the competitiveness is (log k).
Informally, our results imply that the agents can potentially perform well
without any knowledge of their total number k, however, to further improve,
they must be given a relatively good approximation of k. Finally, we propose a
uniform algorithm that is both efficient and extremely simple suggesting its
relevance for actual biological scenarios
Exploring an Infinite Space with Finite Memory Scouts
Consider a small number of scouts exploring the infinite -dimensional grid
with the aim of hitting a hidden target point. Each scout is controlled by a
probabilistic finite automaton that determines its movement (to a neighboring
grid point) based on its current state. The scouts, that operate under a fully
synchronous schedule, communicate with each other (in a way that affects their
respective states) when they share the same grid point and operate
independently otherwise. Our main research question is: How many scouts are
required to guarantee that the target admits a finite mean hitting time?
Recently, it was shown that is an upper bound on the answer to this
question for any dimension and the main contribution of this paper
comes in the form of proving that this bound is tight for .Comment: Added (forgotten) acknowledgement
Memory Lower Bounds for Randomized Collaborative Search and Applications to Biology
Initial knowledge regarding group size can be crucial for collective
performance. We study this relation in the context of the {\em Ants Nearby
Treasure Search (ANTS)} problem \cite{FKLS}, which models natural cooperative
foraging behavior such as that performed by ants around their nest. In this
problem, (probabilistic) agents, initially placed at some central location,
collectively search for a treasure on the two-dimensional grid. The treasure is
placed at a target location by an adversary and the goal is to find it as fast
as possible as a function of both and , where is the (unknown)
distance between the central location and the target. It is easy to see that
time units are necessary for finding the treasure.
Recently, it has been established that time is sufficient if the agents
know their total number (or a constant approximation of it), and enough
memory bits are available at their disposal \cite{FKLS}. In this paper, we
establish lower bounds on the agent memory size required for achieving certain
running time performances. To the best our knowledge, these bounds are the
first non-trivial lower bounds for the memory size of probabilistic searchers.
For example, for every given positive constant , terminating the
search by time requires agents to use
memory bits. Such distributed computing bounds may provide
a novel, strong tool for the investigation of complex biological systems
Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits
In this paper, we study the collaborative learning model, which concerns the
tradeoff between parallelism and communication overhead in multi-agent
multi-armed bandits. For regret minimization in multi-armed bandits, we present
the first set of tradeoffs between the number of rounds of communication among
the agents and the regret of the collaborative learning process.Comment: 13 pages, 1 figur
Distributed Area Search with a Team of Robots
MEng thesisThe main goal of this thesis is to demonstrate the applicability of the distributed systems paradigm to robotic systems. This goal is accomplished by presenting two solutions to the Distributed Area Search problem: organizing a team of robots to collaborate in the task of searching through an area. The first solution is designed for unreliable robots equipped with a reliable GPS-style localization system. This solution demonstrates the efficiency and fault-tolerance of this type of distributed robotic systems, as well as their applicability to the real world. We present a theoretically near-optimal algorithm for solving Distributed Area Search under this setting, and we also present an implementation of our algorithm on an actual system, consisting of twelve robots. The second solution is designed for a completely autonomous system, without the aid of any centralized subsystem. It demonstrates how a distributed robotic system can solve a problem that is practically unsolvable for a single-robot system
Distributed area search with a team of robots
Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 109-111).The main goal of this thesis is to demonstrate the applicability of the distributed systems paradigm to robotic systems. This goal is accomplished by presenting two solutions to the Distributed Area Search problem: organizing a team of robots to collaborate in the task of searching through an area. The first solution is designed for unreliable robots equipped with a reliable GPS-style localization system. This solution demonstrates the efficiency and fault-tolerance of this type of distributed robotic systems, as well as their applicability to the real world. We present a theoretically near-optimal algorithm for solving Distributed Area Search under this setting, and we also present an implementation of our algorithm on an actual system, consisting of twelve robots. The second solution is designed for a completely autonomous system, without the aid of any centralized subsystem. It demonstrates how a distributed robotic system can solve a problem that is practically unsolvable for a single-robot system.by Velin K. Tzanov.M.Eng.and S.B
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Exploration and Coverage with Swarms of Settling Agents
We consider several algorithms for exploring and filling an unknown,
connected region, by simple, airborne agents. The agents are assumed to be
identical, autonomous, anonymous and to have a finite amount of memory. The
region is modeled as a connected sub-set of a regular grid composed of square
cells. The algorithms described herein are suited for Micro Air Vehicles (MAV)
since these air vehicles enable unobstructed views of the ground below and can
move freely in space at various heights. The agents explore the region by
applying various action-rules based on locally acquired information Some of
them may settle in unoccupied cells as the exploration progresses. Settled
agents become virtual pheromones for the exploration and coverage process,
beacons that subsequently aid the remaining, and still exploring, mobile
agents. We introduce a backward propagating information diffusion process as a
way to implement a deterministic indicator of process termination and guide the
mobile agents. For the proposed algorithms, complete covering of the graph in
finite time is guaranteed when the size of the region is fixed. Bounds on the
coverage times are also derived. Extensive simulation results exhibit good
agreement with the theoretical predictions
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