1,380 research outputs found

    Ants: Mobile Finite State Machines

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    Consider the Ants Nearby Treasure Search (ANTS) problem introduced by Feinerman, Korman, Lotker, and Sereni (PODC 2012), where nn 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

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    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 Ω\Omega(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 O(log1+ϵk)O( \log^{1+\epsilon} k)-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) kϵk^\epsilon-approximation to k, then the competitiveness is Ω\Omega(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

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    Consider a small number of scouts exploring the infinite dd-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 d+1d + 1 is an upper bound on the answer to this question for any dimension d1d \geq 1 and the main contribution of this paper comes in the form of proving that this bound is tight for d{1,2}d \in \{ 1, 2 \}.Comment: Added (forgotten) acknowledgement

    Memory Lower Bounds for Randomized Collaborative Search and Applications to Biology

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    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, kk (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 kk and DD, where DD is the (unknown) distance between the central location and the target. It is easy to see that T=Ω(D+D2/k)T=\Omega(D+D^2/k) time units are necessary for finding the treasure. Recently, it has been established that O(T)O(T) time is sufficient if the agents know their total number kk (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 ϵ\epsilon, terminating the search by time O(log1ϵkT)O(\log^{1-\epsilon} k \cdot T) requires agents to use Ω(loglogk)\Omega(\log\log k) 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

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    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

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    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

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    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

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    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

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    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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