A unified approach for different tasks on rings in robot-based computing systems

International audienceA set of autonomous robots have to collaborate in order to accomplish a common task in a ring-topology where neither nodes nor edges are labeled. We present a unified approach to solve three important problems: the exclusive perpetual exploration, the exclusive perpetual search and the gathering problems. In the first problem, each robot aims at visiting each node infinitely often; in perpetual graph searching, the team of robots aims at clearing the whole network infinitely often; and in the gathering problem, all robots must eventually occupy the same node. We investigate these tasks in the Look-Compute- Move distributed computing model where the robots cannot communicate but can perceive the positions of other robots. Each robot is equipped with visibility sensors and motion actuators, and it operates in asynchronous cycles. In each cycle, a robot takes a snapshot of the current global configuration (Look), then, based on the perceived configuration, takes a decision to stay idle or to move to one of its adjacent nodes (Compute), and in the latter case it eventually moves to this neighbor (Move). Moreover, robots are endowed with very weak capabilities. Namely, they are anonymous, oblivious, uniform (execute the same algorithm) and have no common sense of orientation. In this setting, we devise algorithms that, starting from an exclusive rigid (i.e. aperiodic and asymmetric) configuration, solve the three above problems in anonymous ring-topologies

Robot Searching and Gathering on Rings under Minimal Assumptions

Consider a set of mobile robots with minimal capabilities placed over distinct nodes of a discrete anonymous ring. They operate on the basis of the so called \emph{Look}-\emph{Compute}-\emph{Move} cycle. Asynchronously, each robot takes a snapshot of the ring, determining which nodes are either occupied by robots or empty. Based on the observed configuration, it decides whether to move to one of its adjacent nodes or to stay idle. In the first case, it performs the computed move, eventually. The computation also depends on the required task. In this paper, we solve both the well-known \emph{Searching} and \emph{Gathering} tasks. In the literature, most contributions are restricted to a subset of initial configurations. Here, we design two different algorithms and provide a full characterization of the initial configurations that permit the resolution of the problems under minimal assumptions.Nous considérons un ensemble de robots mobiles qui sont placés sur distincts sommets d'un réseau en anneau. Le réseau est anonyme et les robots ont des aptitudes minimales. Ils opérent par des cycles \emph{Observer}-\emph{Calculer}-\emph{Bouger}. Nous résolvons les problémes de la réunion et du nettoyage de graphe dans ce modéle

Automatic construction, maintenance, and optimization of dynamic agent organizations

The goal of this dissertation is to generate organizational structures that increase the overall performance of a multiagent coalition, subject to the system's complex coordination requirements and maintenance of a certain operating point. To this end, a generalized framework capable of producing distributed approximation algorithms based on the new concept of multidirectional graph search is proposed and applied to a family of connectivity problems. It is shown that a wide variety of seemingly unrelated multiagent organization problems live within this family. Su cient conditions are identi ed in which the approach is guaranteed to discover a solution that is within a constant factor of the cost of the optimal solution. The procedure is guaranteed to require no more than linear|and in some well de ned cases logarithmic|communication rounds. A number of examples are given as to how the framework can be applied to create, maintain, and optimize multiagent organizations in the context of real world problems. Finally, algorithmic extensions are introduced that allow for the framework to handle problems in which the agent topology and/or coordination constraints are dynamic, without signi cant consequences to the general runtime, memory, and quality guarantees.Ph.D., Computer Science -- Drexel University, 201

The Cost of Monotonicity in Distributed Graph Searching

International audienceBlin et al. (TCS 2008) proposed a distributed protocol enabling the smallest possible number of searchers to clear any unknown graph in a decentralized manner. However, the strategy that is actually performed lacks of an important property, namely the monotonicity. This paper deals with the smallest number of searchers that are necessary and sufficient to monotonously clear any unknown graph in a decentralized manner. The clearing of the graph is required to be connected, i.e., the clear part of the graph must remain permanently connected, and monotone, i.e., the clear part of the graph only grows. We prove that a distributed protocol clearing any unknown $n$-node graph in a monotone connected way, in a decentralized setting, can achieve but cannot beat competitive ratio of $\Theta(\frac{n}{\log n})$, compared with the centralized minimum number of searchers. Moreover, our lower bound holds even in a synchronous setting, while our constructive upper bound holds even in an asynchronous setting

The Cost of Monotonicity in Distributed Graph Searching

Blin et al. [5] (TCS 2008) proposed a distributed protocol enabling the smallest possible number of searchers to clear any unknown graph in a decentralized manner. However, the strategy that is actually performed lacks of an important property, namely the monotonicity. This paper deals with the smallest number of searchers that are necessary and sufficient to monotonously clear any unknown graph in a decentralized manner. The clearing of the graph is required to be connected, i.e., the clear part of the graph must remain permanently connected, and monotone, i.e., the clear part of the graph only grows. We prove that a distributed protocol clearing any unknown n-node graph in a monotone connected way, in a decentralized setting, can achieve but cannot beat competitive ratio of), compared with the centralized minimum number of searchers. Moreover, our lower bound holds even in a synchronous setting, while our constructive upper bound holds even in an asynchronous setting. Θ ( n log