55,512 research outputs found
Gathering in Dynamic Rings
The gathering problem requires a set of mobile agents, arbitrarily positioned
at different nodes of a network to group within finite time at the same
location, not fixed in advanced.
The extensive existing literature on this problem shares the same fundamental
assumption: the topological structure does not change during the rendezvous or
the gathering; this is true also for those investigations that consider faulty
nodes. In other words, they only consider static graphs. In this paper we start
the investigation of gathering in dynamic graphs, that is networks where the
topology changes continuously and at unpredictable locations.
We study the feasibility of gathering mobile agents, identical and without
explicit communication capabilities, in a dynamic ring of anonymous nodes; the
class of dynamics we consider is the classic 1-interval-connectivity.
We focus on the impact that factors such as chirality (i.e., a common sense
of orientation) and cross detection (i.e., the ability to detect, when
traversing an edge, whether some agent is traversing it in the other
direction), have on the solvability of the problem. We provide a complete
characterization of the classes of initial configurations from which the
gathering problem is solvable in presence and in absence of cross detection and
of chirality. The feasibility results of the characterization are all
constructive: we provide distributed algorithms that allow the agents to
gather. In particular, the protocols for gathering with cross detection are
time optimal. We also show that cross detection is a powerful computational
element.
We prove that, without chirality, knowledge of the ring size is strictly more
powerful than knowledge of the number of agents; on the other hand, with
chirality, knowledge of n can be substituted by knowledge of k, yielding the
same classes of feasible initial configurations
Modelling and Analysis Using GROOVE
In this paper we present case studies that describe how the graph transformation tool GROOVE has been used to model problems from a wide variety of domains. These case studies highlight the wide applicability of GROOVE in particular, and of graph transformation in general. They also give concrete templates for using GROOVE in practice. Furthermore, we use the case studies to analyse the main strong and weak points of GROOVE
Minimizing the Cost of Team Exploration
A group of mobile agents is given a task to explore an edge-weighted graph
, i.e., every vertex of has to be visited by at least one agent. There
is no centralized unit to coordinate their actions, but they can freely
communicate with each other. The goal is to construct a deterministic strategy
which allows agents to complete their task optimally. In this paper we are
interested in a cost-optimal strategy, where the cost is understood as the
total distance traversed by agents coupled with the cost of invoking them. Two
graph classes are analyzed, rings and trees, in the off-line and on-line
setting, i.e., when a structure of a graph is known and not known to agents in
advance. We present algorithms that compute the optimal solutions for a given
ring and tree of order , in time units. For rings in the on-line
setting, we give the -competitive algorithm and prove the lower bound of
for the competitive ratio for any on-line strategy. For every strategy
for trees in the on-line setting, we prove the competitive ratio to be no less
than , which can be achieved by the algorithm.Comment: 25 pages, 4 figures, 5 pseudo-code
Distributed control of multi-robot systems using bifurcating potential fields
The distributed control of multi-robot systems has been shown to have advantages over conventional single robot systems. These include scalability, flexibility and robustness to failures. This paper considers pattern formation and reconfigurability in a multi-robot system using bifurcating potential fields. It is shown how various patterns can be achieved through a simple free parameter change. In addition the stability of the system of robots is proven to ensure that desired behaviours always occur
- ā¦