1,765 research outputs found
Optimal byzantine resilient convergence in oblivious robot networks
Given a set of robots with arbitrary initial location and no agreement on a
global coordinate system, convergence requires that all robots asymptotically
approach the exact same, but unknown beforehand, location. Robots are
oblivious-- they do not recall the past computations -- and are allowed to move
in a one-dimensional space. Additionally, robots cannot communicate directly,
instead they obtain system related information only via visual sensors. We draw
a connection between the convergence problem in robot networks, and the
distributed \emph{approximate agreement} problem (that requires correct
processes to decide, for some constant , values distance
apart and within the range of initial proposed values). Surprisingly, even
though specifications are similar, the convergence implementation in robot
networks requires specific assumptions about synchrony and Byzantine
resilience. In more details, we prove necessary and sufficient conditions for
the convergence of mobile robots despite a subset of them being Byzantine (i.e.
they can exhibit arbitrary behavior). Additionally, we propose a deterministic
convergence algorithm for robot networks and analyze its correctness and
complexity in various synchrony settings. The proposed algorithm tolerates f
Byzantine robots for (2f+1)-sized robot networks in fully synchronous networks,
(3f+1)-sized in semi-synchronous networks. These bounds are optimal for the
class of cautious algorithms, which guarantee that correct robots always move
inside the range of positions of the correct robots
Robust Environmental Mapping by Mobile Sensor Networks
Constructing a spatial map of environmental parameters is a crucial step to
preventing hazardous chemical leakages, forest fires, or while estimating a
spatially distributed physical quantities such as terrain elevation. Although
prior methods can do such mapping tasks efficiently via dispatching a group of
autonomous agents, they are unable to ensure satisfactory convergence to the
underlying ground truth distribution in a decentralized manner when any of the
agents fail. Since the types of agents utilized to perform such mapping are
typically inexpensive and prone to failure, this results in poor overall
mapping performance in real-world applications, which can in certain cases
endanger human safety. This paper presents a Bayesian approach for robust
spatial mapping of environmental parameters by deploying a group of mobile
robots capable of ad-hoc communication equipped with short-range sensors in the
presence of hardware failures. Our approach first utilizes a variant of the
Voronoi diagram to partition the region to be mapped into disjoint regions that
are each associated with at least one robot. These robots are then deployed in
a decentralized manner to maximize the likelihood that at least one robot
detects every target in their associated region despite a non-zero probability
of failure. A suite of simulation results is presented to demonstrate the
effectiveness and robustness of the proposed method when compared to existing
techniques.Comment: accepted to icra 201
Certified Impossibility Results for Byzantine-Tolerant Mobile Robots
We propose a framework to build formal developments for robot networks using
the COQ proof assistant, to state and to prove formally various properties. We
focus in this paper on impossibility proofs, as it is natural to take advantage
of the COQ higher order calculus to reason about algorithms as abstract
objects. We present in particular formal proofs of two impossibility results
forconvergence of oblivious mobile robots if respectively more than one half
and more than one third of the robots exhibit Byzantine failures, starting from
the original theorems by Bouzid et al.. Thanks to our formalization, the
corresponding COQ developments are quite compact. To our knowledge, these are
the first certified (in the sense of formally proved) impossibility results for
robot networks
Distributed Fault-Tolerant Control for Networked Robots in the Presence of Recoverable/Unrecoverable Faults and Reactive Behaviors
The paper presents an architecture for distributed control of multi-robot systems with
an integrated fault detection, isolation, and recovery strategy. The proposed solution is
based on a distributed observer-controller schema where each robot, by communicating
only with its direct neighbors, is able to estimate the overall state of the system; such
an estimate is then used by the controllers of each robot to achieve global missions
as, for example, centroid and formation tracking. The information exchanged among
the observers is also used to compute residual vectors that allow each robot to detect
failures on anyone of the teammates, even if not in direct communication. The proposed
strategy considers both recoverable and unrecoverable actuator faults as well as it
properly manages the possible activation of reactive local control behaviors of the
robots (e.g., the activation of obstacle avoidance strategy), which generate control inputs
different from those required by the global mission control. In particular, when the robots
are subject to recoverable faults, those are managed at a local level by computing a
proper compensating control action. On the other side, when the robots are subject to
unrecoverable faults, the faults are isolated from anyone of the teammates by means of a
distributed fault detection and isolation strategy; then, the faulty robots are removed from
the team and the mission is rearranged. The proposed strategy is validated via numerical
simulations where the system properly identifies and manages the different cases of
recoverable and unrecoverable actuator faults, as well as it manages the activation of
local reactive control in an integrated case study
Optimal Byzantine Resilient Convergence in Asynchronous Robot Networks
We propose the first deterministic algorithm that tolerates up to
byzantine faults in -sized networks and performs in the asynchronous
CORDA model. Our solution matches the previously established lower bound for
the semi-synchronous ATOM model on the number of tolerated Byzantine robots.
Our algorithm works under bounded scheduling assumptions for oblivious robots
moving in a uni-dimensional space
Fault Recovery in Swarm Robotics Systems using Learning Algorithms
When faults occur in swarm robotic systems they can have a detrimental effect on collective behaviours, to the point that failed individuals may jeopardise the swarm's ability to complete its task. Although fault tolerance is a desirable property of swarm robotic systems, fault recovery mechanisms have not yet been thoroughly explored. Individual robots may suffer a variety of faults, which will affect collective behaviours in different ways, therefore a recovery process is required that can cope with many different failure scenarios. In this thesis, we propose a novel approach for fault recovery in robot swarms that uses Reinforcement Learning and Self-Organising Maps to select the most appropriate recovery strategy for any given scenario. The learning process is evaluated in both centralised and distributed settings. Additionally, we experimentally evaluate the performance of this approach in comparison to random selection of fault recovery strategies, using simulated collective phototaxis, aggregation and foraging tasks as case studies. Our results show that this machine learning approach outperforms random selection, and allows swarm robotic systems to recover from faults that would otherwise prevent the swarm from completing its mission. This work builds upon existing research in fault detection and diagnosis in robot swarms, with the aim of creating a fully fault-tolerant swarm capable of long-term autonomy
Fault-tolerant control policies for multi-robot systems
Throughout the past decade, we have witnessed an active interest in distributed motion coordination algorithms for networked mobile autonomous robots. Often, in multi-robot systems, each robot executing a coordination task is a little cost, a disposable autonomous agent that has ad-hoc sensing or communication capability, and limited mobility. Coordination tasks that a group of multiple mobile robots might perform include formation control, rendezvous, distributed estimation, deployment, flocking, etc. Also, there are challenging tasks that are more suitable for a group of mobile robots than an individual robot, such as surveillance, exploration, or hazardous environmental monitoring. The field has been collectively investigated by many researchers in robotics, control, artificial intelligence, and distributed computing. However, relatively little work has been done on developing algorithms to provide resilience to failures that can occur. The problem is extremely difficult to handle in that any partial failure of a robot is not readily detectable. Some failures in robot resources can have an adverse effect on not only the performance of the robot itself, but also other robots, and the collective task performance as well.
This study presents the development of fault-tolerant distributed control policies for multi-robot systems. We consider two problems: rendezvous and coverage. For the former, the goal is to bring all robots to a common location, while for the latter the goal is to deploy robots to achieve optimal coverage of an environment. We consider the case in which each robot is an autonomous decision maker that is anonymous (i.e., robots are indistinguishable to one another), memoryless (i.e., each robot makes decisions based upon only its current information), and dimensionless (i.e., collision checking is not considered). Each robot has a limited sensing range and can directly estimate the state of only those robots within that sensing range, which induces a network topology for the multi-robot system. We assume that it is not possible for the fault-free robots to identify the faulty robots (e.g., due to the anonymous property of the robots). For each problem, we provide an efficient computational framework and analysis of algorithms, all of which converge in the face of faulty robots under a few assumptions on the network topology and sensing abilities.
A suite of experiments and simulations confirm our theoretical analysis and demonstrate that our proposed algorithms are useful in fault-prone multi-robot systems
Compatibility of convergence algorithms for autonomous mobile robots
We investigate autonomous mobile robots in the Euclidean plane. A robot has a
function called target function to decide the destination from the robots'
positions, and operates in Look-Compute-Move cycles, i.e., identifies the
robots' positions, computes the destination by the target function, and then
moves there. Robots may have different target functions. Let and
be a set of target functions and a problem, respectively. If the robots whose
target functions are chosen from always solve , we say that
is compatible with respect to . If is compatible with respect to
, every target function is an algorithm for (in the
conventional sense). Note that even if both and are algorithms
for , may not be compatible with respect to .
From the view point of compatibility, we investigate the convergence, the
fault tolerant ()-convergence (FC()), the fault tolerant
()-convergence to points (FC()-PO), the fault tolerant
()-convergence to a convex -gon (FC()-CP), and the gathering
problems, assuming crash failures. As a result, we see that these problems are
classified into three groups: The convergence, the FC(1), the FC(1)-PO, and the
FC()-CP compose the first group: Every set of target functions which always
shrink the convex hull of a configuration is compatible. The second group is
composed of the gathering and the FC()-PO for : No set of target
functions which always shrink the convex hull of a configuration is compatible.
The third group, the FC() for , is placed in between. Thus, the
FC(1) and the FC(2), the FC(1)-PO and the FC(2)-PO, and the FC(2) and the
FC(2)-PO are respectively in different groups, despite that the FC(1) and the
FC(1)-PO are in the first group
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