2,879 research outputs found
Resilience of multi-robot systems to physical masquerade attacks
The advent of autonomous mobile multi-robot systems has driven innovation in both the industrial and defense sectors. The integration of such systems in safety-and security-critical applications has raised concern over their resilience to attack. In this work, we investigate the security problem of a stealthy adversary masquerading as a properly functioning agent. We show that conventional multi-agent pathfinding solutions are vulnerable to these physical masquerade attacks. Furthermore, we provide a constraint-based formulation of multi-agent pathfinding that yields multi-agent plans that are provably resilient to physical masquerade attacks. This formalization leverages inter-agent observations to facilitate introspective monitoring to guarantee resilience.Accepted manuscrip
Specification Patterns for Robotic Missions
Mobile and general-purpose robots increasingly support our everyday life,
requiring dependable robotics control software. Creating such software mainly
amounts to implementing their complex behaviors known as missions. Recognizing
the need, a large number of domain-specific specification languages has been
proposed. These, in addition to traditional logical languages, allow the use of
formally specified missions for synthesis, verification, simulation, or guiding
the implementation. For instance, the logical language LTL is commonly used by
experts to specify missions, as an input for planners, which synthesize the
behavior a robot should have. Unfortunately, domain-specific languages are
usually tied to specific robot models, while logical languages such as LTL are
difficult to use by non-experts. We present a catalog of 22 mission
specification patterns for mobile robots, together with tooling for
instantiating, composing, and compiling the patterns to create mission
specifications. The patterns provide solutions for recurrent specification
problems, each of which detailing the usage intent, known uses, relationships
to other patterns, and---most importantly---a template mission specification in
temporal logic. Our tooling produces specifications expressed in the LTL and
CTL temporal logics to be used by planners, simulators, or model checkers. The
patterns originate from 245 realistic textual mission requirements extracted
from the robotics literature, and they are evaluated upon a total of 441
real-world mission requirements and 1251 mission specifications. Five of these
reflect scenarios we defined with two well-known industrial partners developing
human-size robots. We validated our patterns' correctness with simulators and
two real robots
3D multi-robot patrolling with a two-level coordination strategy
Teams of UGVs patrolling harsh and complex 3D environments can experience interference and spatial conflicts with one another. Neglecting the occurrence of these events crucially hinders both soundness and reliability of a patrolling process. This work presents a distributed multi-robot patrolling technique, which uses a two-level coordination strategy to minimize and explicitly manage the occurrence of conflicts and interference. The first level guides the agents to single out exclusive target nodes on a topological map. This target selection relies on a shared idleness representation and a coordination mechanism preventing topological conflicts. The second level hosts coordination strategies based on a metric representation of space and is supported by a 3D SLAM system. Here, each robot path planner negotiates spatial conflicts by applying a multi-robot traversability function. Continuous interactions between these two levels ensure coordination and conflicts resolution. Both simulations and real-world experiments are presented to validate the performances of the proposed patrolling strategy in 3D environments. Results show this is a promising solution for managing spatial conflicts and preventing deadlocks
Autonomous surveillance robots: a decision-making framework for networked multiagent systems
This article proposes an architecture for an intelligent surveillance system, where the aim is to mitigate the burden on humans in conventional surveillance systems by incorporating intelligent interfaces, computer vision, and autonomous mobile robots. Central to the intelligent surveillance system is the application of research into planning and decision making in this novel context. In this article, we describe the robot surveillance decision problem and explain how the integration of components in our system supports fully automated decision making. Several concrete scenarios deployed in real surveillance environments exemplify both the flexibility of our system to experiment with different representations and algorithms and the portability of our system into a variety of problem contexts. Moreover, these scenarios demonstrate how planning enables robots to effectively balance surveillance objectives, autonomously performing the job of human patrols and responders.This work was partially supported by the Portuguese Fundação para a Ciência e a Tecnologia (FCT), through strategic funding for Institute for Systems and Robotics/Laboratory for Robotics and Engineering Systems (ISR/LARSyS) under grant PEst-OE/EEI/LA0021/2013 and through the Carnegie Mellon Portugal Program under grant CMU-PT/SIA/0023/2009. This study also received national funds through the FCT, with reference UID/CEC/S0021/2013, and through grant FCT UID/EEA/50009/2013 of ISR/LARSyS
Game Theory Models for Multi-Robot Patrolling of Infraestructures
Abstract This work is focused on the problem of performing multi‐robot patrolling for infrastructure security applications in order to protect a known environment at critical facilities. Thus, given a set of robots and a set of points of interest, the patrolling task consists of constantly visiting these points at irregular time intervals for security purposes. Current existing solutions for these types of applications are predictable and inflexible. Moreover, most of the previous centralized and deterministic solutions and only few efforts have been made to integrate dynamic methods. Therefore, the development of new dynamic and decentralized collaborative approaches in order to solve the aforementioned problem by implementing learning models from Game Theory. The model selected in this work that includes belief‐based and reinforcement models as special cases is called Experience‐Weighted Attraction. The problem has been defined using concepts of Graph Theory to represent the environment in order to work with such Game Theory techniques. Finally, the proposed methods have been evaluated experimentally by using a patrolling simulator. The results obtained have been compared with previous availabl
Distributed approach for coverage and patrolling missions with a team of heterogeneous aerial robots under communication constraints
Using aerial robots in area coverage applications
is an emerging topic. These applications need a coverage
path planning algorithm and a coordinated patrolling
plan. This paper proposes a distributed approach to
coordinate a team of heterogeneous UAVs cooperating
efficiently in patrolling missions around irregular areas,
with low communication ranges and memory storage
requirements. Hence it can be used with small‐scale
UAVs with limited and different capabilities. The
presented system uses a modular architecture and solves
the problem by dividing the area between all the robots
according to their capabilities. Each aerial robot performs
a decomposition based algorithm to create covering paths
and a ’one‐to‐one’ coordination strategy to decide the
path segment to patrol. The system is decentralized and
fault‐tolerant. It ensures a finite time to share
information between all the robots and guarantees
convergence to the desired steady state, based on the
maximal minimum frequency criteria. A set of
simulations with a team of quad‐rotors is used to
validate the approach
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