15,011 research outputs found
Statistical Model Checking of Human-Robot Interaction Scenarios
Robots are soon going to be deployed in non-industrial environments. Before
society can take such a step, it is necessary to endow complex robotic systems
with mechanisms that make them reliable enough to operate in situations where
the human factor is predominant. This calls for the development of robotic
frameworks that can soundly guarantee that a collection of properties are
verified at all times during operation. While developing a mission plan, robots
should take into account factors such as human physiology. In this paper, we
present an example of how a robotic application that involves human interaction
can be modeled through hybrid automata, and analyzed by using statistical
model-checking. We exploit statistical techniques to determine the probability
with which some properties are verified, thus easing the state-space explosion
problem. The analysis is performed using the Uppaal tool. In addition, we used
Uppaal to run simulations that allowed us to show non-trivial time dynamics
that describe the behavior of the real system, including human-related
variables. Overall, this process allows developers to gain useful insights into
their application and to make decisions about how to improve it to balance
efficiency and user satisfaction.Comment: In Proceedings AREA 2020, arXiv:2007.1126
Towards formal models and languages for verifiable Multi-Robot Systems
Incorrect operations of a Multi-Robot System (MRS) may not only lead to
unsatisfactory results, but can also cause economic losses and threats to
safety. These threats may not always be apparent, since they may arise as
unforeseen consequences of the interactions between elements of the system.
This call for tools and techniques that can help in providing guarantees about
MRSs behaviour. We think that, whenever possible, these guarantees should be
backed up by formal proofs to complement traditional approaches based on
testing and simulation.
We believe that tailored linguistic support to specify MRSs is a major step
towards this goal. In particular, reducing the gap between typical features of
an MRS and the level of abstraction of the linguistic primitives would simplify
both the specification of these systems and the verification of their
properties. In this work, we review different agent-oriented languages and
their features; we then consider a selection of case studies of interest and
implement them useing the surveyed languages. We also evaluate and compare
effectiveness of the proposed solution, considering, in particular, easiness of
expressing non-trivial behaviour.Comment: Changed formattin
Modelling and analyzing adaptive self-assembling strategies with Maude
Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify, validate and analyse a prominent example of adaptive system: robot swarms equipped with self-assembly strategies. The analysis exploits the statistical model checker PVeStA
Technical Report: A Receding Horizon Algorithm for Informative Path Planning with Temporal Logic Constraints
This technical report is an extended version of the paper 'A Receding Horizon
Algorithm for Informative Path Planning with Temporal Logic Constraints'
accepted to the 2013 IEEE International Conference on Robotics and Automation
(ICRA). This paper considers the problem of finding the most informative path
for a sensing robot under temporal logic constraints, a richer set of
constraints than have previously been considered in information gathering. An
algorithm for informative path planning is presented that leverages tools from
information theory and formal control synthesis, and is proven to give a path
that satisfies the given temporal logic constraints. The algorithm uses a
receding horizon approach in order to provide a reactive, on-line solution
while mitigating computational complexity. Statistics compiled from multiple
simulation studies indicate that this algorithm performs better than a baseline
exhaustive search approach.Comment: Extended version of paper accepted to 2013 IEEE International
Conference on Robotics and Automation (ICRA
MultiVeStA: Statistical Model Checking for Discrete Event Simulators
The modeling, analysis and performance evaluation of large-scale systems are difficult tasks. Due to the size and complexity of the considered systems, an approach typically followed by engineers consists in performing simulations of systems models to obtain statistical estimations of quantitative properties. Similarly, a technique used by computer scientists working on quantitative analysis is Statistical Model Checking (SMC), where rigorous mathematical languages (typically logics) are used to express systems properties of interest. Such properties can then be automatically estimated by tools performing simulations of the model at hand. These property specifications languages, often not popular among engineers, provide a formal, compact and elegant way to express systems properties without needing to hard-code them in the model definition. This paper presents MultiVeStA, a statistical analysis tool which can be easily integrated with existing discrete event simulators, enriching them with efficient distributed statistical analysis and SMC capabilities
Modelling and analyzing adaptive self-assembling strategies with Maude
Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify, validate and analyse a prominent example of adaptive system: robot swarms equipped with self-assembly strategies. The analysis exploits the statistical model checker PVeStA
Technical Report: Distribution Temporal Logic: Combining Correctness with Quality of Estimation
We present a new temporal logic called Distribution Temporal Logic (DTL)
defined over predicates of belief states and hidden states of partially
observable systems. DTL can express properties involving uncertainty and
likelihood that cannot be described by existing logics. A co-safe formulation
of DTL is defined and algorithmic procedures are given for monitoring
executions of a partially observable Markov decision process with respect to
such formulae. A simulation case study of a rescue robotics application
outlines our approach.Comment: More expanded version of "Distribution Temporal Logic: Combining
Correctness with Quality of Estimation" to appear in IEEE CDC 201
Model checking learning agent systems using Promela with embedded C code and abstraction
As autonomous systems become more prevalent, methods for their verification will become more
widely used. Model checking is a formal verification technique that can help ensure the safety of autonomous
systems, but in most cases it cannot be applied by novices, or in its straight \off-the-shelf" form. In order
to be more widely applicable it is crucial that more sophisticated techniques are used, and are presented
in a way that is reproducible by engineers and verifiers alike. In this paper we demonstrate in detail two
techniques that are used to increase the power of model checking using the model checker SPIN. The first
of these is the use of embedded C code within Promela specifications, in order to accurately re
ect robot
movement. The second is to use abstraction together with a simulation relation to allow us to verify multiple
environments simultaneously. We apply these techniques to a fairly simple system in which a robot moves
about a fixed circular environment and learns to avoid obstacles. The learning algorithm is inspired by the
way that insects learn to avoid obstacles in response to pain signals received from their antennae. Crucially,
we prove that our abstraction is sound for our example system { a step that is often omitted but is vital if
formal verification is to be widely accepted as a useful and meaningful approach
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