160 research outputs found
Sampling-based Approximations with Quantitative Performance for the Probabilistic Reach-Avoid Problem over General Markov Processes
This article deals with stochastic processes endowed with the Markov
(memoryless) property and evolving over general (uncountable) state spaces. The
models further depend on a non-deterministic quantity in the form of a control
input, which can be selected to affect the probabilistic dynamics. We address
the computation of maximal reach-avoid specifications, together with the
synthesis of the corresponding optimal controllers. The reach-avoid
specification deals with assessing the likelihood that any finite-horizon
trajectory of the model enters a given goal set, while avoiding a given set of
undesired states. This article newly provides an approximate computational
scheme for the reach-avoid specification based on the Fitted Value Iteration
algorithm, which hinges on random sample extractions, and gives a-priori
computable formal probabilistic bounds on the error made by the approximation
algorithm: as such, the output of the numerical scheme is quantitatively
assessed and thus meaningful for safety-critical applications. Furthermore, we
provide tighter probabilistic error bounds that are sample-based. The overall
computational scheme is put in relationship with alternative approximation
algorithms in the literature, and finally its performance is practically
assessed over a benchmark case study
Automated Experiment Design for Data-Efficient Verification of Parametric Markov Decision Processes
We present a new method for statistical verification of quantitative
properties over a partially unknown system with actions, utilising a
parameterised model (in this work, a parametric Markov decision process) and
data collected from experiments performed on the underlying system. We obtain
the confidence that the underlying system satisfies a given property, and show
that the method uses data efficiently and thus is robust to the amount of data
available. These characteristics are achieved by firstly exploiting parameter
synthesis to establish a feasible set of parameters for which the underlying
system will satisfy the property; secondly, by actively synthesising
experiments to increase amount of information in the collected data that is
relevant to the property; and finally propagating this information over the
model parameters, obtaining a confidence that reflects our belief whether or
not the system parameters lie in the feasible set, thereby solving the
verification problem.Comment: QEST 2017, 18 pages, 7 figure
Control refinement for discrete-time descriptor systems: a behavioural approach via simulation relations
The analysis of industrial processes, modelled as descriptor systems, is
often computationally hard due to the presence of both algebraic couplings and
difference equations of high order. In this paper, we introduce a control
refinement notion for these descriptor systems that enables analysis and
control design over related reduced-order systems. Utilising the behavioural
framework, we extend upon the standard hierarchical control refinement for
ordinary systems and allow for algebraic couplings inherent to descriptor
systems.Comment: 8 pages, 3 figure
Modularized Control Synthesis for Complex Signal Temporal Logic Specifications
The control synthesis of a dynamic system subject to signal temporal logic (STL) specifications is commonly formulated as a mixed-integer linear programming (MILP) problem. Solving a MILP problem is computationally expensive when the STL formulas are long and complex. In this paper, we propose a framework to transform a long and complex STL formula into a syntactically separate form, i.e., the logical combination of a series of short and simple subformulas with non-overlapping timing intervals. Using this framework, one can easily modularize the synthesis of a complex formula using the synthesis solutions of the subformulas, which improves the efficiency of solving a MILP problem. Specifically, we propose a group of separation principles to guarantee the syntactic equivalence between the original formula and its syntactically separate counterpart. Then, we propose novel methods to solve the largest satisfaction region and the open-loop controller of the specification in a modularized manner. The efficacy of the methods is validated with a robot monitoring case study in simulation. Our work is promising to promote the efficiency of control synthesis for systems with complicated specifications
Data-driven and Model-based Verification: a Bayesian Identification Approach
This work develops a measurement-driven and model-based formal verification
approach, applicable to systems with partly unknown dynamics. We provide a
principled method, grounded on reachability analysis and on Bayesian inference,
to compute the confidence that a physical system driven by external inputs and
accessed under noisy measurements, verifies a temporal logic property. A case
study is discussed, where we investigate the bounded- and unbounded-time safety
of a partly unknown linear time invariant system
Observer-based correct-by-design controller synthesis
Current state-of-the-art correct-by-design controllers are designed for
full-state measurable systems. This work first extends the applicability of
correct-by-design controllers to partially observable LTI systems. Leveraging
2nd order bounds we give a design method that has a quantifiable robustness to
probabilistic disturbances on state transitions and on output measurements. In
a case study from smart buildings we evaluate the new output-based
correct-by-design controller on a physical system with limited sensor
information
Exploration of essential oils as alternatives to conventional fungicides in lupin cultivation
Lupin (Lupinus L.) has the potential to become a true alternative for soybean as protein source, especially in the more temperate regions in the world. However, diseases such as anthracnose (Colletotrichum lupini), gray mold (Botrytis cinerea), and root rot or brown spot (Pleiochaeta setosa) are important threats for lupin production, leading to yield and quality losses. Although conventional fungicides offer a solution to these problems, there is a growing interest in the use of alternative (biological) treatments. In this research, the applicability of four pure plant essential oils (clove oil, juniper oil, tea tree oil, and thyme essential oil) and timbor® (a Thymus vulgaris-derived plant extract) as alternatives for synthetic fungicides towards the lupin pathogens—C. lupini, B. cinerea, and P. setosa—was investigated. The anti-fungal effect of juniper oil was limited, whereas the other oils and timbor® clearly suppressed the growth and spore germination of all fungi. The in vitro experiments revealed that thyme essential oil and timbor® were most effective to inhibit conidial germination and mycelium growth. Furthermore, the results of the pot experiments demonstrated that these Thymus-derived compounds were able to suppress P. setosa brown spot and root rot symptoms. Additional trials are necessary to evaluate the effect of these compounds under field conditions. However, based on these in vitro and pot experiments, it can be concluded that pure essential oils and Thymus-derived plant extracts are promising anti-fungal agents, having the potential to become true alternatives for conventional fungicides in lupin cultivation. To the best of our knowledge, this is the first study demonstrating the potential of plant-derived compounds to treat the main diseases affecting lupin production
Cautious Planning with Incremental Symbolic Perception: Designing Verified Reactive Driving Maneuvers
This work presents a step towards utilizing incrementally-improving symbolic
perception knowledge of the robot's surroundings for provably correct reactive
control synthesis applied to an autonomous driving problem. Combining abstract
models of motion control and information gathering, we show that
assume-guarantee specifications (a subclass of Linear Temporal Logic) can be
used to define and resolve traffic rules for cautious planning. We propose a
novel representation called symbolic refinement tree for perception that
captures the incremental knowledge about the environment and embodies the
relationships between various symbolic perception inputs. The incremental
knowledge is leveraged for synthesizing verified reactive plans for the robot.
The case studies demonstrate the efficacy of the proposed approach in
synthesizing control inputs even in case of partially occluded environments
Energy-Constrained Active Exploration Under Incremental-Resolution Symbolic Perception
In this work, we consider the problem of autonomous exploration in search of
targets while respecting a fixed energy budget. The robot is equipped with an
incremental-resolution symbolic perception module wherein the perception of
targets in the environment improves as the robot's distance from targets
decreases. We assume no prior information about the total number of targets,
their locations as well as their possible distribution within the environment.
This work proposes a novel decision-making framework for the resulting
constrained sequential decision-making problem by first converting it into a
reward maximization problem on a product graph computed offline. It is then
solved online as a Mixed-Integer Linear Program (MILP) where the knowledge
about the environment is updated at each step, combining automata-based and
MILP-based techniques. We demonstrate the efficacy of our approach with the
help of a case study and present empirical evaluation in terms of expected
regret. Furthermore, the runtime performance shows that online planning can be
efficiently performed for moderately-sized grid environments
Automated Formation Control Synthesis from Temporal Logic Specifications
In this paper, we propose a novel framework using formal methods to
synthesize a navigation control strategy for a multi-robot swarm system with
automated formation. The main objective of the problem is to navigate the robot
swarm toward a goal position while passing a series of waypoints. The formation
of the robot swarm should be changed according to the terrain restrictions
around the corresponding waypoint. Also, the motion of the robots should always
satisfy certain runtime safety requirements, such as avoiding collision with
other robots and obstacles. We prescribe the desired waypoints and formation
for the robot swarm using a temporal logic (TL) specification. Then, we
formulate the transition of the waypoints and the formation as a deterministic
finite transition system (DFTS) and synthesize a control strategy subject to
the TL specification. Meanwhile, the runtime safety requirements are encoded
using control barrier functions, and fixed-time control Lyapunov functions
ensure fixed-time convergence. A quadratic program (QP) problem is solved to
refine the DFTS control strategy to generate the control inputs for the robots,
such that both TL specifications and runtime safety requirements are satisfied
simultaneously. This work enlights a novel solution for multi-robot systems
with complicated task specifications. The efficacy of the proposed framework is
validated with a simulation study
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