1,068 research outputs found
From Uncertainty Data to Robust Policies for Temporal Logic Planning
We consider the problem of synthesizing robust disturbance feedback policies
for systems performing complex tasks. We formulate the tasks as linear temporal
logic specifications and encode them into an optimization framework via
mixed-integer constraints. Both the system dynamics and the specifications are
known but affected by uncertainty. The distribution of the uncertainty is
unknown, however realizations can be obtained. We introduce a data-driven
approach where the constraints are fulfilled for a set of realizations and
provide probabilistic generalization guarantees as a function of the number of
considered realizations. We use separate chance constraints for the
satisfaction of the specification and operational constraints. This allows us
to quantify their violation probabilities independently. We compute disturbance
feedback policies as solutions of mixed-integer linear or quadratic
optimization problems. By using feedback we can exploit information of past
realizations and provide feasibility for a wider range of situations compared
to static input sequences. We demonstrate the proposed method on two robust
motion-planning case studies for autonomous driving
Formal controller synthesis for wastewater systems with signal temporal logic constraints: the Barcelona case study
© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/We present an approach for formal controller synthesis of the Barcelona wastewater system. The goal of the controller is to minimize overflow in the system and to reduce environmental contamination (pollution). Due to the influence of sudden and unpredictable weather changes within the Mediterranean climate, we propose robust model predictive control strategy. This approach synthesizes control inputs (i.e., flows through network actuators) that make the system robust to uncertainties in the weather forecast; control inputs are updated in an online fashion to incorporate the newly available measurements from the system and the disturbances. We employ signal temporal logic as a formal mechanism to express the desired behavior of the system. The quantitative semantics of the logic is then used to encode the desired behavior in both the set of constraints and the objective function of the optimization problem. We propose a solution approach for the obtained worst-case optimization, which is based on transforming the nonlinear dynamics of the system into a mixed logical dynamical model. Then, we employ Monte Carlo sampling and dual reformulation to get a mixed integer linear or quadratic programming problem. The proposed approach is applied to a catchment of the Barcelona wastewater system to illustrate its effectiveness.Peer ReviewedPostprint (author's final draft
Signal Temporal Logic Control Synthesis among Uncontrollable Dynamic Agents with Conformal Prediction
The control of dynamical systems under temporal logic specifications among
uncontrollable dynamic agents is challenging due to the agents' a-priori
unknown behavior. Existing works have considered the problem where either all
agents are controllable, the agent models are deterministic and known, or no
safety guarantees are provided. We propose a predictive control synthesis
framework that guarantees, with high probability, the satisfaction of signal
temporal logic (STL) tasks that are defined over the system and uncontrollable
stochastic agents. We use trajectory predictors and conformal prediction to
construct probabilistic prediction regions for each uncontrollable agent that
are valid over multiple future time steps. Specifically, we reduce conservatism
and increase data efficiency compared to existing works by constructing a
normalized prediction region over all agents and time steps. We then formulate
a worst-case mixed integer program (MIP) that accounts for all agent
realizations within the prediction region to obtain control inputs that
provably guarantee task satisfaction with high probability. To efficiently
solve this MIP, we propose an equivalent MIP program based on KKT conditions of
the original one. We illustrate our control synthesis framework on two case
studies
Formal Synthesis of Controllers for Safety-Critical Autonomous Systems: Developments and Challenges
In recent years, formal methods have been extensively used in the design of
autonomous systems. By employing mathematically rigorous techniques, formal
methods can provide fully automated reasoning processes with provable safety
guarantees for complex dynamic systems with intricate interactions between
continuous dynamics and discrete logics. This paper provides a comprehensive
review of formal controller synthesis techniques for safety-critical autonomous
systems. Specifically, we categorize the formal control synthesis problem based
on diverse system models, encompassing deterministic, non-deterministic, and
stochastic, and various formal safety-critical specifications involving logic,
real-time, and real-valued domains. The review covers fundamental formal
control synthesis techniques, including abstraction-based approaches and
abstraction-free methods. We explore the integration of data-driven synthesis
approaches in formal control synthesis. Furthermore, we review formal
techniques tailored for multi-agent systems (MAS), with a specific focus on
various approaches to address the scalability challenges in large-scale
systems. Finally, we discuss some recent trends and highlight research
challenges in this area
Safe Planning And Control Of Autonomous Systems: Robust Predictive Algorithms
Safe autonomous operation of dynamical systems has become one of the most important
research problems. Algorithms for planning and control of such systems are now
nding place on production vehicles, and are fast becoming ubiquitous on the roads
and air-spaces. However most algorithms for such operations, that provide guarantees,
either do not scale well or rely on over-simplifying abstractions that make them
impractical for real world implementations. On the other hand, the algorithms that
are computationally tractable and amenable to implementation generally lack any
guarantees on their behavior.
In this work, we aim to bridge the gap between provable and scalable planning
and control for dynamical systems. The research covered herein can be broadly
categorized into: i) multi-agent planning with temporal logic specications, and ii)
robust predictive control that takes into account the performance of the perception
algorithms used to process information for control.
In the rst part, we focus on multi-robot systems with complicated mission requirements,
and develop a planning algorithm that can take into account a) spatial,
b) temporal and c) reactive mission requirements across multiple robots. The algorithm
not only guarantees continuous time satisfaction of the mission requirements,
but also that the generated trajectories can be followed by the robot.
The other part develops a robust, predictive control algorithm to control the
the dynamical system to follow the trajectories generated by the rst part, within
some desired bounds. This relies on a contract-based framework wherein the control
algorithm controls the dynamical system as well as a resource/quality trade-o in a
perception-based state estimation algorithm. We show that this predictive algorithm
remains feasible with respect to constraints while following a desired trajectory, and
also stabilizes the dynamical system under control.
Through simulations, as well as experiments on actual robotic systems, we show
that the planning method is computationally ecient as well as scales better than
other state-of-the art algorithms that use similar formal specications. We also show
that the robust control algorithm provides better control performance, and is also
computationally more ecient than similar algorithms that do not leverage the resource/
quality trade-o of the perception-based state estimato
Risk of Stochastic Systems for Temporal Logic Specifications
The wide availability of data coupled with the computational advances in
artificial intelligence and machine learning promise to enable many future
technologies such as autonomous driving. While there has been a variety of
successful demonstrations of these technologies, critical system failures have
repeatedly been reported. Even if rare, such system failures pose a serious
barrier to adoption without a rigorous risk assessment. This paper presents a
framework for the systematic and rigorous risk verification of systems. We
consider a wide range of system specifications formulated in signal temporal
logic (STL) and model the system as a stochastic process, permitting
discrete-time and continuous-time stochastic processes. We then define the STL
robustness risk as the risk of lacking robustness against failure. This
definition is motivated as system failures are often caused by missing
robustness to modeling errors, system disturbances, and distribution shifts in
the underlying data generating process. Within the definition, we permit
general classes of risk measures and focus on tail risk measures such as the
value-at-risk and the conditional value-at-risk. While the STL robustness risk
is in general hard to compute, we propose the approximate STL robustness risk
as a more tractable notion that upper bounds the STL robustness risk. We show
how the approximate STL robustness risk can accurately be estimated from system
trajectory data. For discrete-time stochastic processes, we show under which
conditions the approximate STL robustness risk can even be computed exactly. We
illustrate our verification algorithm in the autonomous driving simulator CARLA
and show how a least risky controller can be selected among four neural network
lane keeping controllers for five meaningful system specifications
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