Corpus ID: 16015233

Control with Probabilistic Signal Temporal Logic

  title={Control with Probabilistic Signal Temporal Logic},
  author={Chanyeol Yoo and Calin Belta},
Autonomous agents often operate in uncertain environments where their decisions are made based on beliefs over states of targets. We are interested in controller synthesis for complex tasks defined over belief spaces. Designing such controllers is challenging due to computational complexity and the lack of expressivity of existing specification languages. In this paper, we propose a probabilistic extension to signal temporal logic (STL) that expresses tasks over continuous belief spaces. We… Expand
Safe Control under Uncertainty with Probabilistic Signal Temporal Logic
This work proposes the new Probabilistic Signal Temporal Logic (PrSTL), an expressive language to define stochastic properties and enforce probabilistic guarantees on them, and presents an efficient algorithm to reason about safe controllers given the constraints derived from the PrSTL specification. Expand
Robust Control for Signal Temporal Logic Specifications using Average Space Robustness
A robust and computationally efficient model predictive control framework for signal temporal logic specifications is proposed and discrete average space robustness is introduced, a novel quantitative semantic for signalporal logic, that is directly incorporated into the cost function of the model predictive controller. Expand
Signal Temporal Logic Synthesis as Probabilistic Inference
The notion of random STL~(RSTL), which extends deterministic STL with random predicates is introduced, which leads to a synthesis-as-inference approach and allows for differentiable, gradient-based synthesis while extending the class of possible uncertain semantics. Expand
Collaborative rover-copter path planning and exploration with temporal logic specifications based on Bayesian update under uncertain environments
This paper investigates a collaborative rover-copter path planning and exploration with temporal logic specifications under uncertain environments by capturing the environmental uncertainties by environmental beliefs of the atomic propositions and synthesizing an exploration policy that the rover intends to follow according to the optimal policy. Expand
An Upper Confidence Bound for Simultaneous Exploration and Exploitation in Heterogeneous Multi-Robot Systems
A novel upper confidence bound for simultaneous exploration and exploitation based on mutual information is derived and a general solution for scout–task coordination is presented using decentralised Monte Carlo tree search. Expand
Robust and Abstraction-free Control of Dynamical Systems under Signal Temporal Logic Tasks
Dynamical systems that provably satisfy given specifications have become increasingly important in many engineering areas. For instance, safety-critical systems such as human-robot networks or autoExpand
Stochastic predictive freeway ramp metering from Signal Temporal Logic specifications
A ramp metering strategy capable of treating exogenous arrivals as random variables since freeway network arrivals are stochastic by nature is proposed and sample average approximation techniques are used to obtain ramp flows that minimize the expectation of the total travel time. Expand
Perception-Based Temporal Logic Planning in Uncertain Semantic Maps
A perception-based LTL planning problem gives rise to an optimal control problem, solved by a novel sampling-based algorithm, that generates open-loop control policies that are updated online to adapt to a continuously learned semantic map. Expand


Temporal Logic Motion Planning and Control With Probabilistic Satisfaction Guarantees
We describe a computational framework for automatic deployment of a robot with sensor and actuator noise from a temporal logic specification over a set of properties that are satisfied by the regionsExpand
Temporal logic control in dynamic environments with probabilistic satisfaction guarantees
This work derives a solution to the automatic deployment of a robot from a temporal logic specification assuming three different levels of knowledge and sensing capabilities of the robot, and describes an optimal solution in one setting and sub-optimal, reactive solutions in the other two. Expand
LTL Control in Uncertain Environments with Probabilistic Satisfaction Guarantees
The problem of generating a control policy for a Markov Decision Process (MDP) such that the probability of satisfying an LTL formula over its states is maximized can be reduced to the problem of creating a robot control strategy that maximizes the probability to accomplish a task. Expand
Distributed Multi-Agent Persistent Surveillance Under Temporal Logic Constraints∗
This work forms a distributed optimization problem, where each agent plans its trajectory based only on local information and uses a formal methods approach to show that any trajectory resulting from the proposed controller satisfies the corresponding TL constraints. Expand
Temporal logic robot control based on automata learning of environmental dynamics
A technique to automatically generate a control policy for a robot moving in an environment that includes elements with unknown, randomly changing behavior, and it is shown that the obtained control policy converges to an optimal one when the partially unknown behavior patterns are fully learned. Expand
Probabilistic Temporal Logic for Motion Planning with Resource Threshold Constraints
This work proposes an extension to probabilistic computation tree logic that expresses real-valued resource threshold constraints, and presents model-checking algorithms that evaluate a piecewise control policy with respect to a formal specification and hard or soft performance guarantees. Expand
Efficient reactive controller synthesis for a fragment of linear temporal logic
A fragment of linear temporal logic is introduced that can be used to specify common motion planning tasks such as safe navigation, response to the environment, persistent coverage, and surveillance. Expand
Receding horizon temporal logic planning for dynamical systems
  • T. Wongpiromsarn, U. Topcu, R. Murray
  • Computer Science
  • Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference
  • 2009
To address the computational difficulties in the synthesis of a discrete planner, this paper presents a receding horizon based scheme for executing finite state automata that essentially reduces the synthesis problem to a set of smaller problems. Expand
Provably-correct stochastic motion planning with safety constraints
Novel algorithms for model checking and policy synthesis in PCTL are proposed that provide a quantitative measure of safety and completion time for a given policy, and synthesise policies that minimise completion time with respect to a given safety threshold. Expand
Receding horizon surveillance with temporal logic specifications
This paper provides a framework which guarantees that the overall trajectory of the system satisfies the desired linear temporal logic specification, while the control decisions are made based on local information obtained in real time. Expand