Temporal Logic Motion Planning and Control With Probabilistic Satisfaction Guarantees

Lahijanian, Morteza; Andersson, Sean B.; Belta, Călin

doi:10.1109/tro.2011.2172150

Cited by 129 publications

(102 citation statements)

References 17 publications

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“…When there is uncertainty in actuation, methods exist for temporal logic planning that employ a Markov decision process (mdp) to model the evolution of the system through the state space [20,21]. The goal in these methods is to compute a control policy over the mdp abstraction to satisfy a high-level task with maximum probability.…”

Section: Related Workmentioning

confidence: 99%

Asymptotically Optimal Stochastic Motion Planning with Temporal Goals

Luna

Lahijanian

Moll

et al. 2015

Springer Tracts in Advanced Robotics

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Abstract. This work presents a planning framework that allows a robot with stochastic action uncertainty to achieve a high-level task given in the form of a temporal logic formula. The objective is to quickly compute a feedback control policy to satisfy the task specification with maximum probability. A top-down framework is proposed that abstracts the motion of a continuous stochastic system to a discrete, boundedparameter Markov decision process (bmdp), and then computes a control policy over the product of the bmdp abstraction and a dfa representing the temporal logic specification. Analysis of the framework reveals that as the resolution of the bmdp abstraction becomes finer, the policy obtained converges to optimal. Simulations show that high-quality policies to satisfy complex temporal logic specifications can be obtained in seconds, orders of magnitude faster than existing methods.

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Section: Related Workmentioning

confidence: 99%

Asymptotically Optimal Stochastic Motion Planning with Temporal Goals

Luna

Lahijanian

Moll

et al. 2015

Springer Tracts in Advanced Robotics

Self Cite

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“…In [18] and [19] routines for the verification of PCTL properties of MDPs are adapted to the strategy synthesis problem. These algorithms are polynomial in the model size, but they are not complete [18] or can handle properties with only one quantitative operator [19]. Finally, [20] studies the synthesis of multi-strategies for MDPs.…”

Section: Related Workmentioning

confidence: 99%

Robust strategy synthesis for probabilistic systems applied to risk-limiting renewable-energy pricing

Puggelli

Sangiovanni‐Vincentelli

Seshia

2014

Proceedings of the 14th International Conference on Embedded Software

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Abstract. The use of economic incentives has been proposed to manage user demand in smart grids that integrate renewable sources of energy to compensate for the intrinsic uncertainty in the prediction of the supply generation. We address the problem of synthesizing optimal energy pricing strategies, while quantitatively constraining the risk due to uncertainty for the network operator and guaranteeing quality-of-service for the users. We use Ellipsoidal Markov Decision Processes (EMDP) to model the decision-making scenario. These models are trained with measured data and allow to quantitatively capture the uncertainty in the prediction of energy generation. We then cast the constrained optimization problem as the strategy synthesis problem for EMDPs, with the goal to maximize the total expected reward constrained to properties expressed using the Probabilistic Computation Tree Logic (PCTL), and propose a novel sound and complete synthesis algorithm. An experimental comparison shows the effectiveness of our method with respect to previous approaches presented in the literature.

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“…In this section, we highlight the research aimed at automated generation of control strategies for robotic vehicles in dynamic environments as reported in [41]. The approach is based on the observation that temporal logic such as CTL can be used to specify the mission goals, for example, "the robot will remain in safe regions until exiting successfully".…”

Section: Autonomous Behaviourmentioning

confidence: 99%

“…Later, they extended the synthesis algorithms to include expected reward specifications to incorporate aspects such as time and energy cost, as well as Boolean combinations of PCTL formulae [41]. They also validated their approach using an experimental testbed that employs iRobot Create 1 , the popular programmable robot.…”

Section: Autonomous Behaviourmentioning

confidence: 99%

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Advances in Quantitative Verification for Ubiquitous Computing

Kwiatkowska

2013

Theoretical Aspects of Computing – ICTAC 2013

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Abstract. Ubiquitous computing, where computers 'disappear' and instead sensor-enabled and software-controlled devices assist us in everyday tasks, has become an established trend. To ensure the safety and reliability of software embedded in these devices, rigorous model-based design methodologies are called for. Quantitative verification, a powerful technique for analysing system models against quantitative properties such as "the probability of a data packet being delivered within 1ms to a nearby Bluetooth device is at least 0.98", has proved useful by detecting and correcting flaws in a number of ubiquitous computing applications. In this paper, we focus on three key aspects of ubiquitous computing: autonomous behaviour, constrained resources and adaptiveness. We summarise recent advances of quantitative verification in relation to these aspects, illustrating each with a case study analysed using the probabilistic model checker PRISM. The paper concludes with an outline of future challenges that remain in this area.

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Temporal Logic Motion Planning and Control With Probabilistic Satisfaction Guarantees

Cited by 129 publications

References 17 publications

Asymptotically Optimal Stochastic Motion Planning with Temporal Goals

Asymptotically Optimal Stochastic Motion Planning with Temporal Goals

Robust strategy synthesis for probabilistic systems applied to risk-limiting renewable-energy pricing

Advances in Quantitative Verification for Ubiquitous Computing

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