The Experts below are selected from a list of 22542 Experts worldwide ranked by ideXlab platform
George J Pappas - One of the best experts on this subject based on the ideXlab platform.
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optimal temporal Logic planning in probabilistic semantic maps
International Conference on Robotics and Automation, 2016Co-Authors: Nikolay Atanasov, Ufuk Topcu, George J PappasAbstract:This paper considers robot motion planning under temporal Logic constraints in probabilistic maps obtained by semantic simultaneous localization and mapping (SLAM). The uncertainty in a map distribution presents a great challenge for obtaining correctness guarantees with respect to the linear temporal Logic (LTL) specification. We show that the problem can be Formulated as an optimal control problem in which both the semantic map and the Logic Formula evaluation are stochastic. Our first contribution is to reduce the stochastic control problem for a subclass of LTL to a deterministic shortest path problem by introducing a confidence parameter δ. A robot trajectory obtained from the deterministic problem is guaranteed to have minimum cost and to satisfy the Logic specification in the true environment with probability δ. Our second contribution is to design an admissible heuristic function that guides the planning in the deterministic problem towards satisfying the temporal Logic specification. This allows us to obtain an optimal and very efficient solution using the A* algorithm. The performance and correctness of our approach are demonstrated in a simulated semantic environment using a differential-drive robot.
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Optimal temporal Logic planning in probabilistic semantic maps
Proceedings - IEEE International Conference on Robotics and Automation, 2016Co-Authors: Jie Fu, Nikolay Atanasov, Ufuk Topcu, George J PappasAbstract:This paper considers robot motion planning under temporal Logic constraints in probabilistic maps obtained by semantic simultaneous localization and mapping (SLAM). The uncertainty in a map distribution presents a great challenge for obtaining correctness guarantees with respect to the linear temporal Logic (LTL) specification. We show that the problem can be Formulated as an optimal control problem in which both the semantic map and the Logic Formula evaluation are stochastic. Our first contribution is to reduce the stochastic control problem for a subclass of LTL to a deterministic shortest path problem by introducing a confidence parameter $delta$. A robot trajectory obtained from the deterministic problem is guaranteed to have minimum cost and to satisfy the Logic specification in the true environment with probability $delta$. Our second contribution is to design an admissible heuristic function that guides the planning in the deterministic problem towards satisfying the temporal Logic specification. This allows us to obtain an optimal and very efficient solution using the A* algorithm. The performance and correctness of our approach are demonstrated in a simulated semantic environment using a differential-drive robot.
Dimos V Dimarogonas - One of the best experts on this subject based on the ideXlab platform.
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multi agent planning under local ltl specifications and event based synchronization
arXiv: Systems and Control, 2016Co-Authors: Jana Tumova, Dimos V DimarogonasAbstract:We study the problem of plan synthesis for multi-agent systems, to achieve complex, high-level, long-term goals that are assigned to each agent individually. As the agents might not be capable of satisfying their respective goals by themselves, requests for other agents' collaborations are a part of the task descriptions. We consider that each agent is modeled as a discrete state-transition system and its task specification takes a form of a linear temporal Logic Formula, which may contain requirements and constraints on the other agent's behavior. A traditional automata-based approach to multi-agent plan synthesis from such specifications builds on centralized team planning and full team synchronization after each agents' discrete step, and thus suffers from extreme computational demands. We aim at reducing the computational complexity by decomposing the plan synthesis problem into finite horizon planning problems that are solved iteratively, upon the run of the agents. As opposed to full synchronization, we introduce an event-based synchronization that allows our approach to efficiently adapt to different time durations of different agents' discrete steps. We discuss the correctness of the solution and find assumptions, under which the proposed iterative algorithm leads to provable eventual satisfaction of the desired specifications.
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A receding horizon approach to multi-agent planning from local LTL specifications
Proceedings of the American Control Conference, 2014Co-Authors: Jana Tůmová, Dimos V DimarogonasAbstract:We study the problem of control synthesis for multi-agent systems, to achieve complex, high-level, long-term goals that are assigned to each agent individually. As the agents might not be capable of satisfying their respective goals by themselves, requests for other agents' collaborations are a part of the task descriptions. Particularly, we consider that the task specification takes a form of a linear temporal Logic Formula, which may contain requirements and constraints on the other agent's behavior. A traditional automata-based approach to multi-agent strategy synthesis from such specifications builds on centralized planning for the whole team and thus suffers from extreme computational demands. In this work, we aim at reducing the computational complexity by decomposing the strategy synthesis problem into short horizon planning problems that are solved iteratively, upon the run of the agents. We discuss the correctness of the solution and find assumptions, under which the proposed iterative algorithm leads to provable eventual satisfaction of the desired specifications.
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revising motion planning under linear temporal Logic specifications in partially known workspaces
International Conference on Robotics and Automation, 2013Co-Authors: Meng Guo, Karl Henrik Johansson, Dimos V DimarogonasAbstract:In this paper we propose a generic framework for real-time motion planning based on model-checking and revision. The task specification is given as a Linear Temporal Logic Formula over a finite abstraction of the robot motion. A preliminary motion plan is first generated based on the initial knowledge of the system model. Then real-time information obtained during the runtime is used to update the system model, verify and further revise the motion plan. The implementation and revision of the motion plan are performed in real-time. This framework can be applied to partially-known workspaces and workspaces with large uncertainties. Computer simulations are presented to demonstrate the efficiency of the framework.
Calin Belta - One of the best experts on this subject based on the ideXlab platform.
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temporal Logic inference for classification and prediction from data
International Conference on Hybrid Systems: computation and control, 2014Co-Authors: Zhaodan Kong, Austin Jones, Ana Medina Ayala, Ebru Aydin Gol, Calin BeltaAbstract:This paper presents an inference algorithm that can discover temporal Logic properties of a system from data. Our algorithm operates on finite time system trajectories that are labeled according to whether or not they demonstrate some desirable system properties (e.g. "the car successfully stops before hitting an obstruction"). A temporal Logic Formula that can discriminate between the desirable behaviors and the undesirable ones is constructed. The Formulae also indicate possible causes for each set of behaviors (e.g. "If the speed of the car is greater than 15 m/s within 0.5s of brake application, the obstruction will be struck") which can be used to tune designs or to perform on-line monitoring to ensure the desired behavior. We introduce reactive parameter signal temporal Logic (rPSTL), a fragment of parameter signal temporal Logic (PSTL) that is expressive enough to capture causal, spatial, and temporal relationships in data. We define a partial order over the set of rPSTL Formulae that is based on language inclusion. This order enables a directed search over this set, i.e. given a candidate rPSTL Formula that does not adequately match the observed data, we can automatically construct a Formula that will fit the data at least as well. Two case studies, one involving a cattle herding scenario and one involving a stochastic hybrid gene circuit model, are presented to illustrate our approach.
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ltl receding horizon control for finite deterministic systems
Automatica, 2014Co-Authors: Xu Chu Ding, Mircea Lazar, Calin BeltaAbstract:This paper considers receding horizon control of finite deterministic systems, which must satisfy a high level, rich specification expressed as a linear temporal Logic Formula. Under the assumption that time-varying rewards are associated with states of the system and these rewards can be observed in real-time, the control objective is to maximize the collected reward while satisfying the high level task specification. In order to properly react to the changing rewards, a controller synthesis framework inspired by model predictive control is proposed, where the rewards are locally optimized at each time-step over a finite horizon, and the optimal control computed for the current time-step is applied. By enforcing appropriate constraints, the infinite trajectory produced by the controller is guaranteed to satisfy the desired temporal Logic Formula. Simulation results demonstrate the effectiveness of the approach.
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receding horizon temporal Logic control for finite deterministic systems
arXiv: Optimization and Control, 2012Co-Authors: Xu Chu Ding, Mircea Lazar, Calin BeltaAbstract:This paper considers receding horizon control of finite deterministic systems, which must satisfy a high level, rich specification expressed as a linear temporal Logic Formula. Under the assumption that time-varying rewards are associated with states of the system and they can be observed in real-time, the control objective is to maximize the collected reward while satisfying the high level task specification. In order to properly react to the changing rewards, a controller synthesis framework inspired by model predictive control is proposed, where the rewards are locally optimized at each time-step over a finite horizon, and the immediate optimal control is applied. By enforcing appropriate constraints, the infinite trajectory produced by the controller is guaranteed to satisfy the desired temporal Logic Formula. Simulation results demonstrate the effectiveness of the approach.
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Optimal Path Planning under Temporal Logic Constraints
arXiv: Robotics, 2010Co-Authors: Stephe L. Smith, Jana Tumova, Calin BeltaAbstract:In this paper we present a method for automatically generating optimal robot trajectories satisfying high level mission specifications. The motion of the robot in the environment is modeled as a general transition system, enhanced with weighted transitions. The mission is specified by a general linear temporal Logic Formula. In addition, we require that an optimizing proposition must be repeatedly satisfied. The cost function that we seek to minimize is the maximum time between satisfying instances of the optimizing proposition. For every environment model, and for every Formula, our method computes a robot trajectory which minimizes the cost function. The problem is motivated by applications in robotic monitoring and data gathering. In this setting, the optimizing proposition is satisfied at all locations where data can be uploaded, and the entire Formula specifies a complex (and infinite horizon) data collection mission. Our method utilizes B\"uchi automata to produce an automaton (which can be thought of as a graph) whose runs satisfy the temporal Logic specification. We then present a graph algorithm which computes a path corresponding to the optimal robot trajectory. We also present an implementation for a robot performing a data gathering mission in a road network.
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dealing with nondeterminism in symbolic control
ACM International Conference Hybrid Systems: Computation and Control, 2008Co-Authors: Marius Kloetzer, Calin BeltaAbstract:Abstractions (also called symbolic models) are simple descriptions of continuous and hybrid systems that can be used in analysis and control. They are usually constructed in the form of transition systems with finitely many states. Such abstractions offer a very attractive approach to deal with complexity, while at the same time allowing for rich specification languages. Recent results show that, through the abstraction process, the resulting transition systems can be nondeterministic (i.e.,if an input is applied in a state, several next states are possible). However, the problem of controlling a nondeterministic transition system from a rich specification such as a temporal Logic Formula is not well understood. In this paper, we develop a control strategy for a nondeterministic transition system from a specification given as a Linear Temporal Logic Formula with a deterministic Buchi generator. Our solution is inspired by LTL games on graphs, is complete, and scales polynomially with the size of the Buchi automaton. An example of controlling a linear system from a specification given as a temporal Logic Formula over the regions of its triangulated state space is included for illustration.
Zhaorong Lai - One of the best experts on this subject based on the ideXlab platform.
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dependence in propositional Logic Formula Formula dependence and Formula forgetting application to belief update and conservative extension
arXiv: Artificial Intelligence, 2018Co-Authors: Liangda Fang, Hai Wan, Xianqiao Liu, Biqing Fang, Zhaorong LaiAbstract:Dependence is an important concept for many tasks in artificial intelligence. A task can be executed more efficiently by discarding something independent from the task. In this paper, we propose two novel notions of dependence in propositional Logic: Formula-Formula dependence and Formula forgetting. The first is a relation between Formulas capturing whether a Formula depends on another one, while the second is an operation that returns the strongest consequence independent of a Formula. We also apply these two notions in two well-known issues: belief update and conservative extension. Firstly, we define a new update operator based on Formula-Formula dependence. Furthermore, we reduce conservative extension to Formula forgetting.
Nikolay Atanasov - One of the best experts on this subject based on the ideXlab platform.
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optimal temporal Logic planning in probabilistic semantic maps
International Conference on Robotics and Automation, 2016Co-Authors: Nikolay Atanasov, Ufuk Topcu, George J PappasAbstract:This paper considers robot motion planning under temporal Logic constraints in probabilistic maps obtained by semantic simultaneous localization and mapping (SLAM). The uncertainty in a map distribution presents a great challenge for obtaining correctness guarantees with respect to the linear temporal Logic (LTL) specification. We show that the problem can be Formulated as an optimal control problem in which both the semantic map and the Logic Formula evaluation are stochastic. Our first contribution is to reduce the stochastic control problem for a subclass of LTL to a deterministic shortest path problem by introducing a confidence parameter δ. A robot trajectory obtained from the deterministic problem is guaranteed to have minimum cost and to satisfy the Logic specification in the true environment with probability δ. Our second contribution is to design an admissible heuristic function that guides the planning in the deterministic problem towards satisfying the temporal Logic specification. This allows us to obtain an optimal and very efficient solution using the A* algorithm. The performance and correctness of our approach are demonstrated in a simulated semantic environment using a differential-drive robot.
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Optimal temporal Logic planning in probabilistic semantic maps
Proceedings - IEEE International Conference on Robotics and Automation, 2016Co-Authors: Jie Fu, Nikolay Atanasov, Ufuk Topcu, George J PappasAbstract:This paper considers robot motion planning under temporal Logic constraints in probabilistic maps obtained by semantic simultaneous localization and mapping (SLAM). The uncertainty in a map distribution presents a great challenge for obtaining correctness guarantees with respect to the linear temporal Logic (LTL) specification. We show that the problem can be Formulated as an optimal control problem in which both the semantic map and the Logic Formula evaluation are stochastic. Our first contribution is to reduce the stochastic control problem for a subclass of LTL to a deterministic shortest path problem by introducing a confidence parameter $delta$. A robot trajectory obtained from the deterministic problem is guaranteed to have minimum cost and to satisfy the Logic specification in the true environment with probability $delta$. Our second contribution is to design an admissible heuristic function that guides the planning in the deterministic problem towards satisfying the temporal Logic specification. This allows us to obtain an optimal and very efficient solution using the A* algorithm. The performance and correctness of our approach are demonstrated in a simulated semantic environment using a differential-drive robot.
