The Experts below are selected from a list of 331191 Experts worldwide ranked by ideXlab platform
Michael M. Zavlanos - One of the best experts on this subject based on the ideXlab platform.
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STyLuS*: A Temporal Logic Optimal Control Synthesis Algorithm for Large-Scale Multi-Robot Systems:
The International Journal of Robotics Research, 2020Co-Authors: Yiannis Kantaros, Michael M. ZavlanosAbstract:This article proposes a new highly scalable and asymptotically optimal Control Synthesis algorithm from linear temporal logic specifications, called STyLuS* for large-Scale optimal Temporal Logic S...
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sampling based optimal Control Synthesis for multirobot systems under global temporal tasks
IEEE Transactions on Automatic Control, 2019Co-Authors: Yiannis Kantaros, Michael M. ZavlanosAbstract:This paper proposes a new optimal Control Synthesis algorithm for multirobot systems under global temporal logic tasks. Existing planning approaches under global temporal goals rely on graph search techniques applied to a product automaton constructed among the robots. In this paper, we propose a new sampling-based algorithm that builds incrementally trees that approximate the state space and transitions of the synchronous product automaton. By approximating the product automaton by a tree rather than representing it explicitly, we require much fewer memory resources to store it and motion plans can be found by tracing sequences of parent nodes without the need for sophisticated graph search methods. This significantly increases the scalability of our algorithm compared to existing optimal Control Synthesis methods. We also show that the proposed algorithm is probabilistically complete and asymptotically optimal. Finally, we present numerical experiments showing that our approach can synthesize optimal plans from product automata with billions of states, which is not possible using standard optimal Control Synthesis algorithms or model checkers.
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stylus a temporal logic optimal Control Synthesis algorithm for large scale multi robot systems
arXiv: Robotics, 2018Co-Authors: Yiannis Kantaros, Michael M. ZavlanosAbstract:This paper proposes a new highly scalable and asymptotically optimal Control Synthesis algorithm from linear temporal logic specifications, called $\text{STyLuS}^{*}$ for large-Scale optimal Temporal Logic Synthesis, that is designed to solve complex temporal planning problems in large-scale multi-robot systems. Existing planning approaches with temporal logic specifications rely on graph search techniques applied to a product automaton constructed among the robots. In our previous work, we have proposed a more tractable sampling-based algorithm that builds incrementally trees that approximate the state-space and transitions of the synchronous product automaton and does not require sophisticated graph search techniques. Here, we extend our previous work by introducing bias in the sampling process which is guided by transitions in the B$\ddot{\text{u}}$chi automaton that belong to the shortest path to the accepting states. This allows us to synthesize optimal motion plans from product automata with hundreds of orders of magnitude more states than those that existing optimal Control Synthesis methods or off-the-shelf model checkers can manipulate. We show that $\text{STyLuS}^{*}$ is probabilistically complete and asymptotically optimal and has exponential convergence rate. This is the first time that convergence rate results are provided for sampling-based optimal Control Synthesis methods. We provide simulation results that show that $\text{STyLuS}^{*}$ can synthesize optimal motion plans for very large multi-robot systems which is impossible using state-of-the-art methods.
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Control of magnetic microrobot teams for temporal micromanipulation tasks
IEEE Transactions on Robotics, 2018Co-Authors: Yiannis Kantaros, Sagar Chowdhury, Benjamin V. Johnson, David J. Cappelleri, Michael M. ZavlanosAbstract:In this paper, we present a Control framework that allows magnetic microrobot teams to accomplish complex micromanipulation tasks captured by global linear temporal logic (LTL) formulas. To address this problem, we propose an optimal Control Synthesis method that constructs discrete plans for the robots that satisfy both the assigned tasks as well as proximity constraints between the robots due to the physics of the problem. The proposed algorithm relies on an existing optimal Control Synthesis approach combined with a novel sampling-based technique to reduce the state-space of the product automaton that is associated with the LTL specifications. The synthesized discrete plans are executed by the microrobots independently using local magnetic fields. Simulation studies show that the proposed algorithm can address large-scale planning problems that cannot be solved using existing optimal Control Synthesis approaches. Moreover, we present experimental results that also illustrate the potential of the method in practice. To the best of our knowledge, this is the first Control framework that allows independent Control of teams of magnetic microrobots for temporal micromanipulation tasks.
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Control of magnetic microrobot teams for temporal micromanipulation tasks
arXiv: Robotics, 2018Co-Authors: Yiannis Kantaros, Sagar Chowdhury, Benjamin V. Johnson, David J. Cappelleri, Michael M. ZavlanosAbstract:In this paper, we present a Control framework that allows magnetic microrobot teams to accomplish complex micromanipulation tasks captured by global Linear Temporal Logic (LTL) formulas. To address this problem, we propose an optimal Control Synthesis method that constructs discrete plans for the robots that satisfy both the assigned tasks as well as proximity constraints between the robots due to the physics of the problem. Our proposed algorithm relies on an existing optimal Control Synthesis approach combined with a novel sampling-based technique to reduce the state-space of the product automaton that is associated with the LTL specifications. The synthesized discrete plans are executed by the microrobots independently using local magnetic fields. Simulation studies show that the proposed algorithm can address large-scale planning problems that cannot be solved using existing optimal Control Synthesis approaches. Moreover, we present experimental results that also illustrate the potential of our method in practice. To the best of our knowledge, this is the first Control framework that allows independent Control of teams of magnetic microrobots for temporal micromanipulation tasks.
Dimos V Dimarogonas - One of the best experts on this subject based on the ideXlab platform.
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human in the loop least violating robot Control Synthesis under metric interval temporal logic specifications
European Control Conference, 2018Co-Authors: Sofie Andersson, Dimos V DimarogonasAbstract:Recently, multiple frameworks for Control Synthesis under temporal logic have been suggested. The frameworks allow a user to give one or a set of robots high level tasks of different properties (e.g. temporal, time limited, individual and cooperative). However, the issue of how to handle tasks, which either seem to be or are infeasible, remains unsolved. In this paper we introduce a human to the loop, using the human’s feedback to determine preference towards different types of violations of the tasks. We introduce a metric of violation called hybrid distance. We also suggest a novel framework for synthesizing a least violating Controller with respect to the hybrid distance and the human feedback. Simulation result indicate that the suggested framework gives reasonable estimates of the metric, and that the suggested plans correspond to the expected ones.
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compositional abstraction refinement for Control Synthesis
Nonlinear Analysis: Hybrid Systems, 2018Co-Authors: Pierrejean Meyer, Dimos V DimarogonasAbstract:This paper presents a compositional approach to specification-guided abstraction refinement for Control Synthesis of a nonlinear system associated with a method to over-approximate its reachable se ...
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compositional abstraction refinement for Control Synthesis
arXiv: Systems and Control, 2017Co-Authors: Pierrejean Meyer, Dimos V DimarogonasAbstract:This paper presents a compositional approach to specification-guided abstraction refinement for Control Synthesis of a nonlinear system associated with a method to over-approximate its reachable sets. Given an initial coarse partition of the state space, the Control specification is given as a sequence of the cells of this partition to visit at each sampling time. The dynamics are decomposed into subsystems where some states and inputs are not observed, some states are observed but not Controlled and where assume-guarantee obligations are used on the unControlled states of each subsystem. A finite abstraction is created for each subsystem through a refinement procedure starting from a coarse partition of the state space, then proceeding backwards on the specification sequence to iteratively split the elements of the partition whose coarseness prevents the satisfaction of the specification. Each refined abstraction is associated with a Controller and it is proved that combining these local Controllers can enforce the specification on the original system. The efficiency of the proposed approach compared to other abstraction methods is illustrated in a numerical example.
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abstraction refinement and plan revision for Control Synthesis under high level specifications
IFAC-PapersOnLine, 2017Co-Authors: Pierrejean Meyer, Dimos V DimarogonasAbstract:Abstract This paper presents a novel framework combining abstraction refinement and plan revision for Control Synthesis problems under temporal logic specifications. The Control problem is first solved on a simpler nominal model in order to obtain a satisfying plan to be followed by the real system. A Controller Synthesis is then attempted for an abstraction of the real system to follow this plan. Upon failure of this Synthesis, cost functions are defined to guide towards either refining the initially coarse partition to obtain a finer abstraction, or looking for an alternative plan using the nominal model as above. This tentative Synthesis is then repeated until a plan and an abstraction of the real system able to follow this plan are found. The obtained Controller also ensures that the real system satisfies the initial specification.
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compositional abstraction refinement for Control Synthesis under lasso shaped specifications
Advances in Computing and Communications, 2017Co-Authors: Pierrejean Meyer, Dimos V DimarogonasAbstract:This paper presents a compositional approach to specification-guided abstraction refinement for Control Synthesis of a nonlinear system associated with a method to over-approximate its reachable sets. The Control specification consists in following a lasso-shaped sequence of regions of the state space. The dynamics are decomposed into subsystems with partial Control, partial state observation and possible overlaps between their respective observed state spaces. A finite abstraction is created for each subsystem through a refinement procedure, which starts from a coarse partition of the state space and then proceeds backwards on the lasso sequence to iteratively split the elements of the partition whose coarseness prevents the satisfaction of the specification. The composition of the local Controllers obtained for each subsystem is proved to enforce the desired specification on the original system. This approach is illustrated in a nonlinear numerical example.
Yiannis Kantaros - One of the best experts on this subject based on the ideXlab platform.
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STyLuS*: A Temporal Logic Optimal Control Synthesis Algorithm for Large-Scale Multi-Robot Systems:
The International Journal of Robotics Research, 2020Co-Authors: Yiannis Kantaros, Michael M. ZavlanosAbstract:This article proposes a new highly scalable and asymptotically optimal Control Synthesis algorithm from linear temporal logic specifications, called STyLuS* for large-Scale optimal Temporal Logic S...
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sampling based optimal Control Synthesis for multirobot systems under global temporal tasks
IEEE Transactions on Automatic Control, 2019Co-Authors: Yiannis Kantaros, Michael M. ZavlanosAbstract:This paper proposes a new optimal Control Synthesis algorithm for multirobot systems under global temporal logic tasks. Existing planning approaches under global temporal goals rely on graph search techniques applied to a product automaton constructed among the robots. In this paper, we propose a new sampling-based algorithm that builds incrementally trees that approximate the state space and transitions of the synchronous product automaton. By approximating the product automaton by a tree rather than representing it explicitly, we require much fewer memory resources to store it and motion plans can be found by tracing sequences of parent nodes without the need for sophisticated graph search methods. This significantly increases the scalability of our algorithm compared to existing optimal Control Synthesis methods. We also show that the proposed algorithm is probabilistically complete and asymptotically optimal. Finally, we present numerical experiments showing that our approach can synthesize optimal plans from product automata with billions of states, which is not possible using standard optimal Control Synthesis algorithms or model checkers.
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stylus a temporal logic optimal Control Synthesis algorithm for large scale multi robot systems
arXiv: Robotics, 2018Co-Authors: Yiannis Kantaros, Michael M. ZavlanosAbstract:This paper proposes a new highly scalable and asymptotically optimal Control Synthesis algorithm from linear temporal logic specifications, called $\text{STyLuS}^{*}$ for large-Scale optimal Temporal Logic Synthesis, that is designed to solve complex temporal planning problems in large-scale multi-robot systems. Existing planning approaches with temporal logic specifications rely on graph search techniques applied to a product automaton constructed among the robots. In our previous work, we have proposed a more tractable sampling-based algorithm that builds incrementally trees that approximate the state-space and transitions of the synchronous product automaton and does not require sophisticated graph search techniques. Here, we extend our previous work by introducing bias in the sampling process which is guided by transitions in the B$\ddot{\text{u}}$chi automaton that belong to the shortest path to the accepting states. This allows us to synthesize optimal motion plans from product automata with hundreds of orders of magnitude more states than those that existing optimal Control Synthesis methods or off-the-shelf model checkers can manipulate. We show that $\text{STyLuS}^{*}$ is probabilistically complete and asymptotically optimal and has exponential convergence rate. This is the first time that convergence rate results are provided for sampling-based optimal Control Synthesis methods. We provide simulation results that show that $\text{STyLuS}^{*}$ can synthesize optimal motion plans for very large multi-robot systems which is impossible using state-of-the-art methods.
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Control of magnetic microrobot teams for temporal micromanipulation tasks
IEEE Transactions on Robotics, 2018Co-Authors: Yiannis Kantaros, Sagar Chowdhury, Benjamin V. Johnson, David J. Cappelleri, Michael M. ZavlanosAbstract:In this paper, we present a Control framework that allows magnetic microrobot teams to accomplish complex micromanipulation tasks captured by global linear temporal logic (LTL) formulas. To address this problem, we propose an optimal Control Synthesis method that constructs discrete plans for the robots that satisfy both the assigned tasks as well as proximity constraints between the robots due to the physics of the problem. The proposed algorithm relies on an existing optimal Control Synthesis approach combined with a novel sampling-based technique to reduce the state-space of the product automaton that is associated with the LTL specifications. The synthesized discrete plans are executed by the microrobots independently using local magnetic fields. Simulation studies show that the proposed algorithm can address large-scale planning problems that cannot be solved using existing optimal Control Synthesis approaches. Moreover, we present experimental results that also illustrate the potential of the method in practice. To the best of our knowledge, this is the first Control framework that allows independent Control of teams of magnetic microrobots for temporal micromanipulation tasks.
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Control of magnetic microrobot teams for temporal micromanipulation tasks
arXiv: Robotics, 2018Co-Authors: Yiannis Kantaros, Sagar Chowdhury, Benjamin V. Johnson, David J. Cappelleri, Michael M. ZavlanosAbstract:In this paper, we present a Control framework that allows magnetic microrobot teams to accomplish complex micromanipulation tasks captured by global Linear Temporal Logic (LTL) formulas. To address this problem, we propose an optimal Control Synthesis method that constructs discrete plans for the robots that satisfy both the assigned tasks as well as proximity constraints between the robots due to the physics of the problem. Our proposed algorithm relies on an existing optimal Control Synthesis approach combined with a novel sampling-based technique to reduce the state-space of the product automaton that is associated with the LTL specifications. The synthesized discrete plans are executed by the microrobots independently using local magnetic fields. Simulation studies show that the proposed algorithm can address large-scale planning problems that cannot be solved using existing optimal Control Synthesis approaches. Moreover, we present experimental results that also illustrate the potential of our method in practice. To the best of our knowledge, this is the first Control framework that allows independent Control of teams of magnetic microrobots for temporal micromanipulation tasks.
Hadas Kressgazit - One of the best experts on this subject based on the ideXlab platform.
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event based signal temporal logic Synthesis for single and multi robot tasks
International Conference on Robotics and Automation, 2021Co-Authors: David Gundana, Hadas KressgazitAbstract:We propose a new specification language and Control Synthesis technique for single and multi-robot high-level tasks; these tasks include timing constraints and reaction to environmental events. Specifically, we define Event-based Signal Temporal Logic (STL) and use it to encode tasks that are reactive to unControlled environment events. Our Control Synthesis approach to Event-based STL tasks combines automata and Control barrier functions to produce robot behaviors that satisfy the specification when possible. Our method automatically provides feedback to the user if an Event-based STL task cannot be achieved. We demonstrate the effectiveness of the framework through simulations and physical demonstrations of multi-robot tasks.
Sofie Andersson - One of the best experts on this subject based on the ideXlab platform.
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human in the loop least violating robot Control Synthesis under metric interval temporal logic specifications
European Control Conference, 2018Co-Authors: Sofie Andersson, Dimos V DimarogonasAbstract:Recently, multiple frameworks for Control Synthesis under temporal logic have been suggested. The frameworks allow a user to give one or a set of robots high level tasks of different properties (e.g. temporal, time limited, individual and cooperative). However, the issue of how to handle tasks, which either seem to be or are infeasible, remains unsolved. In this paper we introduce a human to the loop, using the human’s feedback to determine preference towards different types of violations of the tasks. We introduce a metric of violation called hybrid distance. We also suggest a novel framework for synthesizing a least violating Controller with respect to the hybrid distance and the human feedback. Simulation result indicate that the suggested framework gives reasonable estimates of the metric, and that the suggested plans correspond to the expected ones.
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Control Synthesis for multi agent systems under metric interval temporal logic specifications
arXiv: Systems and Control, 2017Co-Authors: Sofie Andersson, Alexandros Nikou, Dimos V DimarogonasAbstract:This paper presents a framework for automatic Synthesis of a Control sequence for multi-agent systems governed by continuous linear dynamics under timed constraints. First, the motion of the agents in the workspace is abstracted into individual Transition Systems (TS). Second, each agent is assigned with an individual formula given in Metric Interval Temporal Logic (MITL) and in parallel, the team of agents is assigned with a collaborative team formula. The proposed method is based on a correct-by-construction Control Synthesis method, and hence guarantees that the resulting closed-loop system will satisfy the specifications. The specifications considers boolean-valued properties under real-time. Extended simulations has been performed in order to demonstrate the efficiency of the proposed Controllers.
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Control Synthesis for Multi-Agent Systems under Metric Interval Temporal Logic Specifications
IFAC-PapersOnLine, 2017Co-Authors: Sofie Andersson, Alexandros Nikou, Dimos V DimarogonasAbstract:This paper presents a framework for automatic Synthesis of a Control sequence for multi-agent systems governed by continuous linear dynamics under timed constraints. First, the motion of the agents in the workspace is abstracted into individual Transition Systems (TS). Second, each agent is assigned with an individual formula given in Metric Interval Temporal Logic (MITL) and in parallel, the team of agents is assigned with a collaborative team formula. The proposed method is based on a correct-by-construction Control Synthesis method, and hence guarantees that the resulting closed-loop system will satisfy the desired specifications. The specifications considers boolean-valued properties under real-time bounds. Extended simulations has been performed in order to demonstrate the efficiency of the proposed methodology.