The Experts below are selected from a list of 59328 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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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.
Wang Jian-yuan - One of the best experts on this subject based on the ideXlab platform.
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ISMIS - A fast temporal texture Synthesis Algorithm using segment genetic Algorithm
Lecture Notes in Computer Science, 2006Co-Authors: Li Wen-hui, Zhang Zhen-hua, Liu Dong-fei, Wang Jian-yuanAbstract:Texture Synthesis is a very active research area in computer vision and graphics, and temporal texture Synthesis is one subset of it. We present a new temporal texture Synthesis Algorithm, in which a segment genetic Algorithm is introduced into the processes of synthesizing videos. In the Algorithm, by analyzing and processing a finite source video clip, Infinite video sequences that are played smoothly in vision can be obtained. Comparing with many temporal texture Synthesis Algorithms nowadays, this Algorithm can get high-quality video results without complicated pre-processing of source video while it improves the efficiency of Synthesis. It is analyzed in this paper that how the population size and the Max number of generations influence the speed and quality of Synthesis.
Jian-yuan Wang - One of the best experts on this subject based on the ideXlab platform.
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A Fast Temporal Texture Synthesis Algorithm Using Segment Genetic Algorithm
Lecture Notes in Computer Science, 2006Co-Authors: Zhen-hua Zhang, Dong-fei Liu, Jian-yuan WangAbstract:Texture Synthesis is a very active research area in computer vision and graphics, and temporal texture Synthesis is one subset of it. We present a new temporal texture Synthesis Algorithm, in which a segment genetic Algorithm is introduced into the processes of synthesizing videos. In the Algorithm, by analyzing and processing a finite source video clip, Infinite video sequences that are played smoothly in vision can be obtained. Comparing with many temporal texture Synthesis Algorithms nowadays, this Algorithm can get high-quality video results without complicated pre-processing of source video while it improves the efficiency of Synthesis. It is analyzed in this paper that how the population size and the Max number of generations influence the speed and quality of Synthesis.
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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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.
Li Wen-hui - One of the best experts on this subject based on the ideXlab platform.
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ISMIS - A fast temporal texture Synthesis Algorithm using segment genetic Algorithm
Lecture Notes in Computer Science, 2006Co-Authors: Li Wen-hui, Zhang Zhen-hua, Liu Dong-fei, Wang Jian-yuanAbstract:Texture Synthesis is a very active research area in computer vision and graphics, and temporal texture Synthesis is one subset of it. We present a new temporal texture Synthesis Algorithm, in which a segment genetic Algorithm is introduced into the processes of synthesizing videos. In the Algorithm, by analyzing and processing a finite source video clip, Infinite video sequences that are played smoothly in vision can be obtained. Comparing with many temporal texture Synthesis Algorithms nowadays, this Algorithm can get high-quality video results without complicated pre-processing of source video while it improves the efficiency of Synthesis. It is analyzed in this paper that how the population size and the Max number of generations influence the speed and quality of Synthesis.
