Rapid Convergence

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Osman Hasan - One of the best experts on this subject based on the ideXlab platform.

  • RRT*-SMART: A Rapid Convergence implementation of RRT*
    International Journal of Advanced Robotic Systems, 2013
    Co-Authors: Jauwairia Nasir, Fahad Islam, Mushtaq Khan, Yaşar Ayaz, Usman Malik, Osman Hasan, Mannan Saeed Muhammad
    Abstract:

    Many sampling based algorithms have been introduced recently. Among them Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. Although it ensures probabilistic completeness, it cannot guarantee finding the most optimal path. Rapidly Exploring Random Tree Star (RRT*), a recently proposed extension of RRT, claims to achieve Convergence towards the optimal solution thus ensuring asymptotic optimality along with probabilistic completeness. However, it has been proven to take an infinite time to do so and with a slow Convergence rate. In this paper an extension of RRT*, called as RRT*-Smart, has been proposed to overcome the limitations of RRT*. The goal of the proposed method is to accelerate the rate of Convergence, in order to reach an optimum or near optimum solution at a much faster rate, thus reducing the execution time. The novel approach of the proposed algorithm makes use of two new techniques in RRT*-- Path Optimization and Intelligent Sampling. Simulation results presented in various obstacle cluttered environments along with statistical and mathematical analysis confirm the efficiency of the proposed RRT*- Smart algorithm.

  • RRT*-Smart: Rapid Convergence implementation of RRT* towards optimal solution
    2012 IEEE International Conference on Mechatronics and Automation ICMA 2012, 2012
    Co-Authors: Fahad Islam, Jauwairia Nasir, Yaşar Ayaz, Usman Malik, Osman Hasan
    Abstract:

    Many sampling based algorithms have been introduced recently. Among them Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. Although it ensures probabilistic completeness, it cannot guarantee finding the most optimal path. Rapidly Exploring Random Tree Star (RRT*), a recently proposed extension of RRT, claims to achieve Convergence towards the optimal solution thus ensuring asymptotic optimality along with probabilistic completeness. However, it has been proven to take an infinite time to do so and with a slow Convergence rate. In this paper an extension of RRT*, called as RRT*-Smart, has been proposed to overcome the limitations of RRT*. The goal of the proposed method is to accelerate the rate of Convergence, in order to reach an optimum or near optimum solution at a much faster rate, thus reducing the execution time. The novel approach of the proposed algorithm makes use of two new techniques in RRT*-- Path Optimization and Intelligent Sampling. Simulation results presented in various obstacle cluttered environments along with statistical and mathematical analysis confirm the efficiency of the proposed RRT*- Smart algorithm.

  • RRT∗-Smart: Rapid Convergence implementation of RRT∗ towards optimal solution
    2012 IEEE International Conference on Mechatronics and Automation, 2012
    Co-Authors: Fahad Islam, Jauwairia Nasir, Yaşar Ayaz, Usman Malik, Osman Hasan
    Abstract:

    Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. However, it cannot guarantee finding the most optimal path. A recently proposed extension of RRT, known as Rapidly Exploring Random Tree Star (RRT*), claims to achieve Convergence towards the optimal solution but has been proven to take an infinite time to do so and with a slow Convergence rate. To overcome these limitations, we propose an extension of RRT*, called RRT*-Smart, which aims to accelerate its rate of Convergence and to reach an optimum or near optimum solution at a much faster rate and at a reduced execution time. Our novel algorithm inculcates two new techniques in RRT*: these are path optimization and intelligent sampling. Simulation results presented in various obstacle cluttered environments confirm the efficiency of RRT*-Smart.

Fahad Islam - One of the best experts on this subject based on the ideXlab platform.

  • RRT*-SMART: A Rapid Convergence implementation of RRT*
    International Journal of Advanced Robotic Systems, 2013
    Co-Authors: Jauwairia Nasir, Fahad Islam, Mushtaq Khan, Yaşar Ayaz, Usman Malik, Osman Hasan, Mannan Saeed Muhammad
    Abstract:

    Many sampling based algorithms have been introduced recently. Among them Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. Although it ensures probabilistic completeness, it cannot guarantee finding the most optimal path. Rapidly Exploring Random Tree Star (RRT*), a recently proposed extension of RRT, claims to achieve Convergence towards the optimal solution thus ensuring asymptotic optimality along with probabilistic completeness. However, it has been proven to take an infinite time to do so and with a slow Convergence rate. In this paper an extension of RRT*, called as RRT*-Smart, has been proposed to overcome the limitations of RRT*. The goal of the proposed method is to accelerate the rate of Convergence, in order to reach an optimum or near optimum solution at a much faster rate, thus reducing the execution time. The novel approach of the proposed algorithm makes use of two new techniques in RRT*-- Path Optimization and Intelligent Sampling. Simulation results presented in various obstacle cluttered environments along with statistical and mathematical analysis confirm the efficiency of the proposed RRT*- Smart algorithm.

  • RRT*-Smart: Rapid Convergence implementation of RRT* towards optimal solution
    2012 IEEE International Conference on Mechatronics and Automation ICMA 2012, 2012
    Co-Authors: Fahad Islam, Jauwairia Nasir, Yaşar Ayaz, Usman Malik, Osman Hasan
    Abstract:

    Many sampling based algorithms have been introduced recently. Among them Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. Although it ensures probabilistic completeness, it cannot guarantee finding the most optimal path. Rapidly Exploring Random Tree Star (RRT*), a recently proposed extension of RRT, claims to achieve Convergence towards the optimal solution thus ensuring asymptotic optimality along with probabilistic completeness. However, it has been proven to take an infinite time to do so and with a slow Convergence rate. In this paper an extension of RRT*, called as RRT*-Smart, has been proposed to overcome the limitations of RRT*. The goal of the proposed method is to accelerate the rate of Convergence, in order to reach an optimum or near optimum solution at a much faster rate, thus reducing the execution time. The novel approach of the proposed algorithm makes use of two new techniques in RRT*-- Path Optimization and Intelligent Sampling. Simulation results presented in various obstacle cluttered environments along with statistical and mathematical analysis confirm the efficiency of the proposed RRT*- Smart algorithm.

  • RRT∗-Smart: Rapid Convergence implementation of RRT∗ towards optimal solution
    2012 IEEE International Conference on Mechatronics and Automation, 2012
    Co-Authors: Fahad Islam, Jauwairia Nasir, Yaşar Ayaz, Usman Malik, Osman Hasan
    Abstract:

    Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. However, it cannot guarantee finding the most optimal path. A recently proposed extension of RRT, known as Rapidly Exploring Random Tree Star (RRT*), claims to achieve Convergence towards the optimal solution but has been proven to take an infinite time to do so and with a slow Convergence rate. To overcome these limitations, we propose an extension of RRT*, called RRT*-Smart, which aims to accelerate its rate of Convergence and to reach an optimum or near optimum solution at a much faster rate and at a reduced execution time. Our novel algorithm inculcates two new techniques in RRT*: these are path optimization and intelligent sampling. Simulation results presented in various obstacle cluttered environments confirm the efficiency of RRT*-Smart.

Jauwairia Nasir - One of the best experts on this subject based on the ideXlab platform.

  • RRT*-SMART: A Rapid Convergence implementation of RRT*
    International Journal of Advanced Robotic Systems, 2013
    Co-Authors: Jauwairia Nasir, Fahad Islam, Mushtaq Khan, Yaşar Ayaz, Usman Malik, Osman Hasan, Mannan Saeed Muhammad
    Abstract:

    Many sampling based algorithms have been introduced recently. Among them Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. Although it ensures probabilistic completeness, it cannot guarantee finding the most optimal path. Rapidly Exploring Random Tree Star (RRT*), a recently proposed extension of RRT, claims to achieve Convergence towards the optimal solution thus ensuring asymptotic optimality along with probabilistic completeness. However, it has been proven to take an infinite time to do so and with a slow Convergence rate. In this paper an extension of RRT*, called as RRT*-Smart, has been proposed to overcome the limitations of RRT*. The goal of the proposed method is to accelerate the rate of Convergence, in order to reach an optimum or near optimum solution at a much faster rate, thus reducing the execution time. The novel approach of the proposed algorithm makes use of two new techniques in RRT*-- Path Optimization and Intelligent Sampling. Simulation results presented in various obstacle cluttered environments along with statistical and mathematical analysis confirm the efficiency of the proposed RRT*- Smart algorithm.

  • RRT*-Smart: Rapid Convergence implementation of RRT* towards optimal solution
    2012 IEEE International Conference on Mechatronics and Automation ICMA 2012, 2012
    Co-Authors: Fahad Islam, Jauwairia Nasir, Yaşar Ayaz, Usman Malik, Osman Hasan
    Abstract:

    Many sampling based algorithms have been introduced recently. Among them Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. Although it ensures probabilistic completeness, it cannot guarantee finding the most optimal path. Rapidly Exploring Random Tree Star (RRT*), a recently proposed extension of RRT, claims to achieve Convergence towards the optimal solution thus ensuring asymptotic optimality along with probabilistic completeness. However, it has been proven to take an infinite time to do so and with a slow Convergence rate. In this paper an extension of RRT*, called as RRT*-Smart, has been proposed to overcome the limitations of RRT*. The goal of the proposed method is to accelerate the rate of Convergence, in order to reach an optimum or near optimum solution at a much faster rate, thus reducing the execution time. The novel approach of the proposed algorithm makes use of two new techniques in RRT*-- Path Optimization and Intelligent Sampling. Simulation results presented in various obstacle cluttered environments along with statistical and mathematical analysis confirm the efficiency of the proposed RRT*- Smart algorithm.

  • RRT∗-Smart: Rapid Convergence implementation of RRT∗ towards optimal solution
    2012 IEEE International Conference on Mechatronics and Automation, 2012
    Co-Authors: Fahad Islam, Jauwairia Nasir, Yaşar Ayaz, Usman Malik, Osman Hasan
    Abstract:

    Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. However, it cannot guarantee finding the most optimal path. A recently proposed extension of RRT, known as Rapidly Exploring Random Tree Star (RRT*), claims to achieve Convergence towards the optimal solution but has been proven to take an infinite time to do so and with a slow Convergence rate. To overcome these limitations, we propose an extension of RRT*, called RRT*-Smart, which aims to accelerate its rate of Convergence and to reach an optimum or near optimum solution at a much faster rate and at a reduced execution time. Our novel algorithm inculcates two new techniques in RRT*: these are path optimization and intelligent sampling. Simulation results presented in various obstacle cluttered environments confirm the efficiency of RRT*-Smart.

Yaşar Ayaz - One of the best experts on this subject based on the ideXlab platform.

  • RRT*-SMART: A Rapid Convergence implementation of RRT*
    International Journal of Advanced Robotic Systems, 2013
    Co-Authors: Jauwairia Nasir, Fahad Islam, Mushtaq Khan, Yaşar Ayaz, Usman Malik, Osman Hasan, Mannan Saeed Muhammad
    Abstract:

    Many sampling based algorithms have been introduced recently. Among them Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. Although it ensures probabilistic completeness, it cannot guarantee finding the most optimal path. Rapidly Exploring Random Tree Star (RRT*), a recently proposed extension of RRT, claims to achieve Convergence towards the optimal solution thus ensuring asymptotic optimality along with probabilistic completeness. However, it has been proven to take an infinite time to do so and with a slow Convergence rate. In this paper an extension of RRT*, called as RRT*-Smart, has been proposed to overcome the limitations of RRT*. The goal of the proposed method is to accelerate the rate of Convergence, in order to reach an optimum or near optimum solution at a much faster rate, thus reducing the execution time. The novel approach of the proposed algorithm makes use of two new techniques in RRT*-- Path Optimization and Intelligent Sampling. Simulation results presented in various obstacle cluttered environments along with statistical and mathematical analysis confirm the efficiency of the proposed RRT*- Smart algorithm.

  • RRT*-Smart: Rapid Convergence implementation of RRT* towards optimal solution
    2012 IEEE International Conference on Mechatronics and Automation ICMA 2012, 2012
    Co-Authors: Fahad Islam, Jauwairia Nasir, Yaşar Ayaz, Usman Malik, Osman Hasan
    Abstract:

    Many sampling based algorithms have been introduced recently. Among them Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. Although it ensures probabilistic completeness, it cannot guarantee finding the most optimal path. Rapidly Exploring Random Tree Star (RRT*), a recently proposed extension of RRT, claims to achieve Convergence towards the optimal solution thus ensuring asymptotic optimality along with probabilistic completeness. However, it has been proven to take an infinite time to do so and with a slow Convergence rate. In this paper an extension of RRT*, called as RRT*-Smart, has been proposed to overcome the limitations of RRT*. The goal of the proposed method is to accelerate the rate of Convergence, in order to reach an optimum or near optimum solution at a much faster rate, thus reducing the execution time. The novel approach of the proposed algorithm makes use of two new techniques in RRT*-- Path Optimization and Intelligent Sampling. Simulation results presented in various obstacle cluttered environments along with statistical and mathematical analysis confirm the efficiency of the proposed RRT*- Smart algorithm.

  • RRT∗-Smart: Rapid Convergence implementation of RRT∗ towards optimal solution
    2012 IEEE International Conference on Mechatronics and Automation, 2012
    Co-Authors: Fahad Islam, Jauwairia Nasir, Yaşar Ayaz, Usman Malik, Osman Hasan
    Abstract:

    Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. However, it cannot guarantee finding the most optimal path. A recently proposed extension of RRT, known as Rapidly Exploring Random Tree Star (RRT*), claims to achieve Convergence towards the optimal solution but has been proven to take an infinite time to do so and with a slow Convergence rate. To overcome these limitations, we propose an extension of RRT*, called RRT*-Smart, which aims to accelerate its rate of Convergence and to reach an optimum or near optimum solution at a much faster rate and at a reduced execution time. Our novel algorithm inculcates two new techniques in RRT*: these are path optimization and intelligent sampling. Simulation results presented in various obstacle cluttered environments confirm the efficiency of RRT*-Smart.

Usman Malik - One of the best experts on this subject based on the ideXlab platform.

  • RRT*-SMART: A Rapid Convergence implementation of RRT*
    International Journal of Advanced Robotic Systems, 2013
    Co-Authors: Jauwairia Nasir, Fahad Islam, Mushtaq Khan, Yaşar Ayaz, Usman Malik, Osman Hasan, Mannan Saeed Muhammad
    Abstract:

    Many sampling based algorithms have been introduced recently. Among them Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. Although it ensures probabilistic completeness, it cannot guarantee finding the most optimal path. Rapidly Exploring Random Tree Star (RRT*), a recently proposed extension of RRT, claims to achieve Convergence towards the optimal solution thus ensuring asymptotic optimality along with probabilistic completeness. However, it has been proven to take an infinite time to do so and with a slow Convergence rate. In this paper an extension of RRT*, called as RRT*-Smart, has been proposed to overcome the limitations of RRT*. The goal of the proposed method is to accelerate the rate of Convergence, in order to reach an optimum or near optimum solution at a much faster rate, thus reducing the execution time. The novel approach of the proposed algorithm makes use of two new techniques in RRT*-- Path Optimization and Intelligent Sampling. Simulation results presented in various obstacle cluttered environments along with statistical and mathematical analysis confirm the efficiency of the proposed RRT*- Smart algorithm.

  • RRT*-Smart: Rapid Convergence implementation of RRT* towards optimal solution
    2012 IEEE International Conference on Mechatronics and Automation ICMA 2012, 2012
    Co-Authors: Fahad Islam, Jauwairia Nasir, Yaşar Ayaz, Usman Malik, Osman Hasan
    Abstract:

    Many sampling based algorithms have been introduced recently. Among them Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. Although it ensures probabilistic completeness, it cannot guarantee finding the most optimal path. Rapidly Exploring Random Tree Star (RRT*), a recently proposed extension of RRT, claims to achieve Convergence towards the optimal solution thus ensuring asymptotic optimality along with probabilistic completeness. However, it has been proven to take an infinite time to do so and with a slow Convergence rate. In this paper an extension of RRT*, called as RRT*-Smart, has been proposed to overcome the limitations of RRT*. The goal of the proposed method is to accelerate the rate of Convergence, in order to reach an optimum or near optimum solution at a much faster rate, thus reducing the execution time. The novel approach of the proposed algorithm makes use of two new techniques in RRT*-- Path Optimization and Intelligent Sampling. Simulation results presented in various obstacle cluttered environments along with statistical and mathematical analysis confirm the efficiency of the proposed RRT*- Smart algorithm.

  • RRT∗-Smart: Rapid Convergence implementation of RRT∗ towards optimal solution
    2012 IEEE International Conference on Mechatronics and Automation, 2012
    Co-Authors: Fahad Islam, Jauwairia Nasir, Yaşar Ayaz, Usman Malik, Osman Hasan
    Abstract:

    Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. However, it cannot guarantee finding the most optimal path. A recently proposed extension of RRT, known as Rapidly Exploring Random Tree Star (RRT*), claims to achieve Convergence towards the optimal solution but has been proven to take an infinite time to do so and with a slow Convergence rate. To overcome these limitations, we propose an extension of RRT*, called RRT*-Smart, which aims to accelerate its rate of Convergence and to reach an optimum or near optimum solution at a much faster rate and at a reduced execution time. Our novel algorithm inculcates two new techniques in RRT*: these are path optimization and intelligent sampling. Simulation results presented in various obstacle cluttered environments confirm the efficiency of RRT*-Smart.