Search Results - motion optimization ((path algorithm) OR (((bees algorithm) OR (based algorithm))))

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  1. 1
    A hybrid sampling-based path planning algorithm for mobile robot navigation in unknown environments

    A hybrid sampling-based path planning algorithm for mobile robot navigation in unknown environments by Khaksar, Weria

    Published 2013
    “…Sampling-based motion planning is a class of randomized path planning algorithms with proven completeness. …”
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  2. 2
    Runtime reduction in optimal multi-query sampling-based motion planning

    Runtime reduction in optimal multi-query sampling-based motion planning by Khaksar W., Sahari K.S.B.M., Ismail F.B., Yousefi M., Ali M.A.

    Published 2023
    “…Algorithms; Dispersions; Manufacture; Query processing; Robotics; High-dimensional; Low dispersions; Optimal solutions; Path length; Planning tasks; Sampling-based; Sampling-based algorithms; Sampling-based motion planning; Motion planning…”
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  3. 3
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Shamsudin, Shamsudin, Mohamaddan, Shahrol

    Published 2023
    “…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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  4. 4
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudzi, Ahmad Athif, Shamsudin, Abu Ubaidah, Shahrol Mohamaddan, Shahrol Mohamaddan

    Published 2023
    “…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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  5. 5
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudzi, Ahmad Athif, Shamsudin, Abu Ubaidah, Mohamaddan, Shahrol

    Published 2023
    “…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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  6. 6
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudzi, Ahmad Athif, Shamsudin, Abu Ubaidah, Mohamaddan, Shahrol

    Published 2023
    “…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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  7. 7
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudzi, Ahmad Athif, Shamsudin, Abu Ubaidah, Mohamaddan, Shahrol

    Published 2022
    “…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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  8. 8
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudzi, Ahmad Athif, Shamsudin, Abu Ubaidah, Mohamaddan, Shahrol

    Published 2023
    “…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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  9. 9
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudz, Ahmad Athif, Shamsudin, Abu Ubaidah, Mohamaddan, Shahrol

    Published 2023
    “…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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  10. 10
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudzi, Ahmad Athif, Shamsudin, Abu Ubaidah, Mohamaddan, Shahrol

    Published 2023
    “…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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  11. 11
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudzi, Ahmad Athif, Shamsudin, Abu Ubaidah, Mohamaddan, Shahrol

    Published 2023
    “…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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  12. 12
    Cyclic Path Planning Of Hyper-Redundant Manipulator Using Whale Optimization Algorithm

    Cyclic Path Planning Of Hyper-Redundant Manipulator Using Whale Optimization Algorithm by Machmudah A., Parman S., Abbasi A., Solihin M.I., Manan T.S.A., Beddu S., Ahmad A., Rasdi N.W.

    Published 2023
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  13. 13
    Minimizing machining airtime motion with an ant colony algorithm

    Minimizing machining airtime motion with an ant colony algorithm by Abdullah, Haslina, Ramli, Rizauddin, Abd Wahab, Dzuraidah, Abu Qudeiri, Jaber

    Published 2016
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  14. 14
    Sampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference system

    Sampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference system by Khaksar, Weria, Tang, Sai Hong, Mohamed Sahari, Khairul Salleh, Khaksar, Mansoor, Toressen, Jim

    Published 2019
    “…Despite the proven advantages of sampling-based motion planning algorithms, their inability to handle online navigation tasks and providing low-cost solutions make them less efficient in practice. …”
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  15. 15
    Sampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference system

    Sampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference system by Khaksar W., Hong T.S., Sahari K.S.M., Khaksar M., Torresen J.

    Published 2023
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  16. 16
    Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms

    Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms by Koh, Johnny Siaw Paw

    Published 2008
    “…This thesis presents a new approach to optimize the performance of a dual beam optical scanning system in terms of its scanning combinations and speed, using Genetic Algorithm (GA). …”
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  17. 17
    A multi-objective parametric algorithm for sensor-based navigation in uncharted terrains

    A multi-objective parametric algorithm for sensor-based navigation in uncharted terrains by Khaksar W., Sahari K.S.M.

    Published 2023
    “…Sensor-based motion planning is one the most challenging tasks in robotics where various approaches and algorithms have been proposed to achieve different planning goals. …”
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  18. 18
    SLOW DRIFT MOTIONS IDENTIFICATION OF FLOATING STRUCTURES USING TIME-VARYING INPUT -OUTPUT MODELS

    SLOW DRIFT MOTIONS IDENTIFICATION OF FLOATING STRUCTURES USING TIME-VARYING INPUT -OUTPUT MODELS by YAZID, EDWAR

    Published 2015
    “…The first step is presenting the backward estimator and combined forward-backward estimator instead of the only forward estimator in the original input-output models; the second step is reformulating the input-output models into a state-space model so that the Kalman Smoother (KS) adaptive filter can be used to estimate the model coefficients; the third step is optimization of KS parameters using evolutionary computing algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Artificial Bee Colony (ABC) to form the PSO-KS, GA-KS and ABC-KS as estimation methods.…”
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  19. 19
    Design, development and performance optimization of a new artificial intelligent controlled multiple-beam optical scanning module

    Design, development and performance optimization of a new artificial intelligent controlled multiple-beam optical scanning module by Koh J.S.P., Aris I.B., Ramachandaramurthy V.K., Bashi S.M., Marhaban M.H.

    Published 2023
    “…This research presents a new approach to optimise the performance of a multiple-beam optical scanning system in terms of its marking combinations and speed, using Genetic Algorithm (GA). The problem has been decomposed into two sub problems; task segregation, where the marking tasks need to be segregated and assigned for each scanner head and path planning where the best combinatorial paths for each scanner are determined in order to minimise the total motion of marking time. …”
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  20. 20
    Design, development and performance optimization of a new artificial intelligent controlled multiple-beam optical scanning module

    Design, development and performance optimization of a new artificial intelligent controlled multiple-beam optical scanning module by Koh J.S.P., Aris I.B., Ramachandaramurthy V.K., Bashi S.M., Marhaban M.H.

    Published 2023
    “…This research presents a new approach to optimise the performance of a multiple-beam optical scanning system in terms of its marking combinations and speed, using Genetic Algorithm (GA). The problem has been decomposed into two sub problems; task segregation, where the marking tasks need to be segregated and assigned for each scanner head and path planning where the best combinatorial paths for each scanner are determined in order to minimise the total motion of marking time. …”
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