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Raheem et al., 2018 - Google Patents

Path planning algorithm using D* heuristic method based on PSO in dynamic environment

Raheem et al., 2018

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Document ID
13541483074288841598
Author
Raheem F
Hameed U
et al.
Publication year
Publication venue
American Scientific Research Journal for Engineering, Technology, and Sciences

External Links

Snippet

This paper is devoted to find a short and safe path for robot in environment with moving obstacles such as different objects, humans, animals or other robots. A mixing approach of robot path planning using the heuristic method D star (D*) algorithm based on optimization …
Continue reading at core.ac.uk (PDF) (other versions)

Classifications

    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00Program-control systems
    • G05B2219/30Nc systems
    • G05B2219/50Machine tool, machine tool null till machine tool work handling
    • G05B2219/50109Soft approach, engage, retract, escape, withdraw path for tool to workpiece
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only

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