[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Batti et al., 2019 - Google Patents

Mobile robot obstacle avoidance in labyrinth environment using fuzzy logic approach

Batti et al., 2019

View PDF
Document ID
2861822978299608956
Author
Batti H
Jabeur C
Seddik H
Publication year
Publication venue
2019 International Conference on Control, Automation and Diagnosis (ICCAD)

External Links

Snippet

The navigation of autonomous mobile robots has in recent times gained interest from many researchers in different areas such as in the industrial, agricultural, and military sectors. This paper presents the development of fuzzy logic controller (FLC) motion for guiding a …
Continue reading at www.researchgate.net (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
    • 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
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02Control of position or course in two dimensions
    • G05D1/021Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • 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
    • G05B13/04Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02Control of position or course in two dimensions
    • G05D1/021Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0268Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02Control of position or course in two dimensions
    • G05D1/021Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0255Control of position or course in two dimensions specially adapted to land vehicles using acoustic signals, e.g. ultra-sonic singals
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00Programme-control systems
    • G05B19/02Programme-control systems electric
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00Systems involving the use of models or simulators of said systems
    • G05B17/02Systems involving the use of models or simulators of said systems electric

Similar Documents

Publication Publication Date Title
Pradhan et al. Fuzzy logic techniques for navigation of several mobile robots
Duguleana et al. Neural networks based reinforcement learning for mobile robots obstacle avoidance
Motlagh et al. An expert fuzzy cognitive map for reactive navigation of mobile robots
Jaradat et al. Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field
Abiyev et al. Navigation of mobile robots in the presence of obstacles
Batti et al. Mobile robot obstacle avoidance in labyrinth environment using fuzzy logic approach
Di Mario et al. A comparison of PSO and reinforcement learning for multi-robot obstacle avoidance
Batti et al. Autonomous smart robot for path predicting and finding in maze based on fuzzy and neuro‐fuzzy approaches
Ma et al. Bi-Risk-RRT based efficient motion planning for autonomous ground vehicles
Chehelgami et al. Safe deep learning-based global path planning using a fast collision-free path generator
Lei et al. A fuzzy behaviours fusion algorithm for mobile robot real-time path planning in unknown environment
Sun et al. Event-triggered reconfigurable reinforcement learning motion-planning approach for mobile robot in unknown dynamic environments
Van Nguyen et al. Behavior-based navigation of mobile robot in unknown environments using fuzzy logic and multi-objective optimization
Boufera et al. Fuzzy inference system optimization by evolutionary approach for mobile robot navigation
Raiesdana A hybrid method for industrial robot navigation
Hewawasam et al. Development and bench-marking of agoraphilic navigation algorithm in dynamic environment
Cherroun et al. Fuzzy logic and reinforcement learning based approaches for mobile robot navigation in unknown environment
Lewis et al. Virtual testing and policy deployment framework for autonomous navigation of an unmanned ground vehicle using reinforcement learning
Obe et al. Fuzzy control of autonomous mobile robot
Bodaragama et al. Path Planning for Moving Robots in an Unknown Dynamic Area Using RND-Based Deep Reinforcement Learning
Apandi et al. The integration of fuzzy logic system for obstacle avoidance behavior of mobile robot
Cherroun et al. Type-1 and Type-2 Fuzzy Techniques: Application to Robotic Systems
Zarei et al. Experimental study on optimal motion planning of wheeled mobile robot using convex optimization and receding horizon concept
Kashyap et al. Navigation for multi-humanoid using MFO-aided reinforcement learning approach
Yildirim et al. Learning Social Navigation from Demonstrations with Deep Neural Networks