Genetic Algorithm Based Optimization Technique for Route Planning Of Wheeled Mobile Robot

In this paper, the optimized point stabilization control of nonholonomic wheeled mobile robot has been researched, and point stabilization control of constraint nonholonomic wheeled system has been achieved through the geometry planning method based on Bézier, and constrained system is converted to un-constraint optimized question based on introducing penalty functions. The optimized control parameters has been got through Hooke-Jeeves method to achieve the perfect combination of the optimized route planning and optimized control, which can make the robot achieve the target pose under the constraint condition and improve the smooth of move path and reduce the stable time. The controller's validity is proved by the experiment.

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A COMPARATIVE STUDY FOR WHEELED MOBILE ROBOT PATH PLANNING BASED ON MODIFIED INTELLIGENT ALGORITHMS

THE IRAQI JOURNAL FOR MECHANICAL AND MATERIALS ENGINEERING ◽

10.32852/iqjfmme.v19i1.266 ◽

2019 ◽

Vol 19 (1) ◽

pp. 60-74 ◽

Cited By ~ 1

Author(s):

Muna M AL -Nayar ◽

Khulood E Dagher ◽

Esraa A Hadi

Keyword(s):

Path Planning ◽

Mobile Robot ◽

Optimization Technique ◽

Pso Algorithm ◽

Wheeled Mobile Robot ◽

Robot Path Planning ◽

Home Schools ◽

The Social ◽

Being There ◽

Robot Path

From the time being, there are even instances for application of mobile robots in our lifelike in home, schools, hospitals, etc. The goal of this paper is to plan a path and minimizing thepath lengths with obstacles avoidance for a mobile robot in static environment. In this work wedepict the issue of off-line wheeled mobile robot (WMR) path planning, which best route forwheeled mobile robot from a start point to a target at a plane environment represented by 2-Dwork space. A modified optimization technique to solve the problem of path planning problemusing particle swarm optimization (PSO) method is given. PSO is a swarm intelligence basedstochastic optimization technique which imitate the social behavior of fish schooling or birdflocking, was applied to locate the optimum route for mobile robot so as to reach a target.Simulation results, which executed using MATLAB 2014 programming language, confirmedthat the suggested algorithm outperforms the standard version of PSO algorithm with the sameenvironment conditions by providing the shortest path for mobile robot.

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Chemical Source Searching by Controlling a Wheeled Mobile Robot to Follow an Online Planned Route in Outdoor Field Environments

Sensors ◽

10.3390/s19020426 ◽

2019 ◽

Vol 19 (2) ◽

pp. 426 ◽

Cited By ~ 6

Author(s):

Ji-Gong Li ◽

Meng-Li Cao ◽

Qing-Hao Meng

Keyword(s):

Mobile Robot ◽

Field Experiments ◽

Route Planning ◽

Wheeled Mobile Robot ◽

Time Varying ◽

Chemical Plume ◽

Average Success Rate ◽

Historical Trajectory ◽

Airflow Condition ◽

Dynamic Route

In this paper, we present an estimation-based route planning (ERP) method for chemical source searching using a wheeled mobile robot and validate its effectiveness with outdoor field experiments. The ERP method plans a dynamic route for the robot to follow to search for a chemical source according to time-varying wind and an estimated chemical-patch path (C-PP), where C-PP is the historical trajectory of a chemical patch detected by the robot, and normally different from the chemical plume formed by the spatial distribution of all chemical patches previously released from the source. Owing to the limitations of normal gas sensors and actuation capability of ground mobile robots, it is quite hard for a single robot to directly trace the intermittent and rapidly swinging chemical plume resulting from the frequent and random changes of wind speed and direction in outdoor field environments. In these circumstances, tracking the C-PP originating from the chemical source back could help the robot approach the source. The proposed ERP method was tested in two different outdoor fields using a wheeled mobile robot. Experimental results indicate that the robot adapts to the time-varying airflow condition, arriving at the chemical source with an average success rate and approaching effectiveness of about 90% and 0.4~0.6, respectively.

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