This paper proposes the right and left motor velocity based multi-objective genetic algorithm controlled navigation method for Two-Wheeled Pioneer P3-DX robot (TWPR) in virtual robot experimentation platform (V-REP) s...
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This paper describes the navigation of an automated Pioneer P3-DX wheeled robot between obstacles using particle swarm optimization (PSO) algorithm tuned feedforward neural network (FNN). This PSO algorithm minimizes ...
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This paper describes the navigation of an automated Pioneer P3-DX wheeled robot between obstacles using particle swarm optimization (PSO) algorithm tuned feedforward neural network (FNN). This PSO algorithm minimizes the mean square error between the actual and predicted values of the FNN. In this work, 2 separate DC motors and 16 ultrasonic sensors have been used for making differential drive steering angle and for collecting the distance from obstacles, respectively. The proposed without tuned FNN and PSO-tuned FNN receives obstacle's distance as inputs form ultrasonic sensors and control the steering angle of a differential drive of automated Pioneer P3-DX wheeled robot as output. We have compared the results between without tuned FNN and PSO-tuned FNN, and it has been found that PSO-tuned FNN gives a better trajectory and takes less distance to reach the target. virtual robot experimentation platform software has been used to design the real-time simulation results. A comparative study between without tuned FNN and PSO-tuned FNN verifies the effectiveness of PSO-tuned FNN for automated Pioneer P3-DX wheeled robot navigation. Also, we have compared this winner PSO-tuned FNN to the previously developed PSO-optimized Fuzzy Logic Controller navigational technique to show the authenticity and real-time implementation of PSO-tuned FNN.
In this paper, a full dynamic model for mobile robot with wheel slip is developed for robust navigation. The type 2 fuzzy logic controller algorithm is implemented and compared to the type 1 fuzzy controller using a d...
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In this paper, a full dynamic model for mobile robot with wheel slip is developed for robust navigation. The type 2 fuzzy logic controller algorithm is implemented and compared to the type 1 fuzzy controller using a differential drive wheels mobile robot (DDWMR) in the presence of wheel slipping from the perspective of control design. We present a dynamic model that explicitly relates perturbations to the vehicle slipping and parameters uncertainties. Both controllers are validated on different behaviors of navigation to evaluate their performances. Furthermore, many advanced robustness tests (modeling errors, localization uncertainties, loss of efficiency of motors, sliding ground) are established to compare the efficiency of each controller. Type 1 and interval type 2 fuzzy logic controllers are validated using V-REP robotic simulator on MATLAB (R) environment and real scenario are considered. The obtained results show clearly the performances of the IT2FLC comparing to the T1FLC controller especially when significant wheel slip and parameters uncertainty are considered.
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