A Method for Path Planning Strategy and Navigation of Service Robot

AbstractThis paper presents our work on the development of Path Planning Strategy and Navigation by using ANFIS(Adaptive Neuro-Fuzzy Inference System)controller for a vision-based service robot. The robot will deliver a cup to a recognized customer and a black line as the guiding track for navigating a robot with a single camera. The contribution of this research includes a proposed architecture of ANFIS controller for vision-based service robot integrated with improved face recognition system using PCA, and the algorithm for moving obstacle avoidance. We also propose a path planning algorithm based on Dijkstra's algorithm to obtain the shortest path for robot to move from the starting point to the destination. In order to avoid moving obstacles, we have proposed an algorithm using binaural ultrasonic sensors. The service robot called Srikandi is also equipped with 4 DOF arm and a framework of face recognition system. The proposed path planning strategy and navigation was tested empirically and proved effective.

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Online Training for Face Recognition System Using Improved PCA

ComTech Computer Mathematics and Engineering Applications ◽

10.21512/comtech.v2i2.2952 ◽

2011 ◽

Vol 2 (2) ◽

pp. 1303

Author(s):

Widodo Budiharto

Keyword(s):

Face Recognition ◽

Stereo Vision ◽

Indoor Environment ◽

Online Training ◽

Recognition System ◽

Service Robot ◽

Challenging Problem ◽

Face Database ◽

Face Images ◽

Face Recognition System

The variation in illumination is one of the main challenging problem for face recognition. It has been proven that in face recognition, differences caused by illumination variations are more significant than differences between individuals. Recognizing face reliably across changes in pose and illumination using PCA has proved to be a much harder problem because eigenfaces method comparing the intensity of the pixel. To solve this problem, this research proposes an online face recognition system using improved PCA for a service robot in indoor environment based on stereo vision. Tested images are improved by generating random values for varying the intensity of face images. A program for online training is also developed where the tested images are captured real-time from camera. Varying illumination in tested images will increase the accuracy using ITS face database which its accuracy is 95.5 %, higher than ATT face database’s as 95.4% and Indian face database’s as 72%. The results from this experiment are still evaluated to be improved in the future.

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An Improved Face Recognition System for Service Robot Using Stereo Vision

Face Analysis, Modeling and Recognition Systems ◽

10.5772/17993 ◽

2011 ◽

Cited By ~ 7

Author(s):

Widodo Budiharto ◽

Ari Santoso ◽

Djoko Purwanto ◽

Achmad Jazidie

Keyword(s):

Face Recognition ◽

Stereo Vision ◽

Recognition System ◽

Service Robot ◽

Face Recognition System

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Face recognition system using adaptive neurofuzzy inference system

2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT) ◽

10.1109/iceeccot.2017.8284546 ◽

2017 ◽

Cited By ~ 1

Author(s):

Tadi Chandrasekhar ◽

Ch. Sumanth Kumar

Keyword(s):

Face Recognition ◽

Recognition System ◽

Inference System ◽

Face Recognition System ◽

Adaptive Neurofuzzy Inference System

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Investigating Reduced Path Planning Strategy for Differential Wheeled Mobile Robot

Robotica ◽

10.1017/s0263574719000572 ◽

2019 ◽

Vol 38 (2) ◽

pp. 235-255 ◽

Cited By ~ 2

Author(s):

Raouf Fareh ◽

Mohammed Baziyad ◽

Mohammad H. Rahman ◽

Tamer Rabie ◽

Maamar Bettayeb

Keyword(s):

Path Planning ◽

Mobile Robot ◽

Control Strategy ◽

Wheeled Mobile Robot ◽

Computational Time ◽

Planning Strategy ◽

Starting Point ◽

Planning Algorithm ◽

Path Efficiency ◽

Path Planning Algorithm

SummaryThis paper presents a vision-based path planning strategy that aims to reduce the computational time required by a robot to find a feasible path from a starting point to the goal point. The proposed algorithm presents a novel strategy that can be implemented on any well-known path planning algorithm such as A*, D* and probabilistic roadmap (PRM), to improve the swiftness of these algorithms. This path planning algorithm is suitable for real-time scenarios since it reduces the computational time compared to the basis and traditional algorithms. To test the proposed path planning strategy, a tracking control strategy is implemented on a mobile platform. This control strategy consists of three major stages. The first stage deals with gathering information about the surrounding environment using vision techniques. In the second stage, a free-obstacle path is generated using the proposed reduced scheme. In the final stage, a Lyapunov kinematic tracking controller and two Artificial Neural Network (ANN) based-controllers are implemented to track the proposed path by adjusting the rotational and linear velocity of the robot. The proposed path planning strategy is tested on a Pioneer P3-DX differential wheeled mobile robot and an Xtion PRO depth camera. Experimental results prove the efficiency of the proposed path planning scheme, which was able to reduce the computational time by a large percentage which reached up to 88% of the time needed by the basis and traditional scheme, without significant adverse effect on the workability of the basis algorithm. Moreover, the proposed path planning algorithm has improved the path efficiency, in terms of the path length and trackability, challenging the traditional trade-off between swiftness and path efficiency.

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