scholarly journals Correction: Xu, S.S.-D.; Huang, H.-C.; Chiu, T.-C.; Lin, S.-K. Biologically-Inspired Learning and Adaptation of Self-Evolving Control for Networked Mobile Robots. Applied Sciences 2019, 9, 1034

2019 ◽  
Vol 9 (11) ◽  
pp. 2215
Author(s):  
Sendren Xu ◽  
Hsu-Chih Huang ◽  
Tai-Chun Chiu ◽  
Shao-Kang Lin
Keyword(s):  
Applied Sciences ◽  
Mobile Robots ◽  
Research Paper ◽  
Biologically Inspired ◽  
Learning And Adaptation

After publication of the research paper [...]

scholarly journals Biologically-Inspired Learning and Adaptation of Self-Evolving Control for Networked Mobile Robots

2019 ◽  
Vol 9 (5) ◽  
pp. 1034 ◽  
Author(s):  
Sendren Sheng-Dong Xu ◽  
Hsu-Chih Huang ◽  
Tai-Chun Chiu ◽  
Shao-Kang Lin
Keyword(s):  
Mobile Robots ◽  
Formation Control ◽  
Kinematic Model ◽  
Control Law ◽  
Biologically Inspired ◽  
Modified Genetic Algorithm ◽  
Robot Systems ◽  
Learning And Adaptation ◽  
Self Learning ◽  
Adaptation Method

This paper presents a biologically-inspired learning and adaptation method for self-evolving control of networked mobile robots. A Kalman filter (KF) algorithm is employed to develop a self-learning RBFNN (Radial Basis Function Neural Network), called the KF-RBFNN. The structure of the KF-RBFNN is optimally initialized by means of a modified genetic algorithm (GA) in which a Lévy flight strategy is applied. By using the derived mathematical kinematic model of the mobile robots, the proposed GA-KF-RBFNN is utilized to design a self-evolving motion control law. The control parameters of the mobile robots are self-learned and adapted via the proposed GA-KF-RBFNN. This approach is extended to address the formation control problem of networked mobile robots by using a broadcast leader-follower control strategy. The proposed pragmatic approach circumvents the communication delay problem found in traditional networked mobile robot systems where consensus graph theory and directed topology are applied. The simulation results and numerical analysis are provided to demonstrate the merits and effectiveness of the developed GA-KF-RBFNN to achieve self-evolving formation control of networked mobile robots.

Swarm Intelligence in Production Management and Engineering

2011 ◽  
pp. 345-365 ◽  
Author(s):  
Swagatam Das ◽  
Amit Konar
Keyword(s):  
Motion Planning ◽  
Mobile Robots ◽  
Swarm Intelligence ◽  
Vehicle Routing ◽  
Computer Simulations ◽  
Real World ◽  
Production Management ◽  
Industrial Applications ◽  
Future Research ◽  
Biologically Inspired

This chapter explores the scope of biologically inspired swarm intelligence (SI) into production management with special emphasis in two specific problems of vehicle routing and motion planning of mobile robots. Computer simulations undertaken for this study have also been included to demonstrate the elegance in the application of the proposed theory in the said real-world problems. Possible directions of future research and industrial applications have also been appended at the end of the chapter.

A biologically inspired method for vision-based docking of wheeled mobile robots

2007 ◽  
Vol 55 (10) ◽  
pp. 769-784 ◽  
Author(s):  
Emily M.P. Low ◽  
Ian R. Manchester ◽  
Andrey V. Savkin
Keyword(s):  
Mobile Robots ◽  
Wheeled Mobile Robots ◽  
Biologically Inspired
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