NDLI: Dynamic path planning algorithm in mobile robot navigation

Content Provider	IEEE Xplore Digital Library
Author	Soh Chin Yun Parasuraman, S. Ganapathy, V.
Copyright Year	2011
Description	Author affiliation: Department of Electrical Engineering, University of Malaya, Malaysia (Ganapathy, V.) \|\| School of Engineering, Monash University, Sunway Campus, Malaysia (Soh Chin Yun; Parasuraman, S.)
Abstract	Mobile Robot Navigation is an advanced technique where static, dynamic, known and unknown environment is involved. In this research, Genetic Algorithm (GA) is used to assist mobile robot to move, identify the obstacles in the environment, learn the environment and reach the desired goal in an unknown and unrecognized environment. This study is focused on exploring the algorithm that avoids acute obstacles in the environment. In the event of mobile robot encountering any dynamic obstacles when travelling from the starting position to the desired goal according to the optimum collision free path determined by the controller, the controller is capable of re-planning the new optimum collision free path. MATLAB simulation is developed to verify and validate the algorithm before they are real time implemented on Team AmigoBotℒ robot. The results obtained from both simulation and actual application confirmed the flexibility and robustness of the controllers designed in path planning.
Starting Page	364
Ending Page	369
File Size	360612
Page Count	6
File Format	PDF
ISBN	9781457714184
e-ISBN	9781457714177
DOI	10.1109/ISIEA.2011.6108732
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-25
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Real time systems Navigation Heuristic algorithms Robot kinematics Team AmigoBot™ robot and MATLAB Genetic Algorithm (GA) Mobile robots Genetic Controller Genetic Algorithm (GA) based Dynamic Path Planning Algorithm (DPPA) Genetic algorithms
Content Type	Text
Resource Type	Article

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