NDLI: Adaptive controller based PSO with virtual sensor for obstacle avoidance in dynamic environments

Content Provider	IEEE Xplore Digital Library
Author	Yousef, M.T. Ali, H.E.I. Habashy, S.M. Saad, E.M.
Copyright Year	2014
Description	Author affiliation: Fac. of Eng., Helwan Univ., Cairo, Egypt (Ali, H.E.I.; Habashy, S.M.; Saad, E.M.) \|\| Tibben Inst. for Metall. Studies, Cairo, Egypt (Yousef, M.T.)
Abstract	In this paper, an Adaptation of Advanced Artificial Potential Field (AAPF) controller based on Particle Swarm Optimization (PSO) algorithm is proposed. It plans the robot's motion in cluttered and dynamic environments to make the robot reaches to its goal. The PSO is used to optimize the factors of the forces applied on the robot to guide the robot towards to the right path. The optimization process is done by selecting the optimum values of these factors. A measure of smoothness is used to guide the PSO algorithm during the optimization process. The PSO is reused once a change in the environment is occurred. This scheme makes the robot able to reach to its target with shortest path and avoidance of the obstacles whatever changed environment. Shortest path means more smoothness and minimum time. The proposed adaptive AAPF controller uses the concept of virtual sensor. The virtual sensor's calculations are modified in this paper. The proposed system is simulated on Windows Vista using MATLAB Software at different workspaces, and compared with another not adaptive system.
Sponsorship	IEEE Egypt Sect.
Starting Page	228
Ending Page	235
File Size	483073
Page Count	8
File Format	PDF
ISBN	9781479938209
e-ISBN	9781479938216
DOI	10.1109/NRSC.2014.6835080
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-04-28
Publisher Place	Egypt
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robot sensing systems Force Robot kinematics Educational institutions Collision avoidance Dynamics odometry particle swarm optimization robot motion artificial potential field controller obstacle avoidance virtual sensor
Content Type	Text
Resource Type	Article

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