Abstract
Application of Path Planning and Image Processing for Rescue Robots
Highlights
In recent years, researchers have paid increasing attention to improving robotic systems, and many related applications and technologies have been widely developed
Zhao and BeMent addressed six common kinds of three-wheeled robot models and their ability of control.[1]. Leow et al analyzed the kinetic model of an alldirection wheeled robot.[2]. Chung et al utilized two wheels with different speeds for position control.[3]. Wang and Juang utilized a localization system for the path planning and parking control of a wheeled mobile robot (WMR).[4]. Lin applied an omnidirectional camera and laser range finder to extract point features and line features as landmarks.[5]. Wang used a low-end camera for simultaneous localization and mapping (SLAM), which had the advantages of standardized hardware and software interfaces with a low price, as well as the potential for wide application in robotic
A laser sensor was utilized to detect the locations of obstacles and provide their edge values
Summary
Researchers have paid increasing attention to improving robotic systems, and many related applications and technologies have been widely developed. Zhao and BeMent addressed six common kinds of three-wheeled robot models and their ability of control.[1] Leow et al analyzed the kinetic model of an alldirection wheeled robot.[2] Chung et al utilized two wheels with different speeds for position control.[3] Wang and Juang utilized a localization system for the path planning and parking control of a WMR.[4] Lin applied an omnidirectional camera and laser range finder to extract point features and line features as landmarks.[5] Wang used a low-end camera for simultaneous localization and mapping (SLAM), which had the advantages of standardized hardware and software interfaces with a low price, as well as the potential for wide application in robotic. Martinez et al used a laser scanner for following mobile objects and avoiding obstacles.[7] Yamakawa et al proposed two fixed-angle laser scanners that could reconstruct the 3D shape of an obstacle, and the reconstructed shapes were used to produce a potential field map for mobile robot path planning.[8] Chen used the data of a laser range finder to develop a feature map. Zhong et al presented a new methodology based on a neural network for robot path planning, in which an improved Hopfieldtype neural network model was established for propagating the target activity among neurons in the manner of physical heat conduction, which guaranteed that the target and obstacles remained at the peak and the bottom of the activity landscape of the neural network, respectively.[13]
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