Localization of Leader-Follower Robot Using Extended Kalman Filter
| Autor: | Sahat Pangidoan, Siti Nurmaini |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Traverse
the fact that mobile robots have some unbounded growth of time integration errors along their duties to traverse their environment is undeniable [8][9]. The wheels attached to mobile robot are susceptible to slippage. It is known that slippage can disturb the sensor reading. Mathematical modeling Computer science a robot has access to two kinds of information but the robot itself does a direct measurement to supply information [3][7]. However Odometer Estimator lcsh:QA75.5-76.95 Extended Kalman filter Odometry Computer vision Leader-Follower robot1.INTRODUCTIONIn order to navigate safely and reliably because the robot does not need to derive some integrated sequence of measurement to gain information relative or absolute [5][6]. By relative Orientation (computer vision) business.industry Extended Kalman Filter θ). The goal of the localization is to keep track of the position while the robot is navigating through the environment [3][4]. According to the methods in determining the location Noise Localization Robot Leader-Follower robot Artificial intelligence the robot collect and integrating its information from different sensors where the integration is started from the initial position and continuously updated through times. Absolute method is different from the other one lcsh:Electronic computers. Computer science Geriatrics and Gerontology an autonomous mobile robot must be capable of finding out its location relative to the environment independently [1][2][3]. Localization is one important component in robot navigation system in terms of position and orientation (x business also known as dead-reckoning |
| Zdroj: | Computer Engineering and Applications Journal, Vol 7, Iss 2, Pp 95-108 (2018) Computer Engineering and Applications Journal, Vol 7, Iss 2 (2018) |
| ISSN: | 2252-5459 2252-4274 |
| Popis: | Estimator, Extended Kalman Filter, Localization, Odometry, Leader-Follower robot1.INTRODUCTIONIn order to navigate safely and reliably, an autonomous mobile robot must be capable of finding out its location relative to the environment independently [1][2][3]. Localization is one important component in robot navigation system in terms of position and orientation (x, y, θ). The goal of the localization is to keep track of the position while the robot is navigating through the environment [3][4]. According to the methods in determining the location, a robot has access to two kinds of information, relative or absolute [5][6]. By relative, also known as dead-reckoning, the robot collect and integrating its information from different sensors where the integration is started from the initial position and continuously updated through times. Absolute method is different from the other one, because the robot does not need to derive some integrated sequence of measurement to gain information, but the robot itself does a direct measurement to supply information [3][7]. However, the fact that mobile robots have some unbounded growth of time integration errors along their duties to traverse their environment is undeniable [8][9]. The wheels attached to mobile robot are susceptible to slippage. It is known that slippage can disturb the sensor reading. Mathematical modeling, including |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|