Control of a Mobile Robot in Localization Uncertainty via visual trajectory Case Study Analysis
Medicine and so forth
There are two main approaches or techniques in the landmark use for the topological navigation in the related literature. One approach tends to use the landmark environmental regions that could be recognized later, even though they aren’t a single object. For example; based on the visual templates, the spatial navigation system is presented in the study presented by (Balkenius, 1998). One could create the template by choosing the number of high contrast features in the image, and after it start storing them together in accordance to their relative spatial locations in the image. The vision based system for topological navigation in the open environment was proposed and developed by (Franz, 1998). Such vision based system tend to represent the chosen locations by local 360 degree surrounding scenes views. The second approach uses the environmental objects as landmarks with the algorithm of perception particularly designed for each object. A series of perceptual and motor behavior are described by (Beccari, Caselli, & Zanichelli, 1998) which are significantly used for the indoor mobile robots navigation; the corridors, doors and walls are used as a landmark.
Discussion
For unmanned ground vehicle (UGV), the path planning is quite effective for number of applications which includes mapping, reconnaissance as well as monitoring. In addition to this, the optimal planning of path tend to require robust and accurate UGV localization. For this reason, a new solution of the unmanned ground vehicle localization utilizes the smooth variable structure filter (SVSF). The simultaneous localization and mapping algorithm is one of the effective mechanisms that helps unmanned ground vehicle in localizing itself as well as developing a map where the global positioning system (GPS) are inaccessible (Demim Fethi, 2018 ). With regard to this, the UGV-SLAM solution has shown the important promise for the remote exploration (Golombek, 1997), (Thrun, 2003).
The SLAM framework was introduced as a technique which could be used in formation from onboard robot sensors with core consideration of providing the feature relative localization which tend to provide an apriori environment map using the extended (Moutarlier, 1989). Such approach is focused on extended EKF technique to estimate the robot position and to develop or construct the map. The work on the implementation of the EKF-SLAM algorithm could be found in number of different environment which includes underwater submarine, indoor, outdoor as well as aerial space. Additionally, the unscented Kalmar filter is the nonlinear Kalmar filter version that employs the specific Gaussian random variable representation in math processing error dimensions with the use of the labeled sigma points. In contradiction to the number of benefits of the unscented Kalmar filter, the noise is recognized as the nonlinear mode to account for the non-Gaussian noise. Similar to EKF, the unscented Kalmar filter does not recover from the poor landmarks.
Additionally, the Fast-SLAM is another estimation algorithm which uses the Rao-Blackwellized Particle Filter for estimating the map and pose of the robot. Each particle demonstrates the possible position of the robot. The uncertainty in relation to the position knowledge is represented by distribution of these particles in the space. The Fast-SLAM implementation could be successfully solved with hundreds and thousands of the landmarks and in comparison to the EKF-SLAM algorithm which have a tendency to use little amount of landmarks (Montemerlo, 2003). In terms of continuity, the SLAM algorithm enhances the path the On the basis of the optimal control theory by providing the more comfortable and smoother trajectory tracking..............................
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