Figure 2
, shows the x position of the robot in time. Figure 3, shows the y position of the robot in time. Figure 4, shows the angle orientation of the robot in time. Figure 5, shows the x-y position of the robot. Figure 6, shows the in-zoom of part of the x-y position. Figure 7, shows the value of the matrix covariance P in time. Figure 8, shows the estimation errors. Figure 9, shows the measurement errors. In figure 2 to 6, red stars represent the true position of the robot, which we simulated. In real case, we won't be able to access these data. Blue line indicates the measurements results that we have from the sensors, and the cyan (light blue) represents the estimation position of the robot by using the Kalman filter.
Source publication
the objective of the presented work is to implement the Kalman Filter in an application in an environment for the position in a mobile robot's movement. The estimated position of a robot was determined, applying the Kalman Extended Filter, using the data of the sensors by means of a system of global positioning (GPS), using a simulation in Matlab a...
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Citations
... The value of P should be maintained as low as possible towards 0. Lower the P value towards 0, more accurate the estimated state of the system is. Thus a comparison is made among the P values of the Oscar's Method [30] and the proposed model and the result is shown in Table 2. When compared to the method of Julio E. Normey-Rico et. ...
Advanced Vehicle Control System (AVCS) includes both the lateral and longitudinal control of the vehicle. Tracking of lane using the physical model of the vehicle and suitable control system for the vehicle is proposed in this work. 1-D line scanning camera is used as vision system to track the black line from the white background. Lane detection algorithm uses 1-D line scanning camera values to precisely identify the position of the vehicle in the track independent of light intensity variations. The vehicle takes advantage of the adaptive nature of the Kalman filter for line tracking and effective control of the PID control algorithm for precise control over the lateral steer. Active speed control algorithm makes the vehicle to track the path smoothly with optimum speed. The control algorithms are tested in two stages. First, the vehicle is modelled to check the controller's feasibility. Second, the controllers are implemented in the prototype vehicle and their performance is analysed. The visual navigation and control system allow the vehicle to navigate and track through the lane to accomplish autonomous locomotion. Proposed algorithm reduces tracking error and minimizes the computational cost and the control action is soft and smooth.
... The value of P should be maintained as low as possible towards 0. Lower the P value towards 0, more accurate the estimated state of the system is. Thus a comparison is made among the P values of the Oscar's Method [23] and the proposed model and the result is shown in Table 6. Thus by the comparisons made between the proposed model and various other existing methods, the proposed method proves to be better when compared in terms of reliability, accuracy and in speed. ...
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experimental results show that the combination of Kalman filter with PID for lateral control reduces trajectory error to a minimum level and adaptive speed control algorithm for longitudinal control provides
smooth speed over the entire track.
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