![]() The steering pivot points are joined by a rigid bar called the tie rod, which can also be part of the steering mechanism, in the form of a rack and pinion for instance. If the track rod is placed ahead of the axle, it should instead be longer in comparison, thus preserving this same "toe out".Ī simple approximation to perfect Ackermann steering geometry may be generated by moving the steering pivot points inward so as to lie on a line drawn between the steering kingpins and the centre of the rear axle. As the steering moved, the wheels turned according to Ackermann, with the inner wheel turning further. This was achieved by making the linkage not a simple parallelogram, but by making the length of the track rod (the moving link between the hubs) shorter than that of the axle, so that the steering arms of the hubs appeared to " toe out". A linkage between these hubs pivots the two wheels together, and by careful arrangement of the linkage dimensions the Ackermann geometry could be approximated. While more complex, this arrangement enhances controllability by avoiding large inputs from road surface variations being applied to the end of a long lever arm, as well as greatly reducing the fore-and-aft travel of the steered wheels. Rather than the preceding "turntable" steering, where both front wheels turned around a common pivot, each wheel gained its own pivot, close to its own hub. Intersecting the axes of the front wheels on this line as well requires that the inside front wheel be turned, when steering, through a greater angle than the outside wheel. As the rear wheels are fixed, this centre point must be on a line extended from the rear axle. The geometrical solution to this is for all wheels to have their axles arranged as radii of circles with a common centre point. The proposed system can be used in the robotic transport systems.The intention of Ackermann geometry is to avoid the need for tires to slip sideways when following the path around a curve. Based on the specified formulas, a stable system for traffic control of pilotless transport electric vehicles on a specified route was developed. Formulas were developed that make it possible, using the Doppler frequencies of sensors, to determine the current velocity and heading angle of electric vehicle control with the use of the Ackermann condition. In order to solve this problem, it is proposed to use microwave Doppler sensors of the linear velocity of the wheels, observations of which do not depend on sliding, the mass of the electric vehicle, the voltage in the buses, and other parameters. During the course of analyzing the dynamic model of the electric vehicle, it was discovered that the primary reasons for the occurrence of errors consists in the indirect nature of measuring the velocity of this vehicle by means of odometers. The issue of ensuring precise correspondence of control signals to the actual motion of transport vehicles in a room was examined. Simulated results indicate that BBNA generates an optimal path by perfectly switching between ‘Go to Goal’, ‘Obstacle Avoidance’, and ‘Follow Wall’ modes, which keeps the FWS mobile robot arriving at the goal position.Ĭontrol of pilotless electric vehicles indoors was studied. Finally, the implementation of the BBNA for the FWS mobile robot is simulated using MATLAB software. Since these equations are the same as those for the unicycle mobile robot, the FWS mobile robot can be controlled by the BBNA. The present study determines the dynamic equations of the FWS mobile robot by using the Ackermann- Jeantnat model of steering. In order to apply the BBNA to an FWS mobile robot, its dynamic equations must be converted to those of a unicycle mobile robot. Thus, the BBNA can navigate the unicycle mobile robot successfully to the goal position. One of the significant characteristics of BBNA is that its control commands can be used to calculate the linear and angular velocity of a unicycle mobile robot. At first, the BBNA was designed to control the navigation of a point mass robot. Due to overcoming this phenomenon, an additional mode is considered between the ‘Go-to-Goal’ and ‘Obstacle Avoidance’ modes that is called ‘Follow-Wall’ behavior. With this algorithm, many switching between modes occurs over a short amount of time, which increases the risk of creating the chattering phenomenon. The BBNA combines the ‘Goal-to-Goal’ and ‘Obstacle Avoidance’ behaviors into one comprehensive navigation strategy. In this study, an effective behavior-based navigation algorithm (BBNA) is applied to control the trajectory of the four-wheel steering (FWS) mobile robot. Therefore, accurate and efficient controllers are required in order to assure safe and accurate navigation of these vehicles. In recent years, wheeled autonomous mobile robots have become widely used in a number of industrial applications. ![]()
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