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... o virtual target switch applied in this method, robot is able to quit limit cycles and find a way out of the dead end. Even in loop shapes like in Fig. 7. a, still robot can find the opening at point C due to the fuzzy logic target seeking and obstacle avoidance behaviors. The algorithm shows its major weakness when it fails to reach the goal in such a concave and recursive U-shape environment shown in Fig. 7.
b. This is because the robot encounters another local minimum at the location B and C when it is working under the influence of previous virtual sub goal [ 9 ]. Therefore this method is not applicable in multiple minimum situations. Fig. 7. (a) The common target switch algorithm is applicable in simple minimum situation, (b) inability of the algorithm in multiple minimum situations. 2. 5.
Conclusion From all the methods discussed here the virtual target technique has better properties in that unlike the landmark learning approach, it doesnt require bulky process of memory integration. Also there is no need to change the basic fuzzy logic rule bases in order to switch from actual to virtual target and vice versus. Using this technique a reactive robot makes more logical trajectories compared to the methods like the minimum risk that take trial and return approach. Therefore the idea of actual-virtual target switch will be the main concept in development of a new approach to the minimum problem while a typical fuzzy logic controller will carry out the basic tasks of target seeking and obstacle avoidance. In this project, the main contribution of the work is in solving the problem of multiple minimum traps in local navigation whereas multiple minimum traps are considered as different possible arrangements of bars, barriers or walls forming any dead end shape from U-shape dead ends to loops, maze, snail shapes and even more complicated shapes. Therefore the objective of this project will be to develop a new fuzzy logic based algorithm by using a virtual target technique for robot local navigation in multiple minimum situations. 3.
The proposed target switch approach In the new approach the critical point to switch from real target to virtual target is the point where the robot detects the obstacle for the first time. Also there is a difference in the actual to virtual target switch process and vice versus. 4. Experimental results A pioneer 3 TM mobile robot has been used to show the performance of the proposed algorithm. In Simulation investigations, the starting location of the robot and the location of the target are given for each navigation task.
The normal speed of the robot is 5 cm / s , the upper bound of the robot speed is 10 cm / s . The pioneer 3 TM is equipped with an array of 8 sonar range finders (2 at sides and 6 at front with 20 degree interval) to perceive its environment. The Simulation and programming soft ware for Active media robotics graphic Simulator is programmable by using Colbert language which is a limited form of C++ added with Robot self localization functions. Fig. 13. The robot behavior in compression with other approaches in the same dead end trap as was shown in figures; 2, 3. a, 5, and 7.
b. Fig. 13 depicts the expected results as previously discussed in part 3. In this method unlike the memory state method, [ 8 ], the robot doesnt have extra wall following and goes directly toward the target as soon as it is out of the dead end trap. And because the motions is not a trail and return type the robot makes more logical trajectories in compression with the minimum risk approach. Fig. 14. Snail shape dead end A more complicated trap of this type is the snail shape shown in figure. 14.
Here after straight motion from A to B, the robot encounters the first wall and therefore follows the walls from B to C in hope of finding an opening. As soon as it detects the opening at point C most logical decision is to follow the walls in the opposite direction from C to D where the robot switches to actual target seeking behavior (case 1). The motion doesnt have to be in one direction until opening appears and then in the opposite direction. In the example shown in Fig. 15, the robot motion is primarily towards the target until the wall is detected and then clockwise&# 61664; counter clockwise&# 61664; clockwise&# 61664; counter clockwise, until it is out of the trap and therefore can reach the real target. More example of this type is shown in fig. 16, to prove the robustness of the algorithm in different multiple minimum situations. Fig. 15 It was found in experimental results that the robot makes undesirable extra turns if the condition for turning is just based on the sum of the turned angles.
In the example of opening snail shape shown in Fig. 17, until point A the robot has a sum of turning angles equal to 360 degree anti-clockwise. Therefore for algorithm without considering the case 4, the robot would continue to wall following until point B where there is no obstacle around and the sum of turned angles is compensated clockwise to zero (Fig. 17. a). In order to solve the problem of extra turns a distance based condition is added to the algorithm (case 4). In this case the decision to switch back to the actual target is taken if only there is no obstacle around and the robot distance to the actual target has the minimum amount compared to the distance between the robot and the target at any time during the robot motion. Fig. 17.
b shows how the robot motion is modified at point A to avoid making extra turns. Fig. 16. Examples of the robot motion 5. Conclusion A new fuzzy logic control system is developed for reactive navigation of a behavior-based mobile robot. The inputs to the fuzzy controller are; the obstacle position detected relative to the robot heading and the target orientation which is defined as the angle between the robot heading direction and the robot-to-target direction. While the fuzzy logic body of the algorithm performs the main tasks of obstacle avoidance, target seeking and speed control, an actual-virtual target switch strategy enables the robot to wall following behavior when needed.
This significantly results in resolving the problem of multiple dead ends in local navigation which is an advantage beyond pure fuzzy logic approach and common virtual target techniques. The work is in progress for the situations where the environment is dynamic and the target shift needs to be supervised by some kind of landmark learning. But still this kind of smooth target shifting seems more interesting because of its similarity with human fuzzy behavior in gradually shifting their direction to left or right in order to turn around an obstacle, and then compensate their turns in opposite direction until finding the original direction to the target. Fig. 17. distance based decision making. (a) Without case condition (case 4); undesired urns from A to B, (b) case condition (case 4); robot-target distance has its minimum amount at point A; straight motion to the actual target. 6.
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Research essay sample on A New Mobile Robot Navigation Algorithm
