Operacionalización de las Variables

When the robot moves from its source position to the destination position, the considerations for the developed PSO based path planner are as follows:

Population generated within sensing range: 80 No. of runs performed: 20

Distance travelled by the robot when PSO is activated: 1.2 units Distance travelled when the robot is not sensed any obstacles: 2 units PSO parameters at 𝐶1 = 1 & 𝐶2 = 1.5 and 𝑟𝑎𝑛𝑑1 = 𝑟𝑎𝑛𝑑2 = 1.

Stop criteria: until Target reached

First fitness proportional parameters W1=0.5 & W2=800

Second fitness function proportional parameter K1 = 1.3.

Note: The path travelled by the robot is represented in various environments by considering abscissa as the X-axis in centimeters (cm) and ordinate as the Y-axis in centimeters.

93

6.5.1. Comparison with respect to 1st Fitness function

Das et. al. [158] have implemented a well-known heuristic A* algorithm for solving mobile robot navigation in static unknown environment. In their work, they considered the cost function as the time metric of distance travelled by the mobile robot. The aim of their work is to minimize the cost function by using A* algorithm. In other words the total distance travelled by the mobile robot from its initial position to destination should be minimum.

Fig.6.16 (a) Path obtained by Das et. al. [158] Fig.6.16 (b) Path obtained by present Motion planner

Fig.6.17 (a) Path obtained by Secchi et. al. [159] Fig. 6.17(b) Path obtained by present Motion planner

94

Secchi et. al. [159] have presented an effective control law for obstacle avoidance in unknown environments. The proposed control system concerns two loops namely, position control loop and impedance control loop. Impedance here referred as a function of the distance between the robot and the sensed obstacles.

Fig.6.18 (a) Robot Path by Zawawi et. al. [160] Fig.6.18 (b)Path obtained by present Motion planner

Zawawi et. al. [160] have described an efficient system architecture development for an autonomous mobile robot using visual simultaneous localisation & mapping, and particle swarm optimization. Their developed methodology is suitable for navigating a mobile robot in indoor environments.

Table 6.10 Path analysis results for Figs.6.16-6.18

Previous methodology Distance travelled(cm) % of path deviation Previous methodology Present methodology Heuristic A* algorithm [158] 470.8 455.2 3.3%

Position and impedance control

loops [159] 1103.6 1037.4 6%

Particle Swarm Optimization [160] 495.2 482.6 2.5%

From the Table 6.10, it is noticed that the current motion planner is giving better results as compared to results obtained by Das et. al. [158], Secchi et. al. [159] and Zawawi et. al. [160] by robot path deviation of 3.3%, 6% and 2.5% respectively.

6.5.2. Comparison with respect to 2nd Fitness function

To solve mobile robot navigation task, various approaches have been introduced in last few decades. Fuzzy Inference System (FIS) is one of the well-known approach have been used for

95

solving path planning problem of an autonomous mobile robot because of its capacity to handle uncertain and imprecise information obtained from sensors using linguistic rules.

Yong et. al. [161] have introduced a behaviour based architecture based on fuzzy logic for

solving mobile robot navigation problem in unknown environments. Fig. 6.19(a) shows the path generated by their algorithm for an autonomous mobile robot starting at the position S(3.5, 1) and its destination at T(4,9). For the same robotic environment, the path generated by current methodology is shown in Fig. 6.19(b).

Fig.6.19 (a) Path obtained by Yong et. al. [161] Fig.6.19 (b) Path obtained by Current methodology

Recently, Mester and Rodic [162] have explained a sensor based intelligent mobile robot navigation in unknown environments. They used Fuzzy Inference System for generating obstacle collision free trajectories within robotic work space. A simulation result of their approach is shown in Fig. 6.20(a) regarding the goal seeking and the obstacle avoidance mobile robot paths. For the same robotic environment, the path generated by current algorithm is shown in Fig. 6.20(b).

Fig. 6.20 (a) Path obtained by Mester and Rodic [162] Fig. 6.20 (b) Path obtained by Current methodology

96

Table 6.11 Path analysis results for Figs.6.19-6.20

Previous methodology Distance travelled(cm) % of path deviation Previous methodology Present methodology

Behavior based architecture

[161] 1153.8 1107.6 4%

Fuzzy Inference System [162] 594.2 549.6 7.5%

From the Table 6.11, it is noticed that the current motion planner is generating the shortest robot path within the robotic unknown environments as compared to results obtained by Yong et. al. [161] and Mester and Rodic [162] by robot path deviation of 4% and 7.5% respectively.

6.6. Summary

A new computational methodology has been proposed for solving path planning problem of an intelligent mobile platform, based on Particle Swarm Optimization. The developed algorithm is effective in avoiding obstacles and generating optimal paths within its unknown environments. The trajectories generated by robot are based on the selection of global best position in each iteration. Among the swarm, the particle which has the minimum fitness is considering as the global best position. There by, the robot moves towards the global best position and this process is continued for several iterations until the robot reaches its target position. A large number of experiments have been carried out for adjusting the controlling parameters of the modelled fitness functions. Simulation results showed the capability of a mobile robot, how effectively the robot is generating trajectories with the help of developed algorithm, by avoiding obstacles, escaping traps and reaches to its goal position within its unknown maze environments. Moreover, from the developed two fitness functions, type-1 fitness is giving efficient results as compared to type-2 fitness function (Table 6.9). Deviations are found to be within 5% during comparisons between simulation and corresponding experimental results. Although the proposed methodology solves the local minima problem up to certain level than the previous researchers as addressed in Chapter 2, it requires some reinforcement learning strategy to achieve better results.

Chapter 7