Fuzzy Logic Behaviour for Control Technique 51 

The first and most common application of fuzzy logic techniques in the domain of an autonomous mobile robot is the use of fuzzy control to implement individual behaviour units. Fuzzy logic controllers incorporate heuristic control knowledge in the form of if-then rules and are a convenient choice when a precise linear model of the system to be controlled cannot be easily obtained. Fuzzy logic has features that are particularly attractive in light of the problems posed by autonomous robot navigation. Fuzzy logic allows the modeling of different types of uncertainty and imprecision, building robust controllers starting from heuristic and qualitative models, and integrating symbolic reasoning and numeric computation in a natural framework [2]. Fuzzy controller helps autonomous mobile robot in navigating to a desired location.

Fuzzy logic behaviour control architecture is implemented using fuzzy rule-base and inference engine. Depending on the type of action, from a set of inputs different fuzzy rule bases are activated and hence the corresponding outputs. The output parameters from the fuzzy rule-bases for all actions are same i.e. the velocities of the left and right wheels of the robot, which drive the robot to a desired posture. The fuzzy rules control the steering of the robot according to whether there are obstacles or targets around it and how far they are from it. Because this information is usually not known precisely, fuzzy logic is an appropriate technique for handling it [6]. An Intelligent Fuzzy controller for mobile robot enables the robot to avoid the obstacle and improve target seeking ability. The inputs to the proposed fuzzy control scheme consist of a target angle between a robot and a specified target and the distances between the robot and the obstacles to the left, front, and right locations, acquired by an array of sensors. The outputs from the control scheme are commands for the speed control unit of two side wheels of the mobile robot. The input signals of fuzzy controller are the distances between the robot and obstacles to the left, front, and right locations as well as the target angle between the robot and a specified target, as shown in Fig. 4.1(a) and Fig. 4.1(b). As the robot perceives the target from the image sensor, it computes the difference in angle with respect to global coordinate system between its current position and the target. And get the angle between the robot current moving direction and the target [212]. When the target is located at the left sides of the mobile robot, target angles (tar-ang) are negative and if the target is located to the right side of the mobile robot, the target angles (tar-ang) is defined as positive.

Right-v Left-obs Right-obs Front-obs Tar-ang Left-v

INPUTS _CONTROLLER FUZZY OUTPUT

Target Mobile robot Front-obs Tar-ang Left-obs Right-obs Path

Fuzzy Sets & Fuzzy rule

(a) (b)

Figure 4.1. Simulation resulting paths of mobile robot.

According to acquired range information by sensors, reactive behaviours are weighted by the fuzzy logic algorithm to control the velocities of the two driving wheels of robot. The basic configuration of a fuzzy system consists of four principal elements: fuzzifier, fuzzy rule base, fuzzy inference engine, and defuzzifier. The fuzzifier is a mapping from the observed crisp input space to the fuzzy sets defined in, the fuzzy set defined is characterized by a membership function and is labeled by the linguistic variables near, medium and far and these are chosen to fuzzify left obstacle distance (left-obs), right obstacle distance (right-obs) and front obstacle distance (front-obs). The linguistic variables positive (P), zero (Z) and negative (N) are used to fuzzify tar-ang and the linguistic variables slow, med. (medium) and fast (Table 4.1). These are used to fuzzify the velocities of the left wheel (left-v) and right wheel (right-v), respectively [86].

Table 4.1. Parameter for variables Left obstacle distance(left-obs)

Right obstacle distance(right-obs) Front obstacle distance(front-obs)

Near(meter) Medium(meter) Far (Meter) 0.0 to 0.6 0.3 to 0.9 0.6 to 1.2

Target angle (tar-ang) Negative Zero Positive

-600 to 00 -300 to + 300 00 to 600 Left wheel velocity(Left-v)

Right wheel velocity(Right-v)

Slow (m/s) Medium (m/s) Fast (m/s) 0 to 2 1 to 3 2 to 4

The fuzzy rule base is a set of linguistic rules in the form of “if a set of conditions are satisfied, then a set of consequences are inferred.” For four inputs two outputs fuzzy system, the general fuzzy rule base may consist of the following.

If “matching degree of is and matching degree of is and matching degree of is and matching degree of is ” Then “matching degree of is

and matching degree of is ”.

The matching degree of final output is computed by the following formula.

, , , (4.1)

Where, i = (1, 2, 3,……n), , , , are the sensor inputs of left, right, front obstacle distance and target angle respectively, , , are the matching degree of corresponding sensor inputs, and , are the inferred inputs matching degree of corresponding left and right wheel velocity.

When the matching degree is one the inferred conclusion is identical to the rule’s consequence and if it is zero no conclusions can be inferred from the rule.

Finally the output firing area of the left wheel velocity and right wheel velocity value can be computed by following formula.

, , , (4.2)

The final output (crisp value) of the fuzzy logic controller of left wheel velocity and right wheel velocity can be calculated by

, ∑ _∑ (4.3)

Where, _, is the firing area of left and right wheel velocity for ith rule, is the centroid distance of the area, n is the total number of parameter, and _{, ,} Velocity of left and right wheel respectively.

In order to reach a specified target in a complex environment, the mobile robot at least needs the following reactive behaviours: 1. Obstacle avoidance, 2. Wall following and 3.Target steer. In this case, a set of fuzzy logic rules is used to describe the reactive behaviours mentioned above. Now, the last part of fuzzy rules from the rule base is to explain, in principle, how these reactive behaviours are realized.