Proposed Vehicle Guidance Principle and Process

The scene in each kind of indoor environment is different and usually complicated, and furniture and decorations within rooms also have difference appearances. Therefore, it is hard to differentiate between obstacles and non-obstacles in guiding the vehicle to correct and safe paths. Hence, a fuzzy-control guidance technique is proposed in this study which guides the vehicle in a natural manner. In every navigation cycle, a suitable steering angle can be obtained by this fuzzy-control

Figure 2.3 Illustration of the proposed learning process Manual learning

Start learning

Data collection

User-driving parameters Odometer data

Path map creation

Saving data for

retrieval in navigation End learning

guidance technique and the goal of obstacle avoidance can be achieved in the meanwhile. In other words, the proposed fuzzy-control guidance technique can nominally reduce the complexity of scenes in indoor environments for choosing a suitable path to navigate.

Furthermore, a vehicle navigation process with two navigation modes for different purposes is proposed. One is the single path mode for navigating along a learned path with only a starting point and a destination. In the single path mode, the learned data are retrieved from a saved file first, and all the nodes within this navigation path are set in accordance with the nodes in the learned graph exactly.

Then the navigation trip can be accomplished by traversing each node in the learned graph orderly. For this reason, two navigation strategies are proposed for the vehicle to navigate in two types of sections during the navigation process. The first strategy is used along each straight-line section between two nodes connected with a line approximately. And the second strategy is used for turning sections where the vehicle navigates around a corner.

The other mode is the area mode for navigating among multiple nodes collected from several paths. In the area mode, a desired navigation path may not be learned before. First, the learned data are retrieved from a saved file, too. After that, the desired navigation path is determined by a given starting point and a given destination which are learned in the learning process. The nodes within this navigation path are found dynamically using Dijkstra shortest path searching algorithm. After the desired navigation path is determined, a similar navigation strategy is adopted for traversing along the nodes along the navigation path. An illustration of the vehicle navigation process is shown in Figure 2.4.

Figure 2.4 An illustration of vehicle navigation process Start Navigation

Single path mode Area mode

Learned data Retrieval

Learned data Retrieval

Dynamic path planning

Vehicle guidance

End navigation

Chapter 3

Proposed Fuzzy Guidance

Techniques for Indoor Navigation

3.1 Introduction

In order to guide a small vehicle to navigate in indoor environments with complicated scenes, two fuzzy-control guidance techniques are proposed in this study for safe indoor navigation. Since the principle of fuzzy control is intuitive and experiential, we utilize fuzzy-guidance techniques for vehicle navigation. The essence of this approach is to keep equilibrium between navigation accuracy and fuzzy control.

Generally speaking, fuzzy theory can be implemented in a wide variety of fields because of its multidisciplinary nature. A system based on a fuzzy will react to triggered events autonomously in an intuitive way. Due to this characteristic of fuzzy theory, many researchers use fuzzy theory in robot-control applications. A review of the fuzzy-control concept will be described in Section 3.2. And the reason of using fuzzy-control techniques for guidance of small vehicles in this study will be described in Section 3.3.

Besides, some parameters for the proposed fuzzy model must be acquired through some image processing works. Since appearances of different kinds of indoor environments can be classified roughly into two types, namely, room and corridor, two processes of parameter acquirement for the two types of indoor environments are

proposed and will be described in Section 3.4. After necessary parameters are acquired, desired fuzzy outputs as meaningful commands for vehicle control can be obtained by a computation process based on the proposed fuzzy model. Similarly to the need of two parameter acquirement processes, two different fuzzy models are designed for vehicle guidance in the two types of indoor environments. The details of the fuzzy models will be described in Section 3.5.

3.2 Review on Fuzzy Control Concepts

The proposed fuzzy control system is derived from fuzzy inference techniques using fuzzy if-then rules and fuzzy reasoning. According to C.T. Sun [20], the basic structure of a fuzzy inference system consists of three conceptual components: a rule base, which contains the detailed fuzzy rules; a database, which defines the membership functions used in the fuzzy rules; and a reasoning mechanism, which performs the fuzzy reasoning process using the rules and certain given facts to obtain a reasonable output.

In a fuzzy inference system, when fuzzy or crisp inputs are taken into the system, the outputs are fuzzy sets in most cases. But fuzzy-set outputs are not suitable for use in our vehicle control system. Therefore, a method of defuzzification is needed to extract a crisp value which best represents a fuzzy set. Besides, in the case with crisp inputs and outputs, the fuzzy inference system just implements a nonlinear mapping from its input space to its output space. And this mapping is accomplished by a number of fuzzy if-then rules, each of which describes the local behavior of the

mapping. The proposed fuzzy control techniques belong to this case.

The procedure of a fuzzy inference system with a crisp output is shown in Figure 3.1. The first part is an input process which takes some fuzzy or crisp values as inputs into this system. And the second part is a fuzzy reasoning process which processes the inputs with fuzzy rules to yield fuzzy-set outputs. The third part is a defuzzification process which transforms the output fuzzy sets into a single crisp value. The final crisp value will be used as the basis for generating commands to guide the vehicle in the proposed fuzzy guidance techniques.

X (crisp or fuzzy)

Figure 3.1 Illustration of a fuzzy inference system.

3.3 Reasons of Using Fuzzy Guidance Techniques

The reason for using fuzzy guidance techniques in this study is three-fold, as described in the following.

1. To reduce the complexity of guidance in indoor environments.

Since the scene structures of indoor environments are usually complicated, it is generally difficult to guide a vehicle to navigate in it. One way to solve this problem is to use fuzzy guidance techniques, which presumably are more proper for use to find vehicle navigation paths. That is, it expected that complicated scene structures will not influence vehicle guidance when fuzzy guidance techniques are employed.

2. To create obstacle avoidance capability for the vehicle.

When a vehicle is navigating in indoor environments using the proposed fuzzy guidance techniques, obstacles may appear in the navigation path. An example of images grabbed by the wireless camera in our vehicle system is shown in Figure 3.1.

Obstacles are regarded in this study as furniture in indoor environments or simply as part of environments because of the use of fuzzy guidance techniques. Therefore, our method for finding safe navigation paths is simple with no necessity to differentiate between objects in navigation paths and furniture or walls in environments. In the situation shown in Figure 3.1, the vehicle is steered to the left side in our experiment to avoid possible collision by considering the paper box as part of the environments instead of as an obstacle.

3. To create an adaptive capability for the proposed vehicle system to navigate in different indoor environments.

Some vehicle systems use visual environments features (e. g., baseline, corner, manual landmark, etc.) in the guidance process. Since these kinds of visual features can be found only in common indoor environments, these vehicle navigation systems are useless when navigating in certain environments with no such visual feature. This weakness will not be found in our navigation system using the proposed fuzzy guidance techniques because any kind of object will be regarded as part of the environment. Therefore, the proposed vehicle navigation system can be used in any kind of indoor environments without any restriction on the scene structures of the navigation environments.

3.4 Parameters for Proposed Fuzzy Guidance System

According to the review on fuzzy-control concepts in Section 3.2, in order to Figure 3.2 An image of an obstacle appearing in navigation path.

acquire a crisp output value, certain crisp values must be taken into consideration in the fuzzy guidance system first. And these crisp values are related to the designed fuzzy rules which are determined in the phase of fuzzy model development. In the proposed fuzzy guidance techniques, still images captured from the wireless camera will be processed to obtain appropriate features as crisp inputs for the fuzzy guidance system in every navigation cycle. And the proposed processes of parameter acquirement for use as inputs to the proposed fuzzy models will be detailed in the following.

Because two different fuzzy guidance techniques are designed in this study, two different processes, as mentioned previously, which acquire the demanded parameters for the two fuzzy guidance techniques, are proposed. They are described in Sections 3.4.1 and 3.4.2, respectively.

3.4.1 Features for Vehicle Guidance in Rooms

In every navigation cycle, an input image captured with the wireless camera is processed to obtain two kinds of features, namely, collision-free direction, and degrees of collisions of the left and the right route sides. The values of these features are taken as inputs into the proposed fuzzy guidance system, which yields an angle as the output for use to steer the vehicle in each navigation cycle. In the following, we describe how we extract the two kinds of features by image processing techniques in the captured images.

A. Computing collision-free direction

The feature of collision-free direction means the direction into which the vehicle may be driven with no collision with the objects along the path traversed by the

vehicle. Such a feature is computed in every navigation cycle in the following way.

First, we divide an input image into 4×4 blocks and classify each image block into two classes, namely, route area and non-route one. As a preliminary step to achieve this goal, we utilize an algorithm presented in Li and Tsai [6] to locate image blocks of possible route areas. The essence of the algorithm is to use two pixel features, namely, a pixel’s grayscale value and its Sobel edge value, to identify candidate route-area pixels. A pixel in the input image with its grayscale value close to a pre-learned grayscale value of the room ground and with its Sobel edge value smaller than a pre-selected threshold is classified as a candidate route-area pixel. An example of such image pixel classification results is shown in Figs. 3.3(a) and 3.3(b). Because of color and uniformity similarities, some wall or furniture regions may be misclassified as route areas, as can be seen in the example shown in Fig. 3.3(b).

Nevertheless, we propose an algorithm in this study to remove such erroneous areas in the following, which in addition computes the above-mentioned feature of collision-free direction from the remaining correct route areas in the input image.

Algorithm 1. Computation of route areas and collision-free direction.

Step 1. Perform region growing to find as the desired route area the largest bottom region in the candidate route-area pixels extracted from the input image using [6]. (The region growing result of the above example is shown in Fig.

3.3(c).)

Step 2. Put two parallel horizontal scanning lines in a fixed lower part of the route area. If any non-route area crosses either scanning line and cuts it into several line segments, pick out the longest segment and call it a non-obstacle segment; otherwise, select the original scanning line as the non-obstacle segment. (At the end of this step, two non-obstacle segments

will be obtained.)

Step 3. Find the middle points of the two non-obstacle segments, connect them to form a line segment, and call it route segment. (The found middle points for the above example are A and B as shown in Fig. 3.3(c).)

Step 4. Compute the middle point of the route segment, and call it route center.

(The result of this step for the last example is point C in Fig. 3.3(c).)

Step 5. Connect the route center to the middle point of the bottom line of the image to form a line segment, and call it guidance line (like the line labeled L in Fig.

3.3(d).)

Step 6. Find as the desired collision-free direction θg the angle of the guidance line with respect to the bottom line of the image.

In the above algorithm, no complicated 3D computer vision technique is used in computing the collision-free direction, as contrasted with most conventional methods.

Also, notice that by the above algorithm, the vehicle automatically has the capability of obstacle avoidance. Actually, obstacles in the path are treated as outside-path objects (like furniture) in this study.

B. Computation of degrees of collisions

The degrees of collisions of the left and the right route sides are defined and computed in this section. We first define a rectangular window in each captured image with two sub-windows separated by a centerline, as shown in Fig. 3.3(e). A region in each rectangular window of captured images represents an area which is two meters in front of the small vehicle. Then the proportion of the route area in the left sub-window is defined to be the degree of collision of the left route side, which will be denoted by P_L. And that of the right route side can be defined similarly, and will be denoted by PR.

3.4.2 Features for Vehicle Guidance in Corridors

The demanded features for the fuzzy guidance system for the corridor type of Figure 3.3 Illustration of image processing results (a) An input image. (b)

Result of candidate route point classification. (c) Result of route area extraction. (d) Result of guidance line computation. (e) Another input image. (f) Result of guidance line computation of (e).

indoor environments are different from those used for the room type. Two kinds of features, namely, baseline angle at the right side or at the left side, and baseline height at the right side or at the left side, are computed by processing an input image captured with the wireless camera in every navigation cycle. These features are taken as inputs into the fuzzy guidance system, which yields an angle as the output for use to steer the vehicle in each navigation cycle. In the following, we describe how we extract the two kinds of features by image processing techniques in captured images for use in vehicle guidance.

A. Computing baseline angle at right side or at left side

The feature of baseline angle either at the left or the right side means the angle between the lower edge of the baseline and the left or right image boundary in the captured image in every navigation cycle. Since baselines are more common in building corridors, we utilize their visual characteristics in the field of view of the camera on the vehicle to adjust the vehicle’s moving direction in the navigation process. We propose an algorithm to compute the value of the baseline angle feature at either side in the input image in the following.

Algorithm 2. Computation of baseline angle at either side.

Step 1. Convert the input image into a binary image with a threshold Tb that is defined in advance. (The result of the above example is shown in Fig.

3.4(b).)

Step 2. Apply the Sobel operator to the processed binary image to find all edges in the input image. (The result of the above example is shown in Fig. 3.4(c).) Step 3. Find the point of the lower edge of the baseline intersecting the image

boundary at either side, and find the other end point of the baseline in the

image using a connected component computation method [21].

Step 4. Calculate the line equations of the two edges (upper and lower), denoted as Lr and L_l, of the baseline at either side according to the two points found in the last step. Denote also the two vertical image boundaries as V_r and V_l . (The result of the above example is shown in Fig. 3.4(d) and (e).)

Step 5. Calculate the angle between L_r and V_r, and that between L_l and V_l, and denote them as θr and θ_l, which are the desired features as inputs to the fuzzy guidance system. (The result of the above example is shown in Fig.

3.4(d) and (e).)

In the above algorithm, we use conventional image processing techniques to compute the two baseline angles. It is pointed out here that the two baselines are not both seen at every navigation cycle, because the direction of the vehicle may be directed to the left or right side. Therefore, a reasonable assumption is made in advance, that is, if no baseline angle can be found due to failure of finding the baseline, then the baseline angle is set to 90 degrees.

B. Computing baseline height at right or left side

The feature of baseline height means the y image coordinate value of the start point of each baseline in the input image, as shown in Figs. 3.4(d) and 3.4(e). This y coordinate value can be obtained easily by performing Steps 1 through 3 of Algorithm 2. The baseline height at the right side will be denoted by Hr, while that at the left side by H_l. Generally speaking, the value of the baseline height is larger when the direction of the vehicle tends to the right or left wall, and on the contrary, the value is smaller when the direction of the vehicle tends to the midway of the corridor.

Figure 3.4 Illustration of image processing results (a) An input image. (b) Result of binarization process. (c) Result of applying Sobel operator to (b).

(d) Result of finding connected components. (e) An another input image. (f) Result of applying binarization process and Sobel operator.

3.5 Proposed Fuzzy-Control Systems and Their Uses in Guidance

After the demanded features described in Section 3.4 are acquired, the input process of the fuzzy control system is finished and the next process is started. In the following, the fuzzy reasoning process that consists of fuzzy rule design, fuzzy aggregation, and defuzzification will be discussed. Because of the difference between the previously-mentioned two indoor environments types, two different fuzzy reasoning processes and their respective defuzzification processes are designed in this study and will be detailed in Section 3.5.1 and Section 3.5.2, respectively.

3.5.1 Proposed Fuzzy Control System for Room-Type Guidance

In the proposed fuzzy control system, when an acquired feature is processed, a steering angle θsi

is computed for use in turning the vehicle in the ith navigation cycle.

If θsi

is positive, it means that the vehicle should turn leftward for the angle of θsi

; if θsi

is negative, it means that the vehicle should turn rightward for θsi

. The proposed fuzzy control system is based on two fuzzy rules in terms of three linguistic variables LESS, LEFT, and RIGHT that describe the values of the input features. The rules are described as follows:

Rule 1: if the collision-free direction θg trends to LEFT and the degree of collision of the right route side PR is LESS, then turn the vehicle rightward for the

angle of θsi

The fire strengths of Rules 1 and 2 can be calculated accordingly as follows:

( )

^{( )}

where ∧ denotes the AND operator which is defined as the minimum function. After the fuzzification stage, the conclusion of each rule can be derived according to fuzzy

where ∧ denotes the AND operator which is defined as the minimum function. After the fuzzification stage, the conclusion of each rule can be derived according to fuzzy