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CHAPTER 3

DYNAMIC PATH PLANNING AGENT (DPPA) BASED

APPROACH FOR MOBILE ROBOT NAVIGATION IN

DYNAMIC ENVIRONMENT

3.1 INTRODUCTON

Mobile robot applications often require repeated traversal in a

changing environment between predefined start and goal points. For example,

a mobile robot could be used to transport details and sub-assemblies between a

store and production lines. This task implies repeated traversal between the

store and the production cells. Jan Willemson and Maarja (2006) presents a

mobile robot that can be used for surveillance. This task implies visiting

certain checkpoints on a closed territory in a predefined order. Real

environments where these kind of mobile robots have to operate are dynamic

by nature. From the point of view of mobile robot navigation, it means that

unexpected obstacles can appear and disappear.

Robots that operate in the real world need to respond rapidly to

changes in the environment. A path to the robot’s goal, generated with

available data, quickly becomes invalid as the environment changes or the

robot receives new information. A challenge in mobile robots, results in

re-planning the path as quickly as possible. Of all the most challenging, are

environments that contain dynamic obstacles and obstacles with associated

costs, such as personal space around people, buffer zones around dangerous

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vehicles, or rough terrain. As sensors are imperfect, robots navigating in

dynamic environments must re-plan whenever they receive new sensory data

in order to ensure a safe, low-cost path.

Researchers are now trying to operate in the real world. Examples

of such robotic systems can be found indoors and outdoors doing their best to

carry out autonomously tasks as diversified as sweeping floors (eg. Probotics

Cye-SR, Gecko Carebot, iRobot Roomba), mowing lawns (eg. Friendly

Robotics RoboMower, Husqvarna Automower), moving goods in warehouses,

factories and port terminals (eg. Seegrid SmartTruck, BT Industries Autopilot,

Frog Container Carriers), tour-guiding people in museums or shows

(eg. Rhino, Minerva, Robox, Rackham), helping people with disabilities

(eg. GuideCane, MAid), driving people around (eg. Frog ParkShuttle and

CyberCab), and even taking part in races (eg. Darpa Grand Challenge).

Designing an autonomous robotic system requires the solving of a number of

challenging problems in domains such as perception, localisation, environment

modelling, reasoning and decision-making, control, etc. Aydin et al (2008)

proposed whatever may be the kind of tasks that is expected to be carried out,

at some point or the other, it is required to move. Motion is therefore a

fundamental issue in Robotics.

Real time collision-free motion planning of mobile robots is one of

the most important issues in robotics. Mobile Robot Navigation in dynamic

environments with moving obstacles is a real challenge in path planning.

Several approaches like the classical Edge Detection by Kuc and Barshan

(1989), the Certainty Grid by Elfes (1987), the Potential Field by Khatib

(1986), the Virtual Force Field (VFF) by Borenstein and Koren (1989),the

Vector Field Histogram (VFH) by Borenstein and Koren (1991), the Dynamic

Window Approach by Burgard et al (1997), the Configuration Space

Approach by Lozano-Perez (1983) and the Dynamic Programming (DP) by

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Willms and Yang (2006) have been proposed for mobile robot path planning

in dynamic environment. However mobile robot navigation in dynamic

environments is still a challenge for real world applications. The robot should

be able to reach its goal position, navigating safely among moving people or

vehicles, facing the implicit uncertainty of the surrounding world and the

limits of its perception system.

Motion planning in dynamic or uncertain environments is an

important problem in the field of mobile robots. In motion planning, the

navigation of the mobile robot is characterised by two main aspects, obstacle-

avoidance and goal-seeking.

Gaudiano et al (1996) proposed a neural-network model for a

mobile robot navigation, which generates dynamic collision-free trajectories

through unsupervised learning. This model is computationally complicated

since it incorporates the vector-associative-map model and the direction-to-

rotation effectors control transform model. The generated trajectories using

learning-based approaches are not optimal, particularly during the initial

learning phase. Glasius et al (1995) proposed to avoid the time-consuming

learning process, a Hopfield-type neural-network model for real time

trajectory generation with obstacle avoidance in a non stationary environment.

Jarvis and Sardjono (2003) proposed unknown dynamic

environments, a linear combination of vectors which is used to obtain the path

that is a compromise between the safest and the shortest path. Heuristic search

algorithms, A* can find optimal paths, but typically do not run fast enough to

replan in real time, as the robot receives new sensory data. Genetic Algorithm

also supports optimal path selection during navigation. Learning algorithms

such as reinforcement algorithms makes the robot predict the environment and

guides it for path selection in a dynamic environment.

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Willms and Yang (2006) motivate this research for movingobstacles. The proposed DPPA (Dynamic Path Planning Agent) uses distance

prediction dynamically, at every time step. DPPA analyses the various paths toreach the target without collision of the static and dynamic obstacles in the

path by calculating their location in the grid structure and decides the shortestpath to reach the target in minimum time.

3.2 DPPA ENVIRONMENT

In the proposed (10×10) grid indoor environment, the ordereddistance propagation module calculates the current status of mobile robot, theneighboring grid current values and decides the incremental movement. Robotenvironment is discretized into a grid of M points, labeled by an index i, each

point being a free space or a barrier location, the targets and the robot mayoccupy any free space. dmin and dmax to be the minimum and maximumdistances between any two adjacent neighbors in the grid. Bi to be the set offree spaces that are adjacent neighbors to point i. The distance dij between any

two free spaces i and j is defined to be the minimum Euclidean length of allpaths joining i and j through non barrier adjacent neighbors. Each grid pointhas an associated variable xi(n), a real value that records the distance to thenearest target at time step n. The system is initialized by setting the variables

xi(0) to 0 for all target locations and all other locations to large maximaldistance D. D > (M 1) dmax is sufficiently large. The dynamic system thenevolves by updating the grid points in an order depending on the distance tothe target.

0, if i=target

xi (n+1) = D,if i=barrier (3.1)

Min(D,f(i,n)), Otherwise

where f(i,n) = min (dij+xj(n)) j € Bi

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The robot navigation in the dynamic environment is shown in

Figure 3.1.

Figure 3.1 Dynamic Path Selection

Initially the robot is assumed at position (1, 1). During the first step

movement the neighbouring grid cell values are (0,0), (0,1),

(1,0),(0,2),(1,2),(2,1) and (2,2). Updating neighbours values using the

Eqn. (3.1), the corresponding values of the above grid position are (4.2, 3.6,

3.6, 3.1, 3.1, 2.3, 2.3, and 100). So the next position movement (2,1) is the

minimum value. Similarly robot selects the time step movement in the gird

environment by calculating the distance propagation values each time, until it

reaches the target.

3.3 PROPOSED METHODOLOGY

The DPPA methodology includes checking the presence of both

static and dynamic obstacles in the path to reach the goal, and dynamically

calculates the distance between the current position of the mobile robot and

the target through the free adjacent grid cells, deciding the shortest path and

moving likewise throughout the distance to reach the goal.

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Figure 3.2 shows flowchart for the dynamic path selection in the

proposed environment. The starting point is marked as the source and the goal

is also marked. Now the source scans all its adjacent 8 grid points for the

presence of the goal. If it is found then it moves to that point and stops. If no

goal is found then the adjacent points are scanned for obstacles. If any obstacle

is found then the corresponding grid point is dropped. Now for the remaining

adjacent points, the one which has the minimum Euclidean distance to the goal

is identified and marked as the “temp”. Now the source is shifted to the

“temp” and then the same process iterates till the goal position is reached.

The following assumptions are made in the work flow of the Robot

Agent movement:

1. The entire Indoor Environment is divided into a group of Grid

cells of size (10×10)

2. The shapes of dynamic obstacle agents are captured by various

simple shapes like triangle, rectangle, etc.,

3. The speed with which the robot Agent moves is assumed to be

higher than the speed of the obstacles.

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Figure 3.2 Flow chart for DPPA Navigation

From Figure 3.2, it can be seen that the current position of the

Robot Agent is marked as the source point during every step in its movement.

From the current cell, the robot agent checks the adjacent cells and calculates

the Euclidean distance from each of the free adjacent cell to the goal. Then,

the robot checks for the minimum Euclidean distance to reach the goal and

Start

Select the Current Position of the Robot as the Source Point

Input the starting location of theRobot, Goal and the Obstacles

For each of the adjacent points to the source

Compute the Euclidean distance between all the freeadjacent points to the source, and the goal point

Amongst this find the free adjacent cell which hasthe minimum Euclidean distance to the goal and

store it as temp

Move the robot to thepoint saved as temp

Stop

Whether thatpoint is the

Goal

Yes

No

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stores the position of that cell value. Now, the robot having marked its

location for the next step moves to the value stored in the cell.

From the current location which is now defined as the source point,

the Robot agent checks the present adjacent cells for obstacles, identifies the

free cell that has the minimum distance to the goal, saves in “temp” and moves

further. The same procedure is repeated by the robot agent till it reaches the

goal. Assuming all the adjacent positions are identified to be having obstacles

at any stage, then, the DPPA displays the non-availability of a free path to

reach the goal and stops the navigation.

3.3.1 Obstacle Visualized as Point and Triangle

In the first case, the obstacle agents are depicted as two “single”

grid points. Two numbers of single grid points are considered. The movements

of these obstacles agent are random and so, their locations cannot be predicted

precisely. Hence, the robot checks for the presence of these obstacle agents in

its path to reach the goal using the DPPA in its every step, avoids collusion

and moves through the minimum distance to reach its goal.

In the next instance, the obstacle agent is considered to be of twin

triangular shapes. The triangle is considered to have three single points as

obstacle agent. Hence, using DPPA, the robot agent checks the three

consecutive grid points forming the base of the triangle in its adjacent cells

and having obstacles agent and to avoid collision, moves through free adjacent

cells to reach the goal. Figure 3.3 shows the path selection with DPPA for the

grid and triangle obstacle agent.

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Figure 3.3 Moving Obstacles - Grid and Triangle Shape Obstacles

(Notation: The blue line depicts the path traversed by the source, avoiding the

obstacle at each instant of time. The red line indicates the random path

traversed by the obstacle)

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3.3.2 Obstacle Visualized as Rectangle and V Shape

Similar to the triangle, the rectangular obstacles and “V” shaped

obstacles are presumed to have three single points. In the proposed research

work, two rectangles and two V shaped obstacles are considered wherein the

Robot agent has to maneuver six single points in each instance to avoid

collusion using DPPA, and moving through the minimum distance thereby

with minimum path cost to reach the goal. The advantage the Robot agent

selects its movement in these cases is that the Robot agent has the freedom to

pass in between the twin obstacles agents. Figure 3.4 shows the path selection

with DPPA for rectangle and V shaped obstacle agents.

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Figure 3.4 Moving Obstacles - Rectangle and V Shaped Obstacles

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3.3.3 Robot in the middle of twin obstacles moving in opposite

directions around it

Figure 3.5 shows the dynamic path selection in between the twin

obstacle agent moving in opposite directions.

Contrary to the assumptions made so far wherein the movement of

the obstacle agent was random and unpredictable, now, the paths of the

obstacle agent are defined in this context. Not a single obstacle agent but two

are assumed to move in opposite directions and the robot agent is placed

inside the two obstacle agents moving in opposite directions.

The movements of the inner and outer obstacle agents are known

and defined and this concept is made use of in DPPA. Using the defined

DPPA, the Robot agent first moves out of the first inner obstacle agent without

collision and safely crosses the second outer obstacle agent to reach the goal.

A situation where the outer obstacle agent cannot be traversed without

collision is simulated and in that context, the Robot Agent traverses back to

the inner path, waits for the free path and then proceeds.

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Figure 3.5 Twin obstacles moving in opposite direction

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Figure 3.5 (Continued)

Results inferred from the simulation of mobile robot using DPPA are,

1. The robot agent is able to reach the goal without collusion in

an Indoor environment amidst obstacle agents.

2. Be the dynamically moving obstacle agents is grid shaped or

triangles or twin rectangles or V Shaped, the robot agent is

capable of finding its path to the goal.

3. With the obstacle agent moving around the Robot agent with

the inner layer obstacle in one direction and the outer layer in

the opposite direction, the Robot agent finds its way to reach

the goal without collision.

4. Every step of the robot agent is taken after ensuring the free

path without obstacle agent.

5. It is ensured that the shortest path is chosen thereby resulting

in minimum cost and minimum time.

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6. It forms the base for utilizing this concept in day-to-day

applications where context sensitive dynamic movements are

required.

7. DPPA plans each task as a discrete control in which optimal

solutions are obtained, using efficient dynamic path planning.

3.4 PERFORMANCE ANALYSIS AND DISCUSSIONS

Table 3.1 shows the results of the mobile robot agent source

position 1 where the robot starts the navigation, the target position assuming

the default 95 for convex shaped obstacles. The execution time for rectangle,

triangle, and grid obstacles is very less. For the complex nature of obstacles

like inward C and outward C the movement of obstacles agent simultaneously,

DPPA calculation time was twice compared to other obstacles listed in the

table.

Table 3.1 Simulation Results

S. No InitialPosition

GoalPosition

ObstacleShape

Executiontime

(milliseconds)

Path Cost

1 1 95 Grid point 29 574.262 1 95 Triangle 40 603.553 1 95 Rectangle 69 874.20

4 45 100

Twin obstaclesmoving inoppositedirections

139 1324.26

The simulation result shows the execution time increases as the

complexity of the environment is increased with respect to change in obstacle

size and shape. Path cost is defined as the total grid cell values the robot

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traverse in the environment to reach the goal. As the twin obstacles need two

times calculations performed for each step movement of the robot, the time

taken as well as path cost is doubled.

3.4.1 Comparison of Simulation Results

Simulation results were compared with Willms and Yang (2006) the

work of the inclusion in the complex nature of obstacles. The Dynamic

Programming strategy with Agents observes the environment and makes the

robot perform efficient path selection. The execution time is 63.1 seconds

compared to the conventional algorithm which is a little higher 84.9 seconds.

The reduction time is due to the agent being coordinated with every step

movement of the mobile robot during its navigation in the dynamic

environment. Since the dynamic obstacle movement is only considered the

comparison was made for the inward and outward movements of the obstacle

movement. The work of Willms and yang (2006) considers the target, moving

along with obstacles, and comparison was made for the common objective

where target movement is not performed in the environment.

3.5 DPPA IMPLEMENTATION IN REAL TIME MOBILE

ROBOT

Robots that operate in the real world need to respond rapidly to

changes in the environment. A plan of available data to the robot’s goal

quickly becomes invalidated as and for the environment changes or the robot

receives new information. Then the challenge to mobile robots is the

replanning of paths as quickly as possible, especially for the challenging

environments with dynamic obstacles and obstacles with associated costs,

such as personal space around people, buffer zones around dangerous vehicles

and rough terrain. As sensors are imperfect, robots navigating in real time

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dynamic environments must replan whenever they receive new sensory data in

order to ensure a safe, low-cost path.

3.5.1 Description of Fire Bird V Mobile Robot

A real time environment is created with a rectangular area of

80 cm 75 cm. This constitutes a dynamic work space in which the obstacles

are moving while the boundary is fixed. Fire Bird V robot has 2

microcontrollers: An ATMEGA2560 - master microcontroller and ATMEGA8

- slave microcontroller. Position encoder discs are mounted on both the

motor’s axle of the Robot to give the position feedback to the microcontroller.

Figure 3.6 Fire Bird V Mobile Robot

Figure 3.6 shows the robot has two DC geared motors for the

locomotion. It is marked with 8 IR sensors to identify the presence of the

obstacle in all the directions around the Robot. The robot has a top speed of

24cm / second. The Front castor wheel provides support at the front of the

robot. Using this configuration, the robot can turn with zero turning radius by

rotating one wheel in the clockwise direction and the other in the counter

clockwise, direction. Position encoder discs are mounted on both the motor’s

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axles to give a clockwise position feedback to the microcontroller. White line

sensors are used for detecting the white line on the ground surface. White lines

are used to give the robot a sense of localizations. A white line sensor consists

of a highly directional phototransistor for line sensing and a red LED for

illumination.

For Microcontroller programming, the AVR (Alf Vegard RISC)

Harvard modified architecture is used to program Atmel Microcontrollers. The

AVR integrated development environment supports faster error tracking in the

real time environment. IR Proximity sensors (to identify the obstacles) and IR

Range sensor (to measure the exact distance) are used. The concept that the

sensor values remain high when there are no obstacles around it and it starts

decreasing on approaching an obstacle is used here. Robots usually have

different kinds of sensors distributed in various nodes. Sensory information is

used to calculate the actions that the robot will execute.

3.5.1.1 IR Range Sensors

IR sensors are used to identify the presence of the obstacle in all the

directions around the Robot. IR proximity sensor consists of an IR LED and

an IR photo diode. IR LED is used to illuminate the object and the IR photo

diode is used to identify the presence of the obstacle based on the reflected

light of the LED from the object. The data values of all the 8 sensors in

different contexts with / without obstacles in various directions are calculated

in the proposed environment and listed below in Table 3.2.

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Table 3.2 Mapping Sensor Values in the Environment

Obstacle Position IR1 IR2 IR3 IR4 IR5 IR6 IR7 IR8No obstacle along any side 33 235 242 242 245 252 252 253Obstacle at front 233 233 229 241 244 251 252 253Obstacle at front and left 228 232 229 240 244 249 250 252Obstacle at front and right 231 232 229 241 242 249 250 251Obstacle at left only 229 235 240 241 245 251 252 253Obstacle at right only 232 232 236 240 239 249 251 250Obstacle at front, left andright

225 233 231 240 239 249 251 250

Obstacle at back 232 232 236 242 245 250 246 252

Fire Bird V is attached with very sensitive IR range sensors. Though

8 IR range sensors are attached with the Robot, only 5 sensors are used for the

forward movements. The IR range sensors have 2 parts – the IR beam for

generating light and the CCD array for measuring the distance. The light

generated by the IR beam illuminates the object. The light hits the obstacle

and is reflected back. The angle of the reflected light varies corresponding to

the distance of the obstacle. The CCD array measures this angle and calculates

the exact distance of the obstacle.

IR Sensor mapping values makes the robot avoid dynamic obstacles

along the path. A white line position is drawn in the environment as a target. A

white line sensor in the mobile robot consists of a highly directional

phototransistor for line sensing and a red LED for illumination. On moving on

a white surface, the sensor values are <=10. The robot is programmed in such

a way that once it detects the while line it stops it navigation and makes a beep

sound before it reaches the target.

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3.6 PERFORMANCE ANALYSIS AND DISCUSSIONS

Figure 3.7 shows the various experiment results for grid, rectangle,

and V shaped obstacles moving in the environment. The Time taken for the

Fire Bird V Robot to reach the target increases with the obstacle shapes in the

environment. Sensor mapping supports the real time execution for dynamic

navigation in the indoor environment.

Each sensor mapping point on the map has only local connections to

its neighbouring mapping from which it receives information in real time. The

information stored at each point is a current estimate of the distance to the

nearest target and the neighbour from which this distance was determined.

Updating the distance estimate at each grid point is done using only the

information gathered from the point’s neighbours, that is, each point can be

considered an independent processor, and the order in which grid points are

updated is not determined based on the global knowledge of the current

distances at each point or the previous history of each point.

The robot path is determined in real time completely from the

information at the robot’s current sensor mapping value. The computational

effort to update each point is minimal, allowing for the rapid propagation of

the distance information outward along the grid from the target locations.

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Figure 3.7 Real time Results

. Table 3.3 shows the real time Firebird V implementation. The

average time to reach the goal with DPAA in real time is 67.67 seconds.

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Table 3.3 Real Time Implementation Results

Obstacle ShapeTime to reach the goal

with DPPA in realtime (Seconds)

Point Obstacle 53Rectangle shapedObstacle 67

V Shaped Obstacle 83

Overall Average ShapeObstacle 67.67

3.7 STATISTICAL ANALYSIS

For the various dynamic obstacles in the environment, DPPA

(Dynamic Path Planning Agent) takes less time to reach the goal without

collision when compared with DP (Dynamic Programming). While testing the

(DP) with moving obstacles of various shapes in the environment it takes

longer time to reach the goal comparing with the DPPA. The DPPA takes

lesser time over DP in various tests of dynamic obstacles in the environment.

Similarly in the overall dynamic obstacles the average time taken by DPPA

(69.25 milliseconds) is lesser than the DP (78.00 milliseconds) in reaching the

goal as shown in Table 3.4. SPSS tool is used for statistical analysis.

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Table 3.4 Paired Sample Statistics

Obstacle Shape Time to reach the goal withDPPA (milliseconds)

Time to reach the goal withDP(milliseconds)

Point Obstacle 29 35Triangle shapedObstacle 40 46

Rectangle shapedObstacle 69 78

Twin Obstaclesmoving in oppositedirection

139 153

Mean 69.25 78.00Std. Deviation 49.47 53.22Std. Error Mean 24.73 26.61

Table 3.5 shows the inferential statistical data that is obtained using

Eqn. (2.2),(2.3) and (2.4) given in chapter 2.Here, the “t” value is -4.64. The

column labelled "d.f" gives the degrees of freedom associated with the “t” test.

In this example, there are 3 degrees of freedom. The two-tailed probability

value is 0.02, which is certainly significant and lesser than 0.05. The result

value proves the proposed DPPA method is significant compared to DP.

Table 3.5 Inferential Statistics

Paired Samples Test

Type Comparison

Paired Differences

t value d.fMean Std.

Deviation

Std.ErrorMean

95% ConfidenceInterval of the

Difference

Lower Upper

Pair 1

Time to reachthe goal withDPPA and Timeto reach the goalwith DP

-8.75 3.77 0.89 14.76 -2.74 -4.64 3.00

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3.8 SUMMARY OF THE CHAPTER

In real time mobile robot navigation it is possible for a Robot to

move ahead amidst the dynamically moving obstacles and reach the goal

without collision. This concept is proved to be limited to an Indoor

Environment in real time using Fire Bird V Mobile Robot. The splitting of the

environment into grid points facilitates the Robot to move with the limited

local environment knowledge without the need for any global knowledge. The

obstacles of different shapes like grid size, rectangle, V shaped are used for

the Fire Bird to wade through and reach the goal without collision. It is also

confirmed that the complexity of the obstacle increases the time taken to pass

through it. It is also proved that the Robot selects the optimal path, passes

through the shortest path thereby minimizing the path cost as well as the time.

The domain of DPPA will be expanded from Indoor to Outdoor

Environment amidst obstacles. DPPA also can be thought of extending it to

real life e.g. for the old age and sick people to move about in DPPA powered

wheel chair both in Indoor and Outdoor environments with ease.

Including the Learning Agent in the environment will force the

robot to perform the task autonomously using relevant feature values taken

from the environment, giving reward values all the while taking into account

the robot’s behaviour. Based on the above values, the necessary action is taken

by the mobile robot to reach the goal by breaking the path planning to

subtasks, learn each and every environmental value and reaching the goal by

optimal path selection. The research work in the next chapter is inspired from

the learning concept added to the environment for optimal path selection.