1. Thongam Khelchandra
Jie Huang
Information systems Department,
The University of Aizu, Japan
Somen Debnath
Department of Information Technology,
Mizoram University, India

2. 1. Introduction
• Problem Definition
• Previous Work
• Tools Used
• Advantage
• Diagram showing whole process
2. Path Planning using Artificial Neural Network
3. Fuzzy Logic System for Obstacle Avoidance
4. Results
5. Conclusion
2

3. Problem definition

a robot with an initial location and orientation, a goal
location and orientation

a set of obstacles located in workspace (static or moving)

compute a collision-free path for the robot

intelligent control of the robot which should move safely in
the environment.
3

4. 
-


Computational geometry
potential functions, roadmap methods, cell
decompositions, sampling based algorithms
unfeasible in real time
Artificial neural network
solves the problem
use of parallel algorithm by ANN
Hybrid system (Neuro-Fuzzy, Genetic-Fuzzy)
4

5. • Artificial Neural Network
- Multilayer
Perceptron with BP learning
algorithm
- Classification task
- Trained to choose a path from n set of paths
• Fuzzy Logic System
- FL is use for obstacle avoidance

6. 



The difficulties of traditional method in
creating the configuration space with
expensive computation are solved by using
neural networks
This method realizes a considerable increase in
performance and speed
If some of the neurons do not work due to lack
of information, still the system will work and
get the output
the combination ANN and fuzzy system is
computationally efficient by helping each other
to eliminate their individual limitations.

7. Schematic Diagram of the whole process
FUZZY
SYSTEM
Distances to
obstacles
INPUT
If all
paths
blocked
by
obstacles
NEURAL
NETWORK
OUTPUT
Collision free
path

8. • ANN is trained with some
training samples initially
• xi is the input which is the
distance in n directions to the
first obstacle
• Vij is the weight connecting
the ith input to the jth hidden
neuron
• wjk is the weight connecting
the jth hidden neuron to the
kth output neuron
• ok is the output, 0 or 1
MLP Network

9. TRAINING PHASE
•x1, x2, x3 are inputs to ANN
•
if obstacle in the path, then
desired output di = 0 else di = 1
• in the figure, d = [ 0, 1, 1]
i
• error between the actual output
oi and desired output di is
minimized by adjusting the
connecting weights
•w
jk
(n+1) = wjk(n) + η*δk(n)*yj(n)
Robot choosing
direction

10. OPERATION PHASE
•Use
the weights vij and
wjk to find the outputs
•
eg: oi = [0,1,0] will
choose the center path
•
if all paths block by
obstacle i,e oi = [0,0,0]
then FL system is use to
avoid the obstacles
Path of the robot

11. • At position 5, all the
3 paths are blocked by
obstacles
• In such situation, FL
is applied
All paths blocked by obstacles

12. •
Input
1. Variable : angle
- Angle between the left obstacle edge and the
robot centre
Values : {small, medium, large}
2. Variable : distance
- Distance between the robot and the obstacle
blocking the middle path
Values : {near, far, very far}
3. Variable : left_obstacle_dist
- Distance between critical obstacle and nearest
obstacle on the left side

13. 4. Variable : right_obstacle_dist
- Distance between critical obstacle and nearest
obstacle on the right side
Values : {near, far}
• Output
1. Variable : adjustment angle
- Adjustment Angle of the robot to avoid
possible collisions with obstacles on left or
right side of the critical obstacle
Values : {small_left, normal_left, big_left,
small_right, normal_right, big_right}

14. •
angle,
distance,
left_obstacle_dist,
right_obstacle_dist are the
input variables
• adjustment angle is the
output variable
• the robot avoids the
obstacles O’s when
moving from point c
• the rule base consist
of 36 rules of the form
if(angle == S) and (distance ==
F) and (left_obstacle_dist
== F) and (right_obstacle_dist ==
F) then adjustment
angle = SL
Avoiding obstacles

15. Rule Evaluation
Calculation for degree of membership of the input values
• Use Trapezoidal membership function with max-min composition
T1 = (input - a) / (b - a)
T2 = (d - input) / (d - c)
T1 = Min(T1, Min(1, T2))
T= Max(T1, 0)
15

16. Defuzzification

Weight of a rule is calculated by
W = angle(angle-input) * distance(distance-input) * left_obs_dist(left_obs_dist-input) *
right_obs_dist(right_obs_dist-input)

The function angle( ), distance( ), left_obs_dist( ), right_obs_dist( ) will
give the value of degree of membership
Actual output is calculated by equation
Crisp output= [(w1 * v1) + (w2 *v2) + (w3 *v3) + (w4 *v4) +(w5
*v5) + ………......+ (wn * vn)]/ [w1+w2+w3+w4+w5+……wn]

16

17.  the robot chooses the
middle path for the first,
second and third motion
using neural network
 in the fourth motion, the
robot chooses the right path
 from fifth to seven, it
chooses the middle path
 in eight motion, it
chooses the left path
 at the ninth motion, all the
3 paths are blocked by
obstacle A
 the obstacle at the ninth
motion is avoided using
fuzzy logic
Robot path upto ninth motion

18.  The values of the input
variables of the fuzzy system of
the robot ninth motion are 1. angle
= M 2. distance = F 3.
left_obstacle_dist
=
N
4.
right_obstacle_dist = N.
 The degree of membership for
variable angle with linguistic value:
(1) small = 0.000000 (2) medium =
0.386667 (3) large = 0.226667.
 The degree of membership for
variable distance with linguistic
value: (1) near = 0.000000 (2) far =
0.750000 (3) very far = 0.500000.
 The degree of membership for
variable left_obstacle_dist with
linguistic value: (1) near =
1.000000 (2) far = 0.500000.
 The degree of membership for
variable right_obstacle_dist with
linguistic value: (1) near =
0.666667 (2) far = 0.750000.
Robot reaching the goal

19. 





Proposed a new method of path planning of a mobile
robot using ANN and Fuzzy system
ANN is used to choose a path from a set of paths
The fuzzy system is used when all the paths are
blocked by obstacles
Results show that the combination of these features is
computational efficient by helping each other to
eliminate their individual limitations
increase in performance and speed as compared to
traditional method with computational geometry
Future work can be path planning in dynamic
environments containing moving obstacles

20. Thank you very much !