This document proposes a method for real-time path planning of mobile robots to avoid collisions in static and dynamic environments. It involves using an artificial neural network (ANN) for obstacle detection and a vector field histogram method with decision trees for local path planning. When all paths are blocked, fuzzy logic will be used to choose a path. The objective is for the robot to safely navigate around obstacles without communication delays. The proposed method aims to overcome limitations of previous techniques by applying ANN, decision trees, and fuzzy logic for robust, real-time collision avoidance.
4. Motivation
Robots send on exploration mission
If robots finds obstacle, sends a signal to earth station
In return earth station respond to that signal
Time consuming and ineffective in real time
application
So this type of inconvenience can be overcome by
the application of collision avoiding robots.
5. Literature survey
Path planning algorithm for motion planning by
Zidek, k Rigasa, E.
Path planning using edge detection method.
In this method, an algorithm tries to determine the
position of the vertical edges of the obstacle and then
steer the robot around either one of the "visible" edges.
The line connecting two visible edges is considered to
represent one of the boundaries of the obstacle.
A common drawbacks are poor directionality, frequent
misreading, specular reflections.
6. The Certainty Grid for Obstacle Representation
In the certainty grid, the robot's work area is
represented by a two-dimensional array of square
elements, denoted as cells. Each cell contains a
certainty value (CV) that indicates the measure of
confidence that an obstacle exists within the cell area.
With the CMU method, CVs are updated by a
probability function that takes into account the
characteristics of a given sensor
In CMU's applications of this method, the mobile robot
remains stationary while it takes a panoramic scan
with its 24 ultrasonic sensors
7. Proposed Method
Application of ANN for path planning and obstacle
detection.
Vector field histogram method using DT for local
path planning.
When all the paths are block then we will used Fuzzy
logic.
8. Artificial Neural Network (ANN)
Compound of a large no. of highly interconnected
processing elements (neurons) working in union to
solve specific problems.
Loosely modelled on biological neural network
Neuron: processing unit
9.
10. Fuzzy Logic
It deals with reasoning that is approximate rather than
fixed and exact.
Three basic steps involve in fuzzy logic
Fuzzification: changing a real scalar value into a fuzzy value.
Rule Evaluation: an inference is made based on a set of rules.
Defuzzification: the resulting fuzzy output is mapped to a crisp
output using the membership functions
11. Working
Our target is to avoid collision with the obstacle in the
path.
To choose path we are using ANN-DT tree
If all the path are blocked then we will use fuzzy logic to
choose the path.
12. Future scope
Games and Virtual
Robot Motion and Navigation
Driverless Vehicles
Transportation Networks
Human Navigation
13. References
[1] Xiongmin Li, Christine W. Chan , “Application of an enhanced decision
tree learning approach for prediction of petroleum production “ , Engineering
Applications of Artificial Intelligence , Elsevier, 23 (2010) 102–109.
[2] Kweku-Muata Osei-Bryson, “Post-pruning in decision tree induction using
multiple performance measures”, Computers & Operations Research, Elsevier,
34 (2007) 3331 – 3345.
[3] Hendrik Blockeel *, Luc De Raedt, “Top-down induction of first-order
logical decision trees”, Artificial Intelligence, Elsevier, 101 (1998) 285-297.
[4] Max Bramer, “Using J-Pruning to reduce overfitting in classification trees”,
Knowledge- Based system, Elsevier, 15(2002) 301-308.
[5] Hussein Almuallim , “An efficient algorithm for optimal pruning of
decision trees”, Artificial Intelligence , Elsevier,83 ( 1996) 347-362
[6] J. Borenstein, Member, IEEE and Y. Koren, Senior Member, IEEE,
“THE VECTOR FIELD HISTOGRAM -FAST OBSTACLE AVOIDANCE FOR
MOBILE ROBOTS », IEEE Journal of Robotics and Automation Vol 7, No 3,
June 1991, pp. 278-288.
