Overview of Robotic Path

Department of Information Communication Technology

Indian Institute of Information Technology and Management Gwalior

Rahul

Kala

Overview of Robotic Path
Planning

Rahul

Kala,

Department of Information Technology

Indian Institute of Information Technology and Management Gwalior


http://students.iiitm.ac.in/~ipg_200545/


rahulkalaiiitm@yahoo.co.in,

rkala@students.iiitm.ac.in

Department of Information Communication Technology

Indian Institute of Information Technology and Management Gwalior

Rahul

Kala

Publications


Kala, Rahul, Shukla, Anupam & Tiwari, Ritu (2009), Robotic Path Planning using
Multi Neuron Heuristic Search,
Proceedings of the ACM 2009 International
Conference on Computer Sciences and Convergence Information Technology, ICCIT
2009,
pp 1318
-
1323, Seoul, Korea


Kala, Rahul, Shukla, Anupam, Tiwari, Ritu, Roongta, Sourabh & Janghel, RR (2009)
Mobile Robot Navigation Control in Moving Obstacle Environment using Genetic
Algorithm, Artificial Neural Networks and A* Algorithm, Proceedings of the
IEEE
World Congress on Computer Science and Information Engineering
, CSIE 2009, pp
705
-
713, Los Angeles/Anaheim, USA


Shukla, Anupam, Tiwari, Ritu & Kala, Rahul (2008), Mobile Robot Navigation
Control in Moving Obstacle Environment using A* Algorithm, Proceedings of the

International conference on Artificial Neural Networks in Engineering, ANNIE 2008,
Intelligent Systems Engineering Systems through Artificial Neural Networks, ASME
Publications
, Vol. 18, pp 113
-
120, Nov 2008


Shukla, Anupam, Tiwari, Ritu, Kala, Rahul (2009) Mobile Robot Navigation Control
in Moving Obstacle Environment using Genetic Algorithms and Artificial Neural
Networks,
International Journal of Artificial Intelligence and Computational
Research
, Vol. 1, No. 1, pp 1
-
12, June 2009

MOBILE ROBOT
PATH PLANNING

Research in

Department of Information Communication Technology

Indian Institute of Information Technology and Management Gwalior

Rahul

Kala

The Problem Statement


Inputs

◦
Robotic Map

◦
Location of Obstacles

◦
Static and Dynamic




Constraints

◦
Time Constraints

◦
Dimensionality of Map

◦
Static and Dynamic Environment

Output

•
Path P such that no collision
occurs

Department of Information Communication Technology

Indian Institute of Information Technology and Management Gwalior

Rahul

Kala

Existing Algorithms:


A* Algorithm


Artificial Neural Networks


Genetic Algorithms


Multi
-
Neuron Heuristic
Search (MNHS)


Neuro
-
Fuzzy

Self designed Algorithms:

Multi Algorithms/Hierarchical Algorithms


Hierarchal MNHS


Hierarchical A* with Genetically
Optimized Fuzzy Inference System


Evolving Robotic Path with
Genetically Optimized Fuzzy
Inference System


Swarm Intelligence etc

Problem Implementation by

Department of Information Communication Technology

Indian Institute of Information Technology and Management Gwalior

Rahul

Kala

A* Algorithm

“I believe this is this way takes me shortest to
the destination…. Lets give it a try”


“Hey I got struck… I’ll choose another path”



Add all possible moves in an
open list
.


Make the best move as per
open list

status


Add all executed moves in the
closed list

Department of Information Communication Technology

Indian Institute of Information Technology and Management Gwalior

Rahul

Kala

Results

Department of Information Communication Technology

Indian Institute of Information Technology and Management Gwalior

Rahul

Kala

ANN with Back Propagation Algorithm

“Whenever this type of situation arrives… Always make
this move”


“Hey rules failed… I’m struck…

OK make random moves till you are out”



Frame input/output pairs for every
situation
comprising of robot position, goal position and
environment


Learn these and use them in decision making


Make random moves when position deteriorates

Department of Information Communication Technology

Indian Institute of Information Technology and Management Gwalior

Rahul

Kala

Results

Department of Information Communication Technology

Indian Institute of Information Technology and Management Gwalior

Rahul

Kala

Genetic Algorithms

“Show me some random paths so that I may decide”


“OK this path is the best to go till a point and this
path the best for the other part of the journey…
Let me mix them both…”



Generate random complete and incomplete
solutions:
source to nowhere, nowhere to goal and
source to goal


Try to mix paths to attain optimality


Generate random paths between needed points

Department of Information Communication Technology

Indian Institute of Information Technology and Management Gwalior

Rahul

Kala

Graphical Genetic Operators

Mutation

Department of Information Communication Technology

Indian Institute of Information Technology and Management Gwalior

Rahul

Kala

Results

Department of Information Communication Technology

Indian Institute of Information Technology and Management Gwalior

Rahul

Kala

MNHS Algorithm

“I believe this is this way takes me shortest to the
destination…. Lets give it a try”


“But in the process I may get struck…

Lets walk a few steps on bad paths as well”



Add all possible moves in an
open list
.


Make the a range of moves best to worst
as per
open list

status


Add all executed moves in the
closed list

Department of Information Communication Technology

Indian Institute of Information Technology and Management Gwalior

Rahul

Kala

Basic Concept of MNHS

Department of Information Communication Technology

Indian Institute of Information Technology and Management Gwalior

Rahul

Kala

Results

Department of Information Communication Technology

Indian Institute of Information Technology and Management Gwalior

Rahul

Kala

Simple Algorithm Analysis

Algorithm

Advantages

Disadvantages

A* Algorithm

Computationally

shortest
paths in best times.

Works only for small

graphs and
restricted and quantized moves

Artificial
Neural
Networks

Can incorporate dynamic
changes in environment.
Computationally very fast

Only works for simple graphs.
Gets trapped in complex graphs.
Path not optimal.

Restricted
Moves.

Genetic
Algorithms

Work for larger and complex
graph.

Computationally expensive.

MNHS

Low computation and best
path lengths in complex

and
uncertain graphs

Works only for small

graphs and
restricted and quantized moves


Neuro
-
Fuzzy
Algorithms

Can incorporate dynamic
changes in environment.
Computationally very fast

Only works for simple graphs.
Gets trapped in complex graphs.
Path not optimal.


These are theoretically advocated

and experimentally supported

Department of Information Communication Technology

Indian Institute of Information Technology and Management Gwalior

Rahul

Kala

The Big Observation

Individual Simple Algorithms
have disadvantages …


They’re too simple for many
complex situations

a
nd

hence the game starts…

Department of Information Communication Technology

Indian Institute of Information Technology and Management Gwalior

Rahul

Kala

Thank

You