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
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