Transcript Overview of Robotic Path Planning

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/
[email protected],
[email protected]
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
Department of Information Communication Technology
Indian Institute of Information Technology and Management Gwalior
Rahul Kala
Research in
MOBILE ROBOT
PATH PLANNING

Inputs
The Problem Statement
◦ 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
Problem Implementation by
Existing Algorithms:
Self designed Algorithms:
Multi Algorithms/Hierarchical Algorithms
A* Algorithm
 Artificial Neural Networks
 Genetic Algorithms
 Multi-Neuron Heuristic
Search (MNHS)
 Neuro-Fuzzy

Hierarchal MNHS
 Hierarchical A* with Genetically
Optimized Fuzzy Inference System
 Evolving Robotic Path with
Genetically Optimized Fuzzy
Inference System
 Swarm Intelligence etc

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
These are theoretically advocated
and experimentally supported
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.
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
and hence the game starts…
Department of Information Communication Technology
Indian Institute of Information Technology and Management Gwalior
Rahul Kala
ThankYou
Department of Information Communication Technology
Indian Institute of Information Technology and Management Gwalior
Rahul Kala