Overview%20of%20Robotic%20Path%20Planning

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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_at_yahoo.co.in,
• rkala_at_students.iiitm.ac.in

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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

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MOBILE Robot Path Planning

• Research in

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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

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Existing Algorithms
Problem Implementation by
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

• A Algorithm
• Artificial Neural Networks
• Genetic Algorithms
• Multi-Neuron Heuristic Search (MNHS)
• Neuro-Fuzzy

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A Algorithm

• I believe this is this way takes me shortest to
  the destination. Lets give it a try
• Hey I got struck Ill 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

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Results
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ANN with Back Propagation Algorithm

• Whenever this type of situation arrives Always
  make this move
• Hey rules failed Im 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

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Results
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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

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Graphical Genetic Operators
Crossover
Mutation
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Results
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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

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Basic Concept of MNHS
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Results
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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
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The Big Observation
and hence the game starts
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Thank You