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Cooperative Behavior & Path Planning for Autonomous Robots

Cooperative Behavior & Path Planning for Autonomous Robots. Lakshmanan Meyyappan (Laks). Bird’s Eye View. Overview Motivation The Scavenging process Experimental Setup Fuzzy Clustering Evolutionary Algorithm Barbarian. Middle and Modern Age Crossover & Mutation The Twin Problem

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Cooperative Behavior & Path Planning for Autonomous Robots

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  1. Cooperative Behavior & Path Planning for Autonomous Robots Lakshmanan Meyyappan (Laks)

  2. Bird’s Eye View • Overview • Motivation • The Scavenging process • Experimental Setup • Fuzzy Clustering • Evolutionary Algorithm • Barbarian. Middle and Modern Age • Crossover & Mutation • The Twin Problem • Results & Research Findings • Future Work

  3. Overview … • Scavenging process • To program the robot to collect objects randomly distributed in an open terrain • Assumptions: • Static environment • Aerial picture of the entire terrain is available

  4. Computer Aerial Picture Image processing Object Locations Fuzzy Clustering Robots in Action Optimum Path Evolutionary Algorithm Overview

  5. Motivation • Scavenging Robots are useful for • Collecting samples from chemically hazardous locations • Landmine removal • Exploring unknown regions • Collecting rock samples from other planets • Assembly line robots • The Evolutionary Programming with some modifications can be used in a number of other areas – network routing, school bus routing, drilling holes in a circuit board ….

  6. The Scavenging Process • The area is too small to fit large number of objects • Evolutionary Algorithm not very efficient

  7. 360 X 160 Matrix (Football field size – 360 X 160 Feet) Random 0’s & 1’s (limiting 1’s to less than 5% in average) 1’s are the objects to be collected R R 1 1 1 1 R R The Experimental Setup 1 160 1 360

  8. Fuzzy Clustering • Time Saving: • N objects present • Search space N! • M clusters • Then search space becomes (N/M)! • If N is large (N/M)!<<N! • 4 Robots – 4 clusters • Better results than manual clustering

  9. Evolutionary Algorithm • To find the shortest path for the four robots • Overlook: • The Barbarian Age • The Middle Age • The Modern Age • Selection: • Rank Based • Elitist (50%) • Operations: • Co evolution • Crossover • Mutation

  10. The Barbarian Age • Random population created • Example: 1-2-3-4-5 (for 5 objects) • Environment in total chaos (Middle of thick Chinese forest) • No parent selection & No crossover • Co evolution takes place • Entire population mutates • The shift register mutation • 1-2-3-4-5 5-1-2-3-4 4-5-1-2-3 • Starting point optimization • During each cycle (total number of cycle less than N), the fittest population (shortest path) are pooled together • After N cycles, the pooled population is moved to a separate location (Netherlands) – The Middle age

  11. The Middle Age • Rigid classification • Royal family, Knights, Working Class, Slaves • All are allowed to breed, but only within their class • Helps in finding quick local optimums • Crossover & Mutation takes place (discussed later) • The fittest population after a set number of cycles is pooled and moved to the land of opportunities (USA) – The Modern age

  12. Modern Age • No class differentiation • No restrictions on who breeds with who • Avoids locking into local minima • Produces exotic results • Kristin Kreuk • Dutch father • Chinese Mother • Born in Canada • Now in USA

  13. Crossover • The Greedy Crossover • Example Parent 1: 1-2-3-4-5 Parent 2: 4-1-3-2-5 2 4 3 Child: 1 1-3 1-3-2 1-3-2-5-4 3 2 5

  14. Mutation • The chance of mutation reduces with civilization (Barbarian-Middle-Modern) • Example • Route: 1-2-3-4-5 • Attempt 1: 2-1-3-4-5 • Attempt 2: 3-2-1-4-5 • Attempt 3: 4-2-3-1-5 • Attempt 4: 5-2-3-4-1 • The shortest of the four routes is chosen as the mutated offspring

  15. The Twin Problem • If any two child resemble each other (same route), they are called twins • Twins are of no use to us as they represent the same routes • Hence Dr. T, The Terminator is called • Dr. T, terminates one of the twin and replaces it with a random child

  16. Results – So Far…

  17. Research Findings • Time advantage • Is this the optimal solution? • Theoretical advantage • Much faster than a random search or heuristic search • Clustering helps in avoiding robot clashes • Mutation operator is not very good

  18. Future Work • Have a dynamic environment • Eliminate image processing • Rephrase the problem to make comparisons with available TSP datasets & solutions

  19. Hey What Do Ya Think Any Questions?

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