Genetic Algorithms

1. A KNOWLEDGE BASED GENETIC ALGORITHM FOR PATH PLANNING OF A MOBILE ROBOT Presented by: Tarundeep Dhot Dept of ECE Concordia University

2. This presentation is based on a research paper written by the following authors: Y. Hu and Simon Yang This paper was published at: International Conference of Robotics and Automation, New Orleans LA – April 2004 This presentation is solely meant for educational purposes. Acknowledgements

6. THE PROPOSED KNOWLEDGE-BASED GA PROBLEM REPRESENTATION EVALUATION METHOD GENETIC OPERATORS Represented by orderly Distinguishes whether Crossover numbered grids , each path is feasible or not. Mutation of which represents a Indicates difference between Node-Repair location in the environment. path qualities in either Line-Repair category. Improvement Deletion GRID REPRESENTATION EVALUATION FUNCTION

16. U SHAPE OBSTACLE ENVIRONMENT FIGURE (a): BEST INITIAL SOLUTION (COST = 80.59) FIGURE (b): BEST SOLUTION IN GENERATION 8 (COST = 42.02) FIGURE (c): BEST SOLUTION IN GENERATION 22 (COST = 33.98) FIGURE (d): OPTIMAL PATH: BEST SOLUTION IN GENERATION 30 (COST = 29.10)

17. PATH PLANNING IN COMPLEX ENVIRONMENT FIGURE (a): PATH OBTAINED BY GA IN ONE TYPICAL RUN FIGURE (b): THREE ALTERNATIVE PATHS OBTAINED BY GA FROM DIFFERENT RUNS

18. PATH PLANNING IN A DYNAMIC ENVIRONMENT FIGURE (a): PATH OBTAINED IN THE ORIGINAL ENVIRONMENT FIGURE (b): PATH AFTER ADDING OBSTACLE FIGURE (c): PATH AFTER REMOVAL OF THE OBSTACLE

19. COMPARISON OF THE GA WITH AND WITHOUT SPECIALIZED OPERATORS SD: STANDARD DEVIATION Thus, the use of specialized operators improve performance of GA significantly. SPECIALIZED OPERATORS WITH WITHOUT NO. OF RUNS 20 20 BEST FOUND PATH COST MEAN 30.85 61.41 SD 0.67 13.54 NO. OF GENERATIONS MEAN 257 799 SD 123 470

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