Overview of Robotic Path Planning

1. 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

2. 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

3. Research in MOBILE Robot Path Planning

4. 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

5. Problem Implementation by Existing Algorithms: Self designed Algorithms: • A* Algorithm • Artificial Neural Networks • Genetic Algorithms • Multi-Neuron Heuristic Search (MNHS) • Neuro-Fuzzy 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

6. 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

7. Results

8. 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

9. Results

10. 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

11. Graphical Genetic Operators Crossover Mutation

12. Results

13. 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

14. Basic Concept of MNHS

15. Results

16. Simple Algorithm Analysis These are theoretically advocated and experimentally supported

17. The Big Observation and hence the game starts…

18. Thank You