Solution time reduction techniques of a stochastic dynamic programming approach for hazardous material route selection problem

2013 ◽  
Vol 65 (4) ◽  
pp. 634-645 ◽  
Author(s):  
Sumeet Desai ◽  
Gino J. Lim
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Selection Problem ◽  
Solution Time ◽  
Programming Approach ◽  
Stochastic Dynamic ◽  
Route Selection ◽  
Hazardous Material ◽  
Dynamic Programming Approach ◽  
Reduction Techniques

A Decoupled and Prioritized Stochastic Dynamic Programming Approach for Automated Transport of Multiple Particles Using Optical Tweezers

2009 ◽  
Author(s):  
Ashis Gopal Banerjee ◽  
Wolfgang Losert ◽  
Satyandra K. Gupta
Keyword(s):  
Dynamic Programming ◽  
Motion Planning ◽  
Optical Tweezers ◽  
Stochastic Dynamic Programming ◽  
Graph Matching ◽  
Programming Approach ◽  
Stochastic Dynamic ◽  
Step Method ◽  
Dynamic Programming Approach ◽  
Multiple Particles

Automated transport of multiple particles using optical tweezers requires the use of motion planning to move them simultaneously while avoiding collisions amongst themselves and with randomly moving obstacles. This paper develops a decoupled and prioritized stochastic dynamic programming based motion planning framework by sequentially applying a partially observable Markov decision process algorithm on every particle that needs to be transported. An iterative version of a maximum bipartite graph matching algorithm is used to assign given goal locations to such particles. The algorithm for individual particle transport is validated using silica beads in a holographic tweezer set-up. Once the individual plans are computed, a three-step method consisting of clustering, classification, and branch and bound optimization is employed to determine the final collision-free paths. Simulation results in the form of sample trajectories and performance characterization plots are presented to illustrate the usefulness of the developed approach.

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