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Asheesh Sharma's Projects

cvrp-genetic icon cvrp-genetic

The capacitated vehicle routing problem is a well-studied combinatorial computing problem. There are many ways to solve the problem like evolutionary computing, reinforcement learning and exact methods. A genetic algorithm was implemented to solve a given CVRP instance with 250 customers. An evolutionary computing approach was chosen because of the following reasons: 1. The exact methods like branch and cut explore the entire search space which is most of the time unfeasible given the computation time. A genetic algorithm can be fine tuned to explore the search space in a specific way. 2. In this case, the constraints are very simple; maximum vehicle capacity of 500 and a vehicle can visit a customer only once. A real world CVRP can have multiple objectives and constraints like traffic conditions, number of turns, multiple depots etc. A genetic algorithm can adapt to these constraints without the need to change the entire programmatic structure by simply formulating the fitness function again.

dartboard-detector icon dartboard-detector

# Particle-filter Robot localization using particle filters and Path Planning using A* search. Run LOCALISE.m Localisation using partice filter ![alt text](https://github.com/Asheeshkrsharma/Particle-filter/blob/master/2.png "partice filter") Path Planning using A* search ![alt text](https://github.com/Asheeshkrsharma/Particle-filter/blob/master/1.png "A* search")

deepbedmap icon deepbedmap

Going beyond BEDMAP2 using a super resolution deep neural network. Also a convenient flat file data repository for high resolution bed elevation datasets around Antarctica.

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