Pinned Repositories
Anveshak
Arduino comm. Control software
Asheeshkrsharma
Config files for my GitHub profile.
ATMD-project
ATMD
blueDad-Api
blueDad-ESP
Scanning code for Esp 32
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
# Particle-filter Robot localization using particle filters and Path Planning using A* search. Run LOCALISE.m Localisation using partice filter  Path Planning using A* search 
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.
devart-template
Identification-OpenCows
Asheeshkrsharma's Repositories
Asheeshkrsharma/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.
Asheeshkrsharma/Identification-OpenCows
Asheeshkrsharma/Anveshak
Arduino comm. Control software
Asheeshkrsharma/Asheeshkrsharma
Config files for my GitHub profile.
Asheeshkrsharma/ATMD-project
ATMD
Asheeshkrsharma/blueDad-Api
Asheeshkrsharma/blueDad-ESP
Scanning code for Esp 32
Asheeshkrsharma/Dartboard-detector
# Particle-filter Robot localization using particle filters and Path Planning using A* search. Run LOCALISE.m Localisation using partice filter  Path Planning using A* search 
Asheeshkrsharma/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.
Asheeshkrsharma/devart-template
Asheeshkrsharma/gateway
WebThings Gateway
Asheeshkrsharma/IAS
Asheeshkrsharma/ICAOdump
ICAO codes and their location
Asheeshkrsharma/libOcultus
End to End encryption system for IOTs.
Asheeshkrsharma/Modal-approach-to-2D-elastica-s-inverse-kinematics-
Asheeshkrsharma/Morphological-Communication
The repository contains the code for the paper "Morphological Communication for swarms"
Asheeshkrsharma/nativescript-background-http
Background Upload plugin for the NativeScript framework
Asheeshkrsharma/Particle-filter
Robot localization using particle filters
Asheeshkrsharma/RRP-Assignment
Version control for RRP research plan assignment
Asheeshkrsharma/solIcons