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This project involves a vehicle that is lost in a map. It uses an initial estimate of it's position from GPS and distances from landmarks measured by LIDAR and utilizes a particle filter to localize itself in the map
Designed and implemented a controller for an Ackermann kinematic model that could take inputs such as goal location and goal heading and apply the appropriate control input to the motors of the vehicle. The inputs were target heading angle and velocity. The outputs were the steering angles and angular velocities for each of the front wheels.
Implmented Apprenticeship learning via IRL for the f1tenth gym that uses ROS2 Foxy.
Implementation of Behavioral Cloning algorithm to train a car to autonomously drive in a simulator.
Implementation of A*,Dijkstra,RRT and BFS in C++ for Turtlebot3 and Gazebo
The aim of this project is to build an omni directional mobile manipulator that can be used for delivery of groceries and food.
Simulated and analyzed different path planning algorithms such as DWA and Dijkstra in a custom world modeled in Autodesk Fusion 360. The modeled world was imported to Gazebo, and the paths planned by turtlebot3 were visualized in Rviz.
Implementation of extended kalman filter in C++ that fuses data from lidar and radar to track a bicycle moving around a car
Designed a custom mobile robot with independently steerable front wheels in SOLIDWORKS and exported it into the Gazebo simulator and teleoperated with the ROS teleoperation node. Additionally, a publisher and a subscriber node were written to control the robot.
Designed an LQG controller using MATLAB for a system consisting of a mobile crane and two pendulums suspended from it. The non-linear system was linearized, and an LQR controller was designed for both linearized and non-linearized systems. A Luenberger observer was designed for a set of observable states and an LQG controller was designed.
ENPM 808X week 5 project
Implemented the extended Kalman filter for the state (x, y, yaw) estimation of a differential drive robot moving in a circle. The robot had been simulated using a Gazebo physics simulator.
Designed a mobile robot controlled using Raspberry Pi. Used hector mapping and AMCL to map the environment and localize the robot. Dijkstra Algorithm was used to autonomously navigate the robot.
Introspective Vision for Simultaneous Localization and Mapping
Designed LQR and iLQR controllers for multiple non-linear systems describing scenarios such as cart-pole balancing, helicopter hovering, hopper stabilization, and trajectory following for helicopter flight.
Rotation estimation of an object of interest between two images
This is the implementation of a basic PID controller with Test Driven Development.
Predict the direction of travel in camera frame from given dashcam videos
Simulation of obstacle avoiding robot in ROS2 using turtlebot3
Implemented RRT_connect on turtlebot3
Implemented a Unscented Kalman Filter and Sensor Fusion to track the position, velocity, yaw and yaw rate of a bicycle maoving closely around a car.
Implemented value iteration to solve a Markov decision process describing a two-dimensional grid with an agent trying to reach from one corner to another. The algorithm was tested with a deterministic policy that maximizes the reward at each step, and a maximum entropy policy that maximizes entropy at each step.
Estimated the motion of a robot using classical computer vision techniques such as the point-n-perspective, local outlier factor, LMeds, and stereo vision with the KITTI dataset.
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