Autonomous Mapping:
​
-
Presented a high-level navigation algorithm on a 2-wheeled robot with a LiDAR scanner using RTAB-Map and ROS. GitHub YouTube
​
-
Created a gazebo world with unique identifiers to prevent RTAB-Map loop closures.
​
-
Tuned the move_base path planner to determine safe points for waypoint navigation.
​
-
Successfully solved the autonomous mapping problem and avoided obstacles for an extended time.
​
​
​
​
​
Waypoint Tracking:
​
-
Applied PID control to the turtlesim robot for waypoint tracking with the help of Python scripts in ROS. GitHub YouTube.
​
​
​
​
​
​
​
​
Balance Cart-pole system:
-
Balanced the pole of a cart pole system in the gazebo simulator with an LQR controller. GitHub YouTube
​
​
​
​
![robotnavigation.png](https://static.wixstatic.com/media/ff84b4_b940b8baa1ca41f2beb9f6ee15294012~mv2.png/v1/fill/w_419,h_292,al_c,q_85,usm_0.66_1.00_0.01,enc_auto/robotnavigation.png)
Simulation Result from Rviz
![waypoint_tracking_SES598.png](https://static.wixstatic.com/media/ff84b4_bfd217b51c074d709054bdf24d791532~mv2.png/v1/crop/x_127,y_18,w_400,h_373/fill/w_174,h_162,al_c,q_85,usm_0.66_1.00_0.01,enc_auto/waypoint_tracking_SES598.png)
Simulation Result turtlesim window
![lqr_cartpole.png](https://static.wixstatic.com/media/ff84b4_aad3540943494bf1aa9312c9eab1b9e6~mv2.png/v1/fill/w_349,h_294,al_c,q_85,usm_0.66_1.00_0.01,enc_auto/lqr_cartpole.png)
Simulation Result from gazebo
PROJECTS
Autonomous Exploration Systems, ASU
​
-
Presented a supervisor agent that employs deep reinforcement learning to increase its speed on a multi-lane freeway, enhancing driver assistance systems. Report
​
-
Trained the RL agent with different reward structures in Python to observe the performance of the agent in the pygame window. GitHub
​
-
Created MATLAB scripts to plot the variation in average speed.
![With hard braking optimized.gif](https://static.wixstatic.com/media/ff84b4_102afe1981ee48ee93f39520735b2e0e~mv2.gif)
Simulation Result from pygame window
Reinforcement Learning for ADAS, ASU
Simulation of Robust Adaptive Control Algorithms, ASU
​​
-
Implemented the gradient and least square parameter estimation algorithms (PEA) to the provided plant models.
​
-
Designed indirect adaptive controllers for the estimated plant models to achieve pole placement and LQG control objectives and incorporated dead-zone modification to improve robustness.
![eee686_simulinkdiag.png](https://static.wixstatic.com/media/ff84b4_f3b4d668bb9f42b69ac8f4f127c1f961~mv2.png/v1/crop/x_12,y_0,w_1069,h_427/fill/w_116,h_47,al_c,q_85,usm_0.66_1.00_0.01,blur_2,enc_auto/eee686_simulinkdiag.png)
Simulink Block Diagram
![eee686_outputandinput.png](https://static.wixstatic.com/media/ff84b4_80f4743403af4d42beb0150ee49c607f~mv2.png/v1/fill/w_118,h_61,al_c,q_85,usm_0.66_1.00_0.01,blur_2,enc_auto/eee686_outputandinput.png)
Plot of Output and Input
![eee686_estimatedpara.png](https://static.wixstatic.com/media/ff84b4_b1d4eb79a3c94257a66032de5d0beae5~mv2.png/v1/fill/w_115,h_61,al_c,q_85,usm_0.66_1.00_0.01,blur_2,enc_auto/eee686_estimatedpara.png)
Plot of Estimated Parameters
Simulation of Robust Adaptive Control Algorithms, ASU
​​
-
Proposed to stabilize an autonomous bike
by implementing both a state feedback control and a
sliding mode control for the steering angles of the bicycle. Report
​
-
Analyzed the stability of the vehicle with
our designed controllers by performing simulations in
Mathworks Simulink software. GitHub
![SMC_states_dist.png](https://static.wixstatic.com/media/ff84b4_dbc8cdb88c964a9dbe78d5fa0f47fc01~mv2.png/v1/fill/w_56,h_49,al_c,q_85,usm_0.66_1.00_0.01,blur_2,enc_auto/SMC_states_dist.png)
![SMC_states_wodist.png](https://static.wixstatic.com/media/ff84b4_d086e989ea434cfda3db85acf58d7860~mv2.png/v1/fill/w_191,h_235,al_c,q_85,usm_0.66_1.00_0.01,blur_3,enc_auto/SMC_states_wodist.png)
![SMC_control_dist.png](https://static.wixstatic.com/media/ff84b4_193c9aa266f84eb0a42c31f87619e197~mv2.png/v1/fill/w_56,h_48,al_c,q_85,usm_0.66_1.00_0.01,blur_2,enc_auto/SMC_control_dist.png)
![SMC_control_wodist.png](https://static.wixstatic.com/media/ff84b4_7f9ae4029b584133bae858279a1e4da9~mv2.png/v1/fill/w_192,h_235,al_c,q_85,usm_0.66_1.00_0.01,blur_3,enc_auto/SMC_control_wodist.png)