Vishrut Bohara

UAV Software Autonomy Intern - Nokia Bell Labs

Developed and implemented a cutting-edge hardware-in-the-loop simulation pipeline for drones in the Nvidia Isaac Sim simulator, enabling comprehensive testing of hardware and software functionality.

Optimized the performance of the VIO algorithm to improve the drone’s localization accuracy.

Software Engineer - Microsoft

I worked for the Credit Services team, which maintains and transforms all the transactional and customer data based on business requirements using Azure Services. My regular work duties involved data analysis and transformation, meeting with business teams, and developing services for my team to automate alerts and resource management. Apart from data transformation tasks, I automated the deployment process and created an E-mail system for daily alerts.

Firmware Intern - Nvidia Graphics

As a part of this internship, I worked with the design team, which develops system-on-chips (SoCs) for mobile devices and vehicles. I designed a bootloader to copy test executables from RAM to low-latency On-chip Memories. My work enabled the emulation platform to run multiple tests in a single simulation.

Adaptive Estimation-Based Safety-Critical Cruise Control of Vehicular Platoons

Under Review - Arxiv

This paper presents an adaptive estimation-based control design for adaptive cruise control (ACC) that swiftly estimates the states of the preceding vehicle while operating the autonomous vehicle within a safe region. Control Lyapunov Functions (CLFs) and Control Barrier Functions (CBFs) are employed to design a safety-critical controller that ensures safety despite potential estimation errors. The proposed estimator-controller framework is implemented in scenarios with and without vehicle-to-vehicle communication, demonstrating successful performance in maintaining platoon safety and stability. Additionally, physics engine-based simulations reinforce both the practical viability of the proposed control framework in real-world situations and the controller's adeptness at ensuring safety amidst realistic environmental conditions.

Multi-agent Reinforcement Learning for Collective Transport - NEST Lab

I trained Deep Q-Network and Deep Deterministic Policy Gradient models to achieve multi agent collective transport for environment with 2 obstacles, 4 obstacles and gate opening. I also explored different reward functions, communication schemes, and observation space to have better performance.

Robot Control Research Intern - Dr. Leena Vachhani

My research internship at IIT Bombay gave me a better insight into control systems and applications. My work was focused on control law for non-holonomic chained form systems and exploring different reaching laws for better performance. The research started with improvising the existing 3-dimensional control law to guarantee finite time convergence and using it to achieve autonomous car parking. We also explored the performance difference between linear and powered reaching law. After completing that, we worked on creating a generalized algorithm to calculate the convergence parameters for both the reaching law.