About Me

​This work demonstrates how differentiable guidance constraints can be effectively combined with diffusion-based motion planning to produce collision-free and dynamically feasible trajectories. By integrating ESDF-based guidance using nvidia's nvbloxx, the method achieves superior trajectory quality while maintaining computational efficiency.

This project holds promise for applications in autonomous navigation, robotic motion planning, and real-time trajectory optimization.

( To Know about the work , one can reach me through the contact Page)

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This work demonstartes a decentralized method for multi-target search problem using a swarm of unmanned aerial vehicles with the information available from the onboard sensors. The proposed method deals with three main objectives: Time optimized multi-target search, optimized payload drops and inter-UAV collision avoidance which is independent of the number of UAVs.

( To Know about the work , one can reach me through the contact Page)

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This video showcases the implementation of a low-level Pure Pursuit controller within the CARLA Simulator as part of an autonomous self-driving stack. The Pure Pursuit algorithm, a geometric path-tracking method, enables the vehicle to follow a predefined trajectory by dynamically adjusting its steering angle based on a lookahead point. Integrated within the CARLA environment, this implementation ensures smooth and accurate path following, enhancing the self-driving system’s ability to navigate varied road conditions, turns, and dynamic scenarios. The controller plays a crucial role in maintaining trajectory fidelity, stability, and real-time responsiveness, making it a fundamental component of autonomous vehicle navigation.

( To Know about the work , one can reach me through the contact Page)​

This video demonstrates the implementation of a low-level Pure Pursuit controller on a real hardware car, showcasing its effectiveness in trajectory tracking and autonomous navigation. The Pure Pursuit algorithm dynamically adjusts the vehicle’s steering based on a lookahead point, ensuring smooth and precise path-following in real-world conditions. This implementation validates the controller’s performance in handling varied road geometries, turns, and dynamic obstacles, bridging the gap between simulation-based testing and real-world deployment in autonomous driving systems.This was done at my time as an inntern at IIIT-Hyderabad at Robotics Research Center LAB.

( To Know about the work , one can reach me through the contact Page)​

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This video demonstrates the training and implementation of a Bang-Bang Reinforcement Learning (RL) controller that learns a Deep Q-Network (DQN) policy for obstacle avoidance using laser scan sensor data. Developed as part of my internship at Addverb, the model was trained in standard ROS simulation environments, where it learned to make discrete control decisions for safe and efficient navigation. By leveraging laser scan inputs, the controller dynamically adjusts movement to avoid obstacles while optimizing path efficiency. This project showcases the integration of RL-based decision-making in robotic navigation, paving the way for autonomous and adaptive motion planning in real-world applications.

This video demonstrates the training and implementation of a Bang-Bang Reinforcement Learning (RL) controller that learns a Deep Q-Network (DQN) policy for obstacle avoidance using laser scan sensor data. Developed as part of my internship at Addverb, the model was trained in standard ROS simulation environments, where it learned to make discrete control decisions for safe and efficient navigation. By leveraging laser scan inputs, the controller dynamically adjusts movement to avoid obstacles while optimizing path efficiency. This project showcases the integration of RL-based decision-making in robotic navigation, paving the way for autonomous and adaptive motion planning in real-world applications.