Posts by Collection

Skid-steered Robots : Learning for system identification & control

Exploration of contemporary machine learning methods for improved motion planning and controls for skid-steered wheeled mobile robots.

Add something here

Vehicle Stabilization on a bumpy terrain

Short description of portfolio item number 1

Articulated Robotics

Short description of portfolio item number 1

Containerization for Systems Integration

Short description of portfolio item number 1

Vehicle Stabilization on a Bumpy Terrain

Short description of portfolio item number 1

Articulated Robotics

Short description of portfolio item number 1

Containerization for Systems Integration

Short description of portfolio item number 1

Online identification of skidding modes with interactive multiple model estimation

Skid-steered wheel mobile robots (SSWMRs) operate in a variety of outdoor environments exhibiting motion behaviors dominated by the effects of complex wheel-ground interactions. Characterizing these interactions is crucial both from the immediate robot autonomy perspective (for motion prediction and control) as well as a long-term predictive maintenance and diagnostics perspective. An ideal solution entails capturing precise state measurements for decisions and controls, which is considerably difficult, especially in increasingly unstructured outdoor regimes of operations for these robots. In this milieu, a framework to identify pre-determined discrete modes of operation can considerably simplify the motion model identification process. To this end, we propose an interactive multiple model (IMM) based filtering framework to probabilistically identify predefined robot operation modes that could arise due to traversal in different terrains or loss of wheel traction.

Deep Reinforcement Learning For Simultaneous Path Planning and Stabilization of Offroad Vehicles

Published in 2021 NDIA GROUND VEHICLE SYSTEM ENGINEERING AND TECHNOLOGY SYMPOSIUM, 2021

Ameya Salvi, Jake Buzhardt, Phanindra Tallapragada, Venkat Krovi, Mark Brudnak, Jonathon M. Smereka

Virtual Evaluation of Deep Learning Techniques for Vision-Based Trajectory Tracking

Published in SAE International Journal of Advances and Current Practices in Mobility, 2022

Ameya Salvi, Jake Buzhardt, Phanindra Tallapragada, Venkat Krovi, Jonathon M. Smereka, Mark Brudnak

Stabilization of vertical motion of a vehicle on bumpy terrain using deep reinforcement learning

Published in 2022 Modeling, Estimation and Controls Conference, 2022

Ameya Salvi, John Coleman, Jake Buzhardt, Venkat Krovi, Phanindra Tallapragada.

Containerization Approach for High-Fidelity Terramechanic Simulations

Published in SAE Technical Paper, 2023

Sanskruti Deepak Jadhav, Ameya Salvi, Krishna Chaitanya Kosaraju, Jonathon Smereka, Mark Brudnak, Venkat N Krovi, David Gorsich.

Reinforcement Learning Control of a Reconfigurable Planar Cable Driven Parallel Manipulator

Published in 2023 International Conference on Robotics and Automation (ICRA), 2023

Adhiti Raman, Ameya Salvi, Matthias Schmid, Venkat Krovi.

Rough Terrain Path Tracking of an Ackermann Steered Platform using Hybrid Deep Reinforcement Learning

Published in 2024 IEEE/ASME International Conference on Advanced Intelligent Mechatronics(Accepted), 2024

Dhruv Mehta, Ameya Salvi, Venkat Krovi

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.