2 citations · 2 across the 6 of their papers we have counts for
8 papers
Traversability-Aware Cooperative Path Planning for Human-UGV Casualty Evacuation
Kristian Dalland, Prithvi Poddar, Souma Chowdhury +2
Heterogeneous multi-robot path planning is a well-studied problem in which agents with disparate kinematic and dynamic models must coordinate to achieve shared objectives. These fo…
HOPHY: A Hierarchical Hypergraph Representation for Off-Road Path and Mission Planning
Pranay Meshram, Charuvahan Adhivarahan, Prithvi Poddar +4
Mission-level autonomy for disaster response, search and rescue, and tactical UGV operations requires repeated path and mission planning as terrain conditions, agent types, and obj…
Topology-Aware Reinforcement Learning over Graphs for Resilient Power Distribution Networks
Roshni Anna Jacob, Prithvi Poddar, Jaidev Goel +3
Extreme weather events and cyberattacks can cause component failures and disrupt the operation of power distribution networks (DNs), during which reconfiguration and load shedding…
Automated Generation of Diverse Courses of Actions for Multi-Agent Operations using Binary Optimization and Graph Learning
Prithvi Poddar, Ehsan Tarkesh Esfahani, Karthik Dantu +1
Operations in disaster response, search \& rescue, and military missions that involve multiple agents demand automated processes to support the planning of the courses of action (C…
A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobility
Prithvi Poddar, Steve Paul, Souma Chowdhury
The advent of Urban Air Mobility (UAM) presents the scope for a transformative shift in the domain of urban transportation. However, its widespread adoption and economic viability…
RE-MOVE: An Adaptive Policy Design for Robotic Navigation Tasks in Dynamic Environments via Language-Based Feedback
Souradip Chakraborty, Kasun Weerakoon, Prithvi Poddar +6
Reinforcement learning-based policies for continuous control robotic navigation tasks often fail to adapt to changes in the environment during real-time deployment, which may resul…
A Model-free Deep Reinforcement Learning Approach To Maneuver A Quadrotor Despite Single Rotor Failure
Paras Sharma, Prithvi Poddar, P. B. Sujit
Ability to recover from faults and continue mission is desirable for many quadrotor applications. The quadrotor's rotor may fail while performing a mission and it is essential to d…
Multi-Agent Deep Reinforcement Learning For Persistent Monitoring With Sensing, Communication, and Localization Constraints
Manav Mishra, Prithvi Poddar, Rajat Agarwal +3
Determining multi-robot motion policies for persistently monitoring a region with limited sensing, communication, and localization constraints in non-GPS environments is a challeng…