activity
20222025
most citedTrust-based Rate-Tunable Control Barrier Functions for Non-Cooperative Multi-Agent Systems

2 citations · 7 across the 8 of their papers we have counts for

collaborators
Showing cs.ROShow all

9 papers · 1 filter

cs.RO2025

GPU-Accelerated Barrier-Rate Guided MPPI Control for Tractor-Trailer Systems

Keyvan Majd, Hardik Parwana, Bardh Hoxha +3

Articulated vehicles such as tractor-trailers, yard trucks, and similar platforms must often reverse and maneuver in cluttered spaces where pedestrians are present. We present how…

cs.RO2025

BR-MPPI: Barrier Rate guided MPPI for Enforcing Multiple Inequality Constraints with Learned Signed Distance Field

Hardik Parwana, Taekyung Kim, Kehan Long +4

Model Predictive Path Integral (MPPI) controller is used to solve unconstrained optimal control problems and Control Barrier Function (CBF) is a tool to impose strict inequality co…

cs.RO20251 cited

MRTA-Sim: A Modular Simulator for Multi-Robot Allocation, Planning, and Control in Open-World Environments

Victoria Marie Tuck, Hardik Parwana, Pei-Wei Chen +5

This paper introduces MRTA-Sim, a Python/ROS2/Gazebo simulator for testing approaches to Multi-Robot Task Allocation (MRTA) problems on simulated robots in complex, indoor environm…

cs.RO2025

Enabling Safety for Aerial Robots: Planning and Control Architectures

Kaleb Ben Naveed, Devansh R. Agrawal, Daniel M. Cherenson +6

Ensuring safe autonomy is crucial for deploying aerial robots in real-world applications. However, safety is a multifaceted challenge that must be addressed from multiple perspecti…

cs.RO2024

Risk-aware MPPI for Stochastic Hybrid Systems

Hardik Parwana, Mitchell Black, Bardh Hoxha +4

Path Planning for stochastic hybrid systems presents a unique challenge of predicting distributions of future states subject to a state-dependent dynamics switching function. In th…

cs.RO2024

Neural Configuration Distance Function for Continuum Robot Control

Kehan Long, Hardik Parwana, Georgios Fainekos +3

This paper presents a novel method for modeling the shape of a continuum robot as a Neural Configuration Euclidean Distance Function (N-CEDF). By learning separate distance fields…