most citedAutonomous Ground Navigation in Highly Constrained Spaces: Lessons learned from The BARN Challenge at ICRA 2022

1 citations · 1 across the 2 of their papers we have counts for

collaborators

7 papers

cs.RO20241 cited

Robust Online Epistemic Replanning of Multi-Robot Missions

Lauren Bramblett, Branko Miloradovic, Patrick Sherman +2

As Multi-Robot Systems (MRS) become more affordable and computing capabilities grow, they provide significant advantages for complex applications such as environmental monitoring,…

cs.RO2024

A GP-based Robust Motion Planning Framework for Agile Autonomous Robot Navigation and Recovery in Unknown Environments

Nicholas Mohammad, Jacob Higgins, Nicola Bezzo

For autonomous mobile robots, uncertainties in the environment and system model can lead to failure in the motion planning pipeline, resulting in potential collisions. In order to…

cs.RO2023

Epistemic Planning for Heterogeneous Robotic Systems

Lauren Bramblett, Nicola Bezzo

In applications such as search and rescue or disaster relief, heterogeneous multi-robot systems (MRS) can provide significant advantages for complex objectives that require a suite…

cs.RO2023

A Decision Tree-based Monitoring and Recovery Framework for Autonomous Robots with Decision Uncertainties

Rahul Peddi, Nicola Bezzo

Autonomous mobile robots (AMR) operating in the real world often need to make critical decisions that directly impact their own safety and the safety of their surroundings. Learnin…

cs.RO2023

A Model Predictive Path Integral Method for Fast, Proactive, and Uncertainty-Aware UAV Planning in Cluttered Environments

Jacob Higgins, Nicholas Mohammad, Nicola Bezzo

Current motion planning approaches for autonomous mobile robots often assume that the low level controller of the system is able to track the planned motion with very high accuracy…

eess.SY20231 cited

RSSI-based Localization with Adaptive Noise Covariance Estimation for Resilient Multi-Agent Formations

Paul J Bonczek, Nicola Bezzo

Typical cooperative multi-agent systems (MASs) exchange information to coordinate their motion in proximity-based control consensus schemes to complete a common objective. However,…