19 citations · 50 across the 23 of their papers we have counts for
26 papers
Higher Order Reasoning for Collaborative Communicationless Mobile Robot Operations
Jonathan Reasoner, Nicola Bezzo
In communicationless environments, multi-robot systems must operate without the constant information exchange that many coordination strategies typically assume. This paper present…
Corridor-based Adaptive Control Barrier and Lyapunov Functions for Safe Mobile Robot Navigation
Nicholas Mohammad, Nicola Bezzo
Safe navigation in unknown and cluttered environments remains a challenging problem in robotics. Model Predictive Contour Control (MPCC) has shown promise for performant obstacle a…
A Schwarz-Christoffel Mapping-based Framework for Sim-to-Real Transfer in Autonomous Robot Operations
Shijie Gao, Nicola Bezzo
Despite the remarkable acceleration of robotic development through advanced simulation technology, robotic applications are often subject to performance reductions in real-world de…
Soft Actor-Critic-based Control Barrier Adaptation for Robust Autonomous Navigation in Unknown Environments
Nicholas Mohammad, Nicola Bezzo
Motion planning failures during autonomous navigation often occur when safety constraints are either too conservative, leading to deadlocks, or too liberal, resulting in collisions…
Implicit Coordination using Active Epistemic Inference for Multi-Robot Systems
Lauren Bramblett, Jonathan Reasoner, Nicola Bezzo
A Multi-robot system (MRS) provides significant advantages for intricate tasks such as environmental monitoring, underwater inspections, and space missions. However, addressing pot…
Using High-Level Patterns to Estimate How Humans Predict a Robot will Behave
Sagar Parekh, Lauren Bramblett, Nicola Bezzo +1
Humans interacting with robots often form predictions of what the robot will do next. For instance, based on the recent behavior of an autonomous car, a nearby human driver might p…
Take Your Best Shot: Sampling-Based Planning for Autonomous Photography
Shijie Gao, Lauren Bramblett, Nicola Bezzo
Autonomous mobile robots (AMRs) equipped with high-quality cameras have revolutionized the field of inspections by providing efficient and cost-effective means of conducting survey…
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,…
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…
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…
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…
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…
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,…
Epistemic Prediction and Planning with Implicit Coordination for Multi-Robot Teams in Communication Restricted Environments
Lauren Bramblett, Shijie Gao, Nicola Bezzo
In communication restricted environments, a multi-robot system can be deployed to either: i) maintain constant communication but potentially sacrifice operational efficiency due to…
Learning Enabled Fast Planning and Control in Dynamic Environments with Intermittent Information
Matthew Cleaveland, Esen Yel, Yiannis Kantaros +2
This paper addresses a safe planning and control problem for mobile robots operating in communication- and sensor-limited dynamic environments. In this case the robots cannot sense…
Autonomous Ground Navigation in Highly Constrained Spaces: Lessons learned from The BARN Challenge at ICRA 2022
Xuesu Xiao, Zifan Xu, Zizhao Wang +14
The BARN (Benchmark Autonomous Robot Navigation) Challenge took place at the 2022 IEEE International Conference on Robotics and Automation (ICRA 2022) in Philadelphia, PA. The aim…
A Conformal Mapping-based Framework for Robot-to-Robot and Sim-to-Real Transfer Learning
Shijie Gao, Nicola Bezzo
This paper presents a novel method for transferring motion planning and control policies between a teacher and a learner robot. With this work, we propose to reduce the sim-to-real…
Interpretable Run-Time Prediction and Planning in Co-Robotic Environments
Rahul Peddi, Nicola Bezzo
Mobile robots are traditionally developed to be reactive and avoid collisions with surrounding humans, often moving in unnatural ways without following social protocols, forcing pe…
Detection and Inference of Randomness-based Behavior for Resilient Multi-vehicle Coordinated Operations
Paul J Bonczek, Nicola Bezzo
A resilient multi-vehicle system cooperatively performs tasks by exchanging information, detecting, and removing cyber attacks that have the intent of hijacking or diminishing perf…
Detection of Hidden Attacks on Cyber-Physical Systems from Serial Magnitude and Sign Randomness Inconsistencies
Paul J Bonczek, Nicola Bezzo
Stealthy false data injection attacks on cyber-physical systems (CPSs) introduce erroneous measurement information to on-board sensors with the purpose to degrade system performanc…
A Meta-Learning-based Trajectory Tracking Framework for UAVs under Degraded Conditions
Esen Yel, Nicola Bezzo
Due to changes in model dynamics or unexpected disturbances, an autonomous robotic system may experience unforeseen challenges during real-world operations which may affect its saf…
Model-based Randomness Monitor for Stealthy Sensor Attacks
Paul J. Bonczek, Shijie Gao, Nicola Bezzo
Malicious attacks on modern autonomous cyber-physical systems (CPSs) can leverage information about the system dynamics and noise characteristics to hide while hijacking the system…
Memoryless Cumulative Sign Detector for Stealthy CPS Sensor Attacks
Paul J. Bonczek, Nicola Bezzo
Stealthy false data injection attacks on cyber-physical systems introduce erroneous measurements onto sensors with the intent to degrade system performance. An intelligent attacker…
A Satisfiability Modulo Theory Approach to Secure State Reconstruction in Differentially Flat Systems Under Sensor Attacks
Yasser Shoukry, Pierluigi Nuzzo, Nicola Bezzo +3
We address the problem of estimating the state of a differentially flat system from measurements that may be corrupted by an adversarial attack. In cyber-physical systems, maliciou…