1 citations · 2 across the 7 of their papers we have counts for
7 papers
The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy
Mihir Dharmadhikari, Nikhil Khedekar, Mihir Kulkarni +7
We introduce and open-source the Unified Autonomy Stack, a system-level solution that enables resilient autonomy across diverse aerial and ground robot morphologies. The architectu…
DigiForest: Digital Analytics and Robotics for Sustainable Forestry
Marco Camurri, Enrico Tomelleri, Matías Mattamala +18
Covering one third of Earth's land surface, forests are vital to global biodiversity, climate regulation, and human well-being. In Europe, forests and woodlands reach approximately…
Neural NMPC through Signed Distance Field Encoding for Collision Avoidance
Martin Jacquet, Marvin Harms, Kostas Alexis
This paper introduces a neural Nonlinear Model Predictive Control (NMPC) framework for mapless, collision-free navigation in unknown environments with Aerial Robots, using onboard…
Embedded Safe Reactive Navigation for Multirotors Systems using Control Barrier Functions
Nazar Misyats, Marvin Harms, Morten Nissov +2
Aiming to promote the wide adoption of safety filters for autonomous aerial robots, this paper presents a safe control architecture designed for seamless integration into widely us…
Safe Quadrotor Navigation using Composite Control Barrier Functions
Marvin Harms, Martin Jacquet, Kostas Alexis
This paper introduces a safety filter to ensure collision avoidance for multirotor aerial robots. The proposed formalism leverages a single Composite Control Barrier Function from…
Neural Control Barrier Functions for Safe Navigation
Marvin Harms, Mihir Kulkarni, Nikhil Khedekar +2
Autonomous robot navigation can be particularly demanding, especially when the surrounding environment is not known and safety of the robot is crucial. This work relates to the syn…
N-MPC for Deep Neural Network-Based Collision Avoidance exploiting Depth Images
Martin Jacquet, Kostas Alexis
This paper introduces a Nonlinear Model Predictive Control (N-MPC) framework exploiting a Deep Neural Network for processing onboard-captured depth images for collision avoidance i…