101 citations · 419 across the 30 of their papers we have counts for
22 papers · 1 filter
D-SafeMPC: Diffusion-Driven Safe Model Predictive Control with Discrete-Time Control Barrier Functions
Erdi Sayar, Ersin Daş, Joel W. Burdick +2
A key limitation on the use of diffusion models in robotic planning is their inability to inherently enforce safety or dynamical constraints, which often results in physically infe…
Exp2VLA: Enabling Vision-Language-Action for Drone Navigation from Expert Demonstrations
Van Huyen Dang, Kabilesh Rajendran, Erdi Sayar +1
Vision-language-action (VLA) models open a new path toward intuitive robot control by directly linking perception, language, and action in a single end-to-end framework. Yet for UA…
SeaVis: Modeling and Control of a Remotely Operated Towed Vehicle for Seabed Visualization and Mapping
Abdelhakim Amer, Aske Alstrup, Frederik Rasmussen +3
High-resolution seafloor mapping necessitates stable and precise positioning for underwater robots. This paper introduces a novel mathematical model for SeaVis remotely operated to…
Fuzzy Logic Theory-based Adaptive Reward Shaping for Robust Reinforcement Learning (FARS)
Hürkan Şahin, Van Huyen Dang, Erdi Sayar +2
Reinforcement learning (RL) often struggles in real-world tasks with high-dimensional state spaces and long horizons, where sparse or fixed rewards severely slow down exploration a…
GIANT - Global Path Integration and Attentive Graph Networks for Multi-Agent Trajectory Planning
Jonas le Fevre Sejersen, Toyotaro Suzumura, Erdal Kayacan
This paper presents a novel approach to multi-robot collision avoidance that integrates global path planning with local navigation strategies, utilizing attentive graph neural netw…
REACT: Real-time Entanglement-Aware Coverage Path Planning for Tethered Underwater Vehicles
Abdelhakim Amer, Mohit Mehindratta, Yury Brodskiy +2
Inspection of underwater structures with tethered underwater vehicles is often hindered by the risk of tether entanglement. We propose REACT (real-time entanglement-aware coverage…
Monocular visual simultaneous localization and mapping: (r)evolution from geometry to deep learning-based pipelines
Olaya Alvarez-Tunon, Yury Brodskiy, Erdal Kayacan
With the rise of deep learning, there is a fundamental change in visual SLAM algorithms toward developing different modules trained as end-to-end pipelines. However, regardless of…
Continual Learning for Robust Gate Detection under Dynamic Lighting in Autonomous Drone Racing
Zhongzheng Qiao, Xuan Huy Pham, Savitha Ramasamy +3
In autonomous and mobile robotics, a principal challenge is resilient real-time environmental perception, particularly in situations characterized by unknown and dynamic elements,…
Visual Tracking Nonlinear Model Predictive Control Method for Autonomous Wind Turbine Inspection
Abdelhakim Amer, Mohit Mehndiratta, Jonas le Fevre Sejersen +2
Automated visual inspection of on-and offshore wind turbines using aerial robots provides several benefits, namely, a safe working environment by circumventing the need for workers…
UNav-Sim: A Visually Realistic Underwater Robotics Simulator and Synthetic Data-generation Framework
Abdelhakim Amer, Olaya Álvarez-Tuñón, Halil Ibrahim Ugurlu +3
Underwater robotic surveys can be costly due to the complex working environment and the need for various sensor modalities. While underwater simulators are essential, many existing…
PencilNet: Zero-Shot Sim-to-Real Transfer Learning for Robust Gate Perception in Autonomous Drone Racing
Huy Xuan Pham, Andriy Sarabakha, Mykola Odnoshyvkin +1
In autonomous and mobile robotics, one of the main challenges is the robust on-the-fly perception of the environment, which is often unknown and dynamic, like in autonomous drone r…
Event-based Navigation for Autonomous Drone Racing with Sparse Gated Recurrent Network
Kristoffer Fogh Andersen, Huy Xuan Pham, Halil Ibrahim Ugurlu +1
Event-based vision has already revolutionized the perception task for robots by promising faster response, lower energy consumption, and lower bandwidth without introducing motion…
Real-Time Volumetric-Semantic Exploration and Mapping: An Uncertainty-Aware Approach
Rui Pimentel de Figueiredo, Jonas le Fevre Sejersen, Jakob Grimm Hansen +2
In this work we propose a holistic framework for autonomous aerial inspection tasks, using semantically-aware, yet, computationally efficient planning and mapping algorithms. The s…
Safe Vessel Navigation Visually Aided by Autonomous Unmanned Aerial Vehicles in Congested Harbors and Waterways
Jonas le Fevre Sejersen, Rui Pimentel de Figueiredo, Erdal Kayacan
In the maritime sector, safe vessel navigation is of great importance, particularly in congested harbors and waterways. The focus of this work is to estimate the distance between a…
On the Advantages of Multiple Stereo Vision Camera Designs for Autonomous Drone Navigation
Rui Pimentel de Figueiredo, Jakob Grimm Hansen, Jonas Le Fevre +2
In this work we showcase the design and assessment of the performance of a multi-camera UAV, when coupled with state-of-the-art planning and mapping algorithms for autonomous navig…
Adaptive Neuro-Fuzzy Control of a Spherical Rolling Robot Using Sliding-Mode-Control-Theory-Based Online Learning Algorithm
Erkan Kayacan, Erdal Kayacan, Herman Ramon +1
As a model is only an abstraction of the real system, unmodeled dynamics, parameter variations, and disturbances can result in poor performance of a conventional controller based o…
Towards Agrobots: Trajectory Control of an Autonomous Tractor Using Type-2 Fuzzy Logic Controllers
Erdal Kayacan, Erkan Kayacan, Herman Ramon +2
Provision of some autonomous functions to an agricultural vehicle would lighten the job of the operator but in doing so, the accuracy should not be lost to still obtain an optimal…
Autonomous Aerial Cinematography In Unstructured Environments With Learned Artistic Decision-Making
Rogerio Bonatti, Wenshan Wang, Cherie Ho +6
Aerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely piloting a drone wh…
End-to-End Motion Planning of Quadrotors Using Deep Reinforcement Learning
Efe Camci, Erdal Kayacan
In this work, a novel, end-to-end motion planning method is proposed for quadrotor navigation in cluttered environments. The proposed method circumvents the explicit sensing-recons…
Online Deep Learning for Improved Trajectory Tracking of Unmanned Aerial Vehicles Using Expert Knowledge
Andriy Sarabakha, Erdal Kayacan
This work presents an online learning-based control method for improved trajectory tracking of unmanned aerial vehicles using both deep learning and expert knowledge. The proposed…
Can a Robot Become a Movie Director? Learning Artistic Principles for Aerial Cinematography
Mirko Gschwindt, Efe Camci, Rogerio Bonatti +3
Aerial filming is constantly gaining importance due to the recent advances in drone technology. It invites many intriguing, unsolved problems at the intersection of aesthetical and…
Knowledge Transfer Between Robots with Similar Dynamics for High-Accuracy Impromptu Trajectory Tracking
Siqi Zhou, Andriy Sarabakha, Erdal Kayacan +2
In this paper, we propose an online learning approach that enables the inverse dynamics model learned for a source robot to be transferred to a target robot (e.g., from one quadrot…