Tootfinder

This https://arxiv.org/abs/2505.09833 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Learning Rock Pushability on Rough Planetary Terrain
In the context of mobile navigation in unstructured environments, the predominant approach entails the avoidance of obstacles. The prevailing path planning algorithms are contingent upon deviating from the intended path for an indefinite duration and returning to the closest point on the route after the obstacle is left behind spatially. However, avoiding an obstacle on a path that will be used repeatedly by multiple agents can hinder long-term efficiency and lead to a lasting reliance on an ac…

A Reinforcement Learning Framework for Mobility Control of gNBs in Dynamic Radio Access Networks
Pedro Duarte, Andr\'e Coelho, Manuel Ricardo
https://arxiv.org/abs/2508.02960

A Reinforcement Learning Framework for Mobility Control of gNBs in Dynamic Radio Access Networks
The increasing complexity of wireless environments, characterized by user mobility and dynamic obstructions, poses challenges for the maintenance of Line-of-Sight (LoS) connectivity. Mobile base stations (gNBs) stand as a promising solution by physically relocating to restore or sustain LoS, thereby necessitating the development of intelligent algorithms for autonomous movement control. As part of the CONVERGE research project, which is developing an experimental chamber to integrate computer…

This https://arxiv.org/abs/2505.00237 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Future-Oriented Navigation: Dynamic Obstacle Avoidance with One-Shot Energy-Based Multimodal Motion Prediction
This paper proposes an integrated approach for the safe and efficient control of mobile robots in dynamic and uncertain environments. The approach consists of two key steps: one-shot multimodal motion prediction to anticipate motions of dynamic obstacles and model predictive control to incorporate these predictions into the motion planning process. Motion prediction is driven by an energy-based neural network that generates high-resolution, multi-step predictions in a single operation. The pred…

It’s frustrating and disappointing to see people, including governments, police, and others, misrepresent GrapheneOS and even Pixel phones.
https://grapheneos.social/@GrapheneOS/114784469162979608

TRIDENT -- A Three-Tier Privacy-Preserving Propaganda Detection Model in Mobile Networks using Transformers, Adversarial Learning, and Differential Privacy
Al Nahian Bin Emran, Dhiman Goswami, Md Hasan Ullah Sadi, Sanchari Das
https://arxiv.org/abs/2506.05421

TRIDENT -- A Three-Tier Privacy-Preserving Propaganda Detection Model in Mobile Networks using Transformers, Adversarial Learning, and Differential Privacy
The proliferation of propaganda on mobile platforms raises critical concerns around detection accuracy and user privacy. To address this, we propose TRIDENT - a three-tier propaganda detection model implementing transformers, adversarial learning, and differential privacy which integrates syntactic obfuscation and label perturbation to mitigate privacy leakage while maintaining propaganda detection accuracy. TRIDENT leverages multilingual back-translation to introduce semantic variance, charact…

Socially-aware Object Transportation by a Mobile Manipulator in Static Planar Environments with Obstacles
Caio C. G. Ribeiro, Leonardo R. D. Paes, Douglas G. Macharet
https://arxiv.org/abs/2506.13953

Socially-aware Object Transportation by a Mobile Manipulator in Static Planar Environments with Obstacles
Socially-aware robotic navigation is essential in environments where humans and robots coexist, ensuring both safety and comfort. However, most existing approaches have been primarily developed for mobile robots, leaving a significant gap in research that addresses the unique challenges posed by mobile manipulators. In this paper, we tackle the challenge of navigating a robotic mobile manipulator, carrying a non-negligible load, within a static human-populated environment while adhering to soci…

A Step-by-step Guide on Nonlinear Model Predictive Control for Safe Mobile Robot Navigation
Dennis Benders, Laura Ferranti, Johannes K\"ohler
https://arxiv.org/abs/2507.17856

A Step-by-step Guide on Nonlinear Model Predictive Control for Safe Mobile Robot Navigation
Designing a Model Predictive Control (MPC) scheme that enables a mobile robot to safely navigate through an obstacle-filled environment is a complicated yet essential task in robotics. In this technical report, safety refers to ensuring that the robot respects state and input constraints while avoiding collisions with obstacles despite the presence of disturbances and measurement noise. This report offers a step-by-step approach to implementing Nonlinear Model Predictive Control (NMPC) schemes …

Real-Time Obstacle Avoidance Algorithms for Unmanned Aerial and Ground Vehicles
Jingwen Wei
https://arxiv.org/abs/2506.20311 https://…

Real-Time Obstacle Avoidance Algorithms for Unmanned Aerial and Ground Vehicles
The growing use of mobile robots in sectors such as automotive, agriculture, and rescue operations reflects progress in robotics and autonomy. In unmanned aerial vehicles (UAVs), most research emphasizes visual SLAM, sensor fusion, and path planning. However, applying UAVs to search and rescue missions in disaster zones remains underexplored, especially for autonomous navigation. This report develops methods for real-time and secure UAV maneuvering in complex 3D environments, crucial during f…

Imitation Learning for Obstacle Avoidance Using End-to-End CNN-Based Sensor Fusion
Lamiaa H. Zain, Hossam H. Ammar, Raafat E. Shalaby
https://arxiv.org/abs/2507.08112

Imitation Learning for Obstacle Avoidance Using End-to-End CNN-Based Sensor Fusion
Obstacle avoidance is crucial for mobile robots' navigation in both known and unknown environments. This research designs, trains, and tests two custom Convolutional Neural Networks (CNNs), using color and depth images from a depth camera as inputs. Both networks adopt sensor fusion to produce an output: the mobile robot's angular velocity, which serves as the robot's steering command. A newly obtained visual dataset for navigation was collected in diverse environments with varying lighting con…

Corridor-based Adaptive Control Barrier and Lyapunov Functions for Safe Mobile Robot Navigation
Nicholas Mohammad, Nicola Bezzo
https://arxiv.org/abs/2507.14700

Corridor-based Adaptive Control Barrier and Lyapunov Functions for Safe Mobile Robot Navigation
Safe navigation in unknown and cluttered environments remains a challenging problem in robotics. Model Predictive Contour Control (MPCC) has shown promise for performant obstacle avoidance by enabling precise and agile trajectory tracking, however, existing methods lack formal safety assurances. To address this issue, we propose a general Control Lyapunov Function (CLF) and Control Barrier Function (CBF) enabled MPCC framework that enforces safety constraints derived from a free-space corridor …