arXiv subjects

Veronica Vannini

Publications and source records attributed to Veronica Vannini.

5 recordsLinked to original sources

arXiv2026

Inertial Steering and Motion Prediction of Tracked Vehicles on Low-Adherence Surfaces

Tracked vehicles are widely used in off-road applications, but their conventional motion models often rely on no-slip assumptions that become inaccurate on low-adherence surfaces. Rather than treating inertial effects arising from slip as a disturbance to be minimized, this work investigates how inertial effects arising from a moving mass can be deliberately exploited for steering on low-friction surfaces. A dynamic model for an articulated tracked vehicle with a passive trailer is developed, allowing inertial effects from trailer motion to be captured. The approach is experimentally evaluated on ice, where our dynamic model outperforms the kinematic baseline. We also propose a mixed dynamic-kinematic model to adapt predictions to both surface and situation by exploiting predictions from both methods. Finally, we demonstrate inertial steering on a mono-track vehicle without a conventional steering mechanism, showing that controlled movements of an internal mass can generate turning motion on ice. The resulting behavior can be predicted using the proposed dynamic model by representing the internal mass as a frictionless trailer, revealing the analogous dynamics between the two configurations. These results demonstrate the feasibility of inertial steering as an alternative steering mechanism for tracked vehicles operating in low-adherence environments.

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arXiv2026

Steering Through Contact: A Finite-Support Motion Model for Single-Track Center-Articulated Robots

Trajectory planning and control in field robotics rely on predicting how propulsion and steering affect vehicle motion when contact points undergo slip. For articulated vehicles, the point-contact kinematic model (PCK) accounts for the linkage geometry but neglects the rotational resistance distributed along the contacts. We propose a finite-support quadratic model (FSQ) for single-track, center-articulated vehicles that incorporates this resistance through a quasi-static balance of lateral slip. Our approach generalizes the standard PCK formulation by relaxing the contact-point assumption. An exact reduction of the quadratic slip cost to contact moments gives a compact closed-form solution for real-time prediction of lateral velocity and yaw rate. We evaluate the proposed method in real-world experiments across asphalt, grass, ice, and mixed routes, using more than 7 km of data. For five-second predictions, FSQ reduces the weighted median translation and yaw errors by 53.5% and 68.9%, respectively, relative to PCK.

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arXiv2026

One year in a forest: Analyzing the challenges of autonomous navigation in subarctic environments

Subarctic regions have the potential to see increased deployment of autonomous robots in applications including forestry, mining, and environmental monitoring. In these conditions, an autonomous system's reliance on GNSS or cloud computing is precarious due to dense tree canopies and atmospheric attenuation, necessitating onboard sensing and data processing. However, established exteroceptive modalities, including cameras, lidars, and radars, are typically evaluated in structured urban settings or in environments that lack significant seasonal variations. To address this, we present a field report on a year-long deployment of a mobile robot in a subarctic boreal forest. We evaluate 64 km of data using nine odometry, localization, and mapping methods and assess their performance across seasonal changes. The performed experiments suggest that the environment changes significantly hinder the performance of state-of-the-art techniques, which show increased fragility when subject to conditions characterized by self-similar scenes or tall snowbanks. Additionally, complex Simultaneous Localization and Mapping (SLAM) algorithms offer limited accuracy gains over a proprioceptive baseline while significantly increasing system fragility. Furthermore, by correlating the position drift with features and confidence weight distribution, we show that visual-based SLAM methods are particularly affected by the seasonal changes. Additionally, we investigate the task of cross-season localization in a prior map. While lidar-based methods successfully completed localization runs between seasons, radar and visual methods are prone to failure due to a few matching features between runs, even within the same season. Finally, we detail the challenges and lessons learned from this year-long trial, including a multi-season Teach and Repeat (T&R) evaluation using both radar and lidar-based pipelines.

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arXiv2026

Stretch-ICP: A Continuous-Trajectory Registration and Deskewing Algorithm in Scenarios of Aggressive Motions

Robust robotic autonomy remains challenging in complex environments, where loss of stability on uneven or slippery terrain can induce extreme accelerations and angular velocities. Such motions corrupt sensor measurements and degrade state estimation, motivating the need for improved algorithmic robustness. To investigate this issue, we introduce the Tumbling-Induced Gyroscope Saturation (TIGS) dataset, which consists of recordings from a mechanical lidar and an Inertial Measurement Unit (IMU) tumbling down a hill. The dataset contains angular speeds up to four times higher than those in similar datasets and is publicly available. We then propose two complementary methods to improve Simultaneous Localization And Mapping (SLAM) robustness and evaluate them on TIGS. First, Saturation-Aware Angular Velocity Estimation (SAAVE) estimates angular velocities when gyroscope measurements become saturated during aggressive motions, reducing angular speed estimation error by 83.4%. Second, Stretch-ICP, a novel registration and deskewing algorithm, enables reconstruction of smoother 6-Degrees Of Freedom (DOF) trajectories under aggressive motions compared to classical Iterative Closest Point (ICP). Stretch-ICP reduces linear and angular velocity errors by 95.2% and 94.8%, respectively, at scan boundaries. Together, these contributions improve the robustness and consistency of lidar-inertial state estimation under aggressive motions.

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arXiv2025

ASAP-MO:Advanced Situational Awareness and Perception for Mission-critical Operations

Deploying robotic missions can be challenging due to the complexity of controlling robots with multiple degrees of freedom, fusing diverse sensory inputs, and managing communication delays and interferences. In nuclear inspection, robots can be crucial in assessing environments where human presence is limited, requiring precise teleoperation and coordination. Teleoperation requires extensive training, as operators must process multiple outputs while ensuring safe interaction with critical assets. These challenges are amplified when operating a fleet of heterogeneous robots across multiple environments, as each robot may have distinct control interfaces, sensory systems, and operational constraints. Efficient coordination in such settings remains an open problem. This paper presents a field report on how we integrated robot fleet capabilities - including mapping, localization, and telecommunication - toward a joint mission. We simulated a nuclear inspection scenario for exposed areas, using lights to represent a radiation source. We deployed two Unmanned Ground Vehicles (UGVs) tasked with mapping indoor and outdoor environments while remotely controlled from a single base station. Despite having distinct operational goals, the robots produced a unified map output, demonstrating the feasibility of coordinated multi-robot missions. Our results highlight key operational challenges and provide insights into improving adaptability and situational awareness in remote robotic deployments.

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