arXiv subjects

Roland Lenain

Publications and source records attributed to Roland Lenain.

9 recordsLinked to original sources

arXiv2026

Tracking the Ground: Online Lidar Identification of Robot-Induced Soil Deformation in Agricultural Environments

Agriculture faces many challenges, and robotic systems can play an important role in addressing them by improving the efficiency and sustainability of field operations. Among these challenges, preserving soil health is a critical concern, as vehicle-soil interactions can degrade the soil structure and produce unwanted surface deformation. A key step toward soil-aware robotics is to explicitly account for how vehicle traffic deforms the ground, yet soil state is typically not treated as a variable. We address this gap by proposing a framework to quantify traffic-induced soil deformation and estimate its evolution online from lidar observations. The method relies on a reduced-order parametric model that represents the soil behavior via physically interpretable parameters, yielding a continuously updated and observable representation of soil state. Experiments conducted in different soil conditions demonstrate the ability of the approach to capture deformation induced by the robot. By making soil response measurable and interpretable during operation, the proposed framework establishes a basis for soil-aware robotic operation, in which the estimated state can be exploited to adapt robotic behaviors in order to reduce soil degradation.

cs.RO↗

arXiv2026

When Obstacles Bend: Modeling Vegetation Deformation in the context of Field Robotics

Autonomous robots operating in natural environments must often interact with vegetation rather than simply avoid it. In this context, traversability is typically defined from the robot's perspective, by measuring how a specific platform responds when moving through the environment. While practical, this viewpoint entangles the assessment of the environment with the robot's own dynamics, making the resulting characterization difficult to transfer across different platforms. More importantly, it does not directly reflect the properties of the vegetation itself, which are the true source of interaction and potential damage in applications such as agriculture and environmental monitoring. To address this limitation, we propose to characterize vegetation through its intrinsic mechanical properties, independently of any specific robot. By combining deformation measurements with contact force data, we estimate the underlying mechanical parameters and reconstruct the vegetation's response to interaction. This enables vegetation-aware navigation based on intrinsic environmental properties rather than platform-dependent metrics.

cs.RO↗

arXiv2026

Instrumentation and field tests to evaluate a rollover risk estimator for mobile machinery with mobile tools

Agricultural machines that carry mobile tools or implements have an internal dynamic that makes them more prone to rollover risk than machines with fixed configurations. Previous research has led to the development of algorithms capable of predicting rollover risk in real time by estimating relevant metrics, primarily the LTR (Load Transfer Ratio), with a focus on vehicles with static configurations. This paper presents the adaptation of these algorithms for machines with mobile tools and describes the field tests, conducted on a self-propelled sprayer, to evaluate their effectiveness in predicting rollover. The tests also aimed to acquire the necessary data to build an accurate numerical model of the vehicle and produce high-quality simulated tests. This paper describes the chosen instrumentation, the test procedure, and the experimental design of these tests. These field tests include static tests and dynamic tests. Static tests determine vehicle parameters, while dynamic tests measure variables related to the machine displacement and wheel/ground forces in real time. The initial results demonstrate the algorithms' ability to capture variations in LTR, which express lateral and longitudinal rollover risk. The data collected through these tests will enable the vehicle displacements to be replayed by simulation, producing simulated rollover accidents and evaluating the effectiveness of the developed algorithms in accident prevention.

cs.CE↗

arXiv2026

CropCraft: A Procedural World Generator for Robotic Simulation of Agricultural Tasks

The adoption of agroecological practices in modern agriculture requires robotic systems capable of operating in highly diverse and complex field environments. Developing and evaluating such systems relies heavily on simulation, yet generating realistic and configurable 3D environments representative of agroecological diversity remains a major challenge. This paper presents CropCraft, an open-source procedural world generator built on Blender and Python, designed to produce 3D simulation environments tailored to agricultural robotics. CropCraft generates crop fields from a simple YAML configuration file, supporting a wide range of scenarios including intercropping, vineyards, and weed-infested fields. The tool includes a library of 3D plant models (crops, grasses, and weeds) at multiple growth stages, and uses stochastic placement algorithms to realistically reproduce the spatial variability observed in real fields. Generated worlds are directly importable into the Gazebo simulator and include ground-truth annotations for all placed elements, supporting both perception and navigation algorithm development. To demonstrate the practical utility of CropCraft, we apply it to the task of crop-weed semantic segmentation using deep learning. A dataset of 10,000 synthetic images of maize fields with varying weed densities, growth stages, and lighting conditions was generated and used to train several segmentation architectures. Models trained exclusively on synthetic data achieve a sim-to-real gap of approximately 10% mean Intersection over Union (mIoU) on real field images, outperforming previous state-of-the-art synthetic generation approaches. We further show that combining even a few real images with synthetic data improves generalization across domains, providing new insights into the effective use of synthetic data for agricultural perception tasks.

cs.CV↗

arXiv2026

A Predictive Control Strategy to Offset-Point Tracking for Agricultural Mobile Robots

Robots are increasingly being deployed in agriculture to support sustainable practices and improve productivity. They offer strong potential to enable precise, efficient, and environmentally friendly operations. However, most existing path-following controllers focus solely on the robot's center of motion and neglect the spatial footprint and dynamics of attached implements. In practice, implements such as mechanical weeders or spring-tine cultivators are often large, rigidly mounted, and directly interacting with crops and soil; ignoring their position can degrade tracking performance and increase the risk of crop damage. To address this limitation, we propose a closed-form predictive control strategy extending the approach introduced in [1]. The method is developed specifically for Ackermann-type agricultural vehicles and explicitly models the implement as a rigid offset point, while accounting for lateral slip and lever-arm effects. The approach is benchmarked against state-of-the-art baseline controllers, including a reactive geometric method, a reactive backstepping method, and a model-based predictive scheme. Real-world agricultural experiments with two different implements show that the proposed method reduces the median tracking error by 24% to 56%, and decreases peak errors during curvature transitions by up to 70%. These improvements translate into enhanced operational safety, particularly in scenarios where the implement operates in close proximity to crop rows.

cs.RO↗

arXiv2025

Feeling the Force: A Nuanced Physics-based Traversability Sensor for Navigation in Unstructured Vegetation

In many applications, robots are increasingly deployed in unstructured and natural environments where they encounter various types of vegetation. Vegetation presents unique challenges as a traversable obstacle, where the mechanical properties of the plants can influence whether a robot can safely collide with and overcome the obstacle. A more nuanced approach is required to assess the safety and traversability of these obstacles, as collisions can sometimes be safe and necessary for navigating through dense or unavoidable vegetation. This paper introduces a novel sensor designed to directly measure the applied forces exerted by vegetation on a robot: by directly capturing the push-back forces, our sensor provides a detailed understanding of the interactions between the robot and its surroundings. We demonstrate the sensor's effectiveness through experimental validations, showcasing its ability to measure subtle force variations. This force-based approach provides a quantifiable metric that can inform navigation decisions and serve as a foundation for developing future learning algorithms.

cs.RO↗

arXiv2025

From Theory to Practice: Identifying the Optimal Approach for Offset Point Tracking in the Context of Agricultural Robotics

Modern agriculture faces escalating challenges: increasing demand for food, labor shortages, and the urgent need to reduce environmental impact. Agricultural robotics has emerged as a promising response to these pressures, enabling the automation of precise and suitable field operations. In particular, robots equipped with implements for tasks such as weeding or sowing must interact delicately and accurately with the crops and soil. Unlike robots in other domains, these agricultural platforms typically use rigidly mounted implements, where the implement's position is more critical than the robot's center in determining task success. Yet, most control strategies in the literature focus on the vehicle body, often neglecting the acctual working point of the system. This is particularly important when considering new agriculture practices where crops row are not necessary straights. This paper presents a predictive control strategy targeting the implement's reference point. The method improves tracking performance by anticipating the motion of the implement, which, due to its offset from the vehicle's center of rotation, is prone to overshooting during turns if not properly accounted for.

cs.RO↗

arXiv2025

A Novel Control Strategy for Offset Points Tracking in the Context of Agricultural Robotics

In this paper, we present a novel method to control a rigidly connected location on the vehicle, such as a point on the implement in case of agricultural tasks. Agricultural robots are transforming modern farming by enabling precise and efficient operations, replacing humans in arduous tasks while reducing the use of chemicals. Traditionnaly, path_following algorithms are designed to guide the vehicle's center along a predefined trajetory. However, since the actual agronomic task is performed by the implement, it is essential to control a specific point on the implement itself rather than vehicle's center. As such, we present in this paper two approaches for achieving the control of an offset point on the robot. The first approach adapts existing control laws, initially inteded for rear axle's midpoint, to manage the desired lateral deviation. The second approach employs backstepping control techniques to create a control law that directly targets the implement. We conduct real-world experiments, highlighting the limitations of traditional approaches for offset points control, and demonstrating the strengths and weaknesses of the proposed methods.

eess.SY↗

arXiv2024

Obstacle crossing strategies for high-speed 4WD small-scale vehicle

Unmanned ground vehicle obstacle crossing generally relies on two strategies: (i) applying a wheel torque for climbing and (ii) modifying the vehicle shape by using a wheel-leg or wheel-paddle to lift the wheel on top of the obstacle. However, most of those strategies sacrifice speed in order to have a longer contact duration between the wheels and the obstacle. This paper investigates the behaviour of a 4WD high-speed vehicle while crossing a step obstacle using a design of experiment (DoE). A 3D multibody vehicle model is equipped with a novel 2-DoF suspension system, which horizontal damping coefficient is modify to dampen wheel motion in longitudinal and vertical directions in relation to the chassis, for a given speed and obstacle height. The DoE results allow to propose a novel high-speed obstacle crossing strategy based on three metrics: (i) the kinetic energy variation of the vehicle, (ii) the contact duration between the wheel and the obstacle, and (iii) the pitch rate at the start of the ballistic phase. Experimental function are proposed to be able modify these metric in real time.

cs.RO↗