Collision-Aware Traversability Analysis for Autonomous Vehicles in the Context of Agricultural Robotics
<|reference_start|>Collision-Aware Traversability Analysis for Autonomous Vehicles in the Context of Agricultural Robotics: In this paper, we introduce a novel method for safe navigation in agricultural robotics. As global environmental challenges intensify, robotics offers a powerful solution to reduce chemical usage while meeting the increasing demands for food production. However, significant challenges remain in ensuring the autonomy and resilience of robots operating in unstructured agricultural environments. Obstacles such as crops and tall grass, which are deformable, must be identified as safely traversable, compared to rigid obstacles. To address this, we propose a new traversability analysis method based on a 3D spectral map reconstructed using a LIDAR and a multispectral camera. This approach enables the robot to distinguish between safe and unsafe collisions with deformable obstacles. We perform a comprehensive evaluation of multispectral metrics for vegetation detection and incorporate these metrics into an augmented environmental map. Utilizing this map, we compute a physics-based traversability metric that accounts for the robot's weight and size, ensuring safe navigation over deformable obstacles.<|reference_end|>
@article{philippe2024collision-aware,
    title={Collision-Aware Traversability Analysis for Autonomous Vehicles in the
  Context of Agricultural Robotics},
    author={Florian Philippe, Johann Laconte, Pierre-Jean Lapray, Matthias Spisser
  and Jean-Philippe Lauffenburger},
    journal={arXiv preprint arXiv:2410.03370},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03370},
    primaryClass={cs.RO}
}