Robotic monitoring of forests: a dataset from the EU habitat 9210* in the Tuscan Apennines (central Italy)

Effective monitoring of habitats is crucial for their preservation. As the impact of anthropic activities on natural habitats increases, accurate and up-to-date information on the state of ecosystems has become imperative. This paper presents a new dataset collected from the forests located in the Tuscan Apennines (Italy) using the ANYmal robot. The dataset provides information regarding the structure and composition of the EU priority habitat 9210*. The dataset, which is publicly available through a Zenodo repository, includes photos, videos, and point clouds of the environment. This dataset is a valuable resource for the scientific community working in the field of forest ecology and conservation and has the potential to inform future research and conservation efforts on habitat 9210*. the collaboration between robotic engineers and plant scientists provides a unique perspective on the forest ecosystem and underscores the potential for interdisciplinary work in this field. This dataset constitutes an important contribution to the ongoing effort to monitor and conserve habitats globally, particularly in light of the challenges posed by global changes

Robotic monitoring of dunes: a dataset from the EU habitats 2110 and 2120 in Sardinia (Italy)

Abstract This data descriptor presents a novel dataset collected using the quadrupedal robot ANYmal C in the Mediterranean coastal dune environment of the European Union (EU) habitats 2110 and 2120 in Sardinia, Italy. The dataset mainly consists of photos, videos, and point clouds of the coastal dunes, providing valuable information on the structure and composition of this habitat. The data was collected by a team of robotic engineers and plant scientists as result of a joint effort towards robotic habitat monitoring. The dataset is publicly available through Zenodo and can be used by researchers working in both the fields of robotics and habitat ecology and conservation. The availability of this dataset has the potential to inform future research and conservation efforts in the EU habitats 2110 and 2120, and it highlights the importance of interdisciplinary collaboration in the field of habitat monitoring. This paper serves as a comprehensive description of the dataset and the methods used to collect it, making it a valuable resource for the scientific community

Robotic monitoring of Alpine screes: a dataset from the EU Natura2000 habitat 8110 in the Italian Alps

Abstract The surveying of European Union (EU) Annex I habitat “8110 - Siliceous scree of the montane to snow levels (Androsacetalia alpinae and Galeopsietalia ladani)” is generally executed by humans. However, robots could increase human monitoring capabilities. To this end, we collected information on this habitat employing the quadrupedal robot ANYmal C. These data include videos of eight different typical or early warning species. Additionally, data on four relevés are provided. These consist, for instance, of the robot state, and videos and pictures collected to evaluate the habitat conservation status. The aim of this dataset is to help researchers in a variety of fields. For instance, information on plant species collected by the robot can be utilized to develop new procedures and new metrics to assess the habitat conservation status or to train neural networks for plant classification. On the other hand, engineers can use robot state information to validate their algorithms. This database is publicly available in the provided Zenodo repository

Planning Natural Locomotion for Articulated Soft Quadrupeds

Embedding elastic elements into legged robots through mechanical design enables highly efficient oscillating patterns that resemble natural gaits. However, current trajectory planning techniques miss the opportunity of taking advantage of these natural motions. This work proposes a locomotion planning method that aims to unify traditional trajectory generation with modal oscillations. Our method utilizes task-space linearized modes for generating center of mass trajectories on the sagittal plane. We then use nonlinear optimization to find the gait timings that match these trajectories within the Divergent Component of Motion planning framework. This way, we can robustly translate the modes-aware centroidal motions into joint coordinates. We validate our approach with promising results and insights through experiments on a compliant quadrupedal robot