Terrain Traversability via Sensed Data for Robots Operating Inside Heterogeneous, Highly Unstructured Spaces
This paper presents a comprehensive approach to evaluating the ability of multi-legged robots to traverse confined and geometrically complex unstructured environments. The proposed approach utilizes advanced point cloud processing techniques integrating voxel-filtered cloud, boundary and mesh genera...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/2/439 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587489087324160 |
|---|---|
| author | Amir Gholami Alejandro Ramirez-Serrano |
| author_facet | Amir Gholami Alejandro Ramirez-Serrano |
| author_sort | Amir Gholami |
| collection | DOAJ |
| description | This paper presents a comprehensive approach to evaluating the ability of multi-legged robots to traverse confined and geometrically complex unstructured environments. The proposed approach utilizes advanced point cloud processing techniques integrating voxel-filtered cloud, boundary and mesh generation, and dynamic traversability analysis to enhance the robot’s terrain perception and navigation. The proposed framework was validated through rigorous simulation and experimental testing with humanoid robots, showcasing the potential of the proposed approach for use in applications/environments characterized by complex environmental features (navigation inside collapsed buildings). The results demonstrate that the proposed framework provides the robot with an enhanced capability to perceive and interpret its environment and adapt to dynamic environment changes. This paper contributes to the advancement of robotic navigation and path-planning systems by providing a scalable and efficient framework for environment analysis. The integration of various point cloud processing techniques into a single architecture not only improves computational efficiency but also enhances the robot’s interaction with its environment, making it more capable of operating in complex, hazardous, unstructured settings. |
| format | Article |
| id | doaj-art-3dae137783b84e16b430753dcaf5b588 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3dae137783b84e16b430753dcaf5b5882025-01-24T13:48:56ZengMDPI AGSensors1424-82202025-01-0125243910.3390/s25020439Terrain Traversability via Sensed Data for Robots Operating Inside Heterogeneous, Highly Unstructured SpacesAmir Gholami0Alejandro Ramirez-Serrano1Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaDepartment of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaThis paper presents a comprehensive approach to evaluating the ability of multi-legged robots to traverse confined and geometrically complex unstructured environments. The proposed approach utilizes advanced point cloud processing techniques integrating voxel-filtered cloud, boundary and mesh generation, and dynamic traversability analysis to enhance the robot’s terrain perception and navigation. The proposed framework was validated through rigorous simulation and experimental testing with humanoid robots, showcasing the potential of the proposed approach for use in applications/environments characterized by complex environmental features (navigation inside collapsed buildings). The results demonstrate that the proposed framework provides the robot with an enhanced capability to perceive and interpret its environment and adapt to dynamic environment changes. This paper contributes to the advancement of robotic navigation and path-planning systems by providing a scalable and efficient framework for environment analysis. The integration of various point cloud processing techniques into a single architecture not only improves computational efficiency but also enhances the robot’s interaction with its environment, making it more capable of operating in complex, hazardous, unstructured settings.https://www.mdpi.com/1424-8220/25/2/439traversabilityunstructured spacespoint cloud processingmulti-legged robots |
| spellingShingle | Amir Gholami Alejandro Ramirez-Serrano Terrain Traversability via Sensed Data for Robots Operating Inside Heterogeneous, Highly Unstructured Spaces Sensors traversability unstructured spaces point cloud processing multi-legged robots |
| title | Terrain Traversability via Sensed Data for Robots Operating Inside Heterogeneous, Highly Unstructured Spaces |
| title_full | Terrain Traversability via Sensed Data for Robots Operating Inside Heterogeneous, Highly Unstructured Spaces |
| title_fullStr | Terrain Traversability via Sensed Data for Robots Operating Inside Heterogeneous, Highly Unstructured Spaces |
| title_full_unstemmed | Terrain Traversability via Sensed Data for Robots Operating Inside Heterogeneous, Highly Unstructured Spaces |
| title_short | Terrain Traversability via Sensed Data for Robots Operating Inside Heterogeneous, Highly Unstructured Spaces |
| title_sort | terrain traversability via sensed data for robots operating inside heterogeneous highly unstructured spaces |
| topic | traversability unstructured spaces point cloud processing multi-legged robots |
| url | https://www.mdpi.com/1424-8220/25/2/439 |
| work_keys_str_mv | AT amirgholami terraintraversabilityviasenseddataforrobotsoperatinginsideheterogeneoushighlyunstructuredspaces AT alejandroramirezserrano terraintraversabilityviasenseddataforrobotsoperatinginsideheterogeneoushighlyunstructuredspaces |