Autonomous Navigation of Tracked Robot in Uneven Terrains
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 14274 LNAI, Page: 74-84
2023
- 1Citations
- 1Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Since microdosing of chemical, biological, radiological, and nuclear (CBRN) contaminants is enough to cause great damage to humans, operating robots are widely used to handle CBRN-related tasks. However, how to improve the automation capabilities of these robots in uneven environments, such as autonomous navigation, is still a huge challenge. Current navigation methods usually set the scene as flat pavement, without considering the situation that the land slope exceeds a certain threshold. In order to explore ways of autonomous navigation in uneven environments, a 3D path planning and navigation method for the tracked robot is proposed in this paper, respecting applicable traversability constraints in uneven terrains. Firstly, a 3D graph-based map is built according to the occupancy map of the uneven environment. A set of spatial points, ensuring collision-avoidance of the robot, is randomly sampled in free space, and a vertex map is generated based on these vertices for robot traversing. Then, regarding the robot’s climbing and obstacle crossing ability as constraints, a path planning algorithm is used to search for the best path based on the Dijkstra algorithm. Finally, a fusion SLAM method based on LiDAR, IMU and RGB-D camera is used to achieve real-time localization, and the pure pursuit algorithm is used for navigation. The simulation results show that the proposed method can provide a safe and effective 3D path for the tracked robot and enable the robot’s autonomous navigation in uneven environments.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85175984919&origin=inward; http://dx.doi.org/10.1007/978-981-99-6501-4_7; https://link.springer.com/10.1007/978-981-99-6501-4_7; https://dx.doi.org/10.1007/978-981-99-6501-4_7; https://link.springer.com/chapter/10.1007/978-981-99-6501-4_7
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know