Sensor Fusion-Based Cooperative Trail Following for Autonomous Multi-Robot System
Autonomously following a man-made trail in the wild is a challenging problem for robotic systems. Recently, deep learning-based approaches have cast the trail following problem as an image classification task and have achieved great success in the vision-based trail-following problem. However, the e...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/4/823 |
| _version_ | 1798025879612293120 |
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| author | Mingyang Geng Shuqi Liu Zhaoxia Wu |
| author_facet | Mingyang Geng Shuqi Liu Zhaoxia Wu |
| author_sort | Mingyang Geng |
| collection | DOAJ |
| description | Autonomously following a man-made trail in the wild is a challenging problem for robotic systems. Recently, deep learning-based approaches have cast the trail following problem as an image classification task and have achieved great success in the vision-based trail-following problem. However, the existing research only focuses on the trail-following task with a single-robot system. In contrast, many robotic tasks in reality, such as search and rescue, are conducted by a group of robots. While these robots are grouped to move in the wild, they can cooperate to lead to a more robust performance and perform the trail-following task in a better manner. Concretely, each robot can periodically exchange the vision data with other robots and make decisions based both on its local view and the information from others. This paper proposes a sensor fusion-based cooperative trail-following method, which enables a group of robots to implement the trail-following task by fusing the sensor data of each robot. Our method allows each robot to face the same direction from different altitudes to fuse the vision data feature on the collective level and then take action respectively. Besides, considering the quality of service requirement of the robotic software, our method limits the condition to implementing the sensor data fusion process by using the “threshold” mechanism. Qualitative and quantitative experiments on the real-world dataset have shown that our method can significantly promote the recognition accuracy and lead to a more robust performance compared with the single-robot system. |
| first_indexed | 2024-04-11T18:26:02Z |
| format | Article |
| id | doaj.art-14e93d892e8f438c9fe28aa85d45be26 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:26:02Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-14e93d892e8f438c9fe28aa85d45be262022-12-22T04:09:37ZengMDPI AGSensors1424-82202019-02-0119482310.3390/s19040823s19040823Sensor Fusion-Based Cooperative Trail Following for Autonomous Multi-Robot SystemMingyang Geng0Shuqi Liu1Zhaoxia Wu2National Key Laboratory of Parallel and Distributed Processing, College of Computer, National University of Defense Technology, Changsha 410073, ChinaNational Key Laboratory of Big Data Management and Analysis, Northeastern University, Shenyang 110000, ChinaSchool of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, ChinaAutonomously following a man-made trail in the wild is a challenging problem for robotic systems. Recently, deep learning-based approaches have cast the trail following problem as an image classification task and have achieved great success in the vision-based trail-following problem. However, the existing research only focuses on the trail-following task with a single-robot system. In contrast, many robotic tasks in reality, such as search and rescue, are conducted by a group of robots. While these robots are grouped to move in the wild, they can cooperate to lead to a more robust performance and perform the trail-following task in a better manner. Concretely, each robot can periodically exchange the vision data with other robots and make decisions based both on its local view and the information from others. This paper proposes a sensor fusion-based cooperative trail-following method, which enables a group of robots to implement the trail-following task by fusing the sensor data of each robot. Our method allows each robot to face the same direction from different altitudes to fuse the vision data feature on the collective level and then take action respectively. Besides, considering the quality of service requirement of the robotic software, our method limits the condition to implementing the sensor data fusion process by using the “threshold” mechanism. Qualitative and quantitative experiments on the real-world dataset have shown that our method can significantly promote the recognition accuracy and lead to a more robust performance compared with the single-robot system.https://www.mdpi.com/1424-8220/19/4/823trail followingcooperative perceptionmulti-robot systemfeature fusion |
| spellingShingle | Mingyang Geng Shuqi Liu Zhaoxia Wu Sensor Fusion-Based Cooperative Trail Following for Autonomous Multi-Robot System Sensors trail following cooperative perception multi-robot system feature fusion |
| title | Sensor Fusion-Based Cooperative Trail Following for Autonomous Multi-Robot System |
| title_full | Sensor Fusion-Based Cooperative Trail Following for Autonomous Multi-Robot System |
| title_fullStr | Sensor Fusion-Based Cooperative Trail Following for Autonomous Multi-Robot System |
| title_full_unstemmed | Sensor Fusion-Based Cooperative Trail Following for Autonomous Multi-Robot System |
| title_short | Sensor Fusion-Based Cooperative Trail Following for Autonomous Multi-Robot System |
| title_sort | sensor fusion based cooperative trail following for autonomous multi robot system |
| topic | trail following cooperative perception multi-robot system feature fusion |
| url | https://www.mdpi.com/1424-8220/19/4/823 |
| work_keys_str_mv | AT mingyanggeng sensorfusionbasedcooperativetrailfollowingforautonomousmultirobotsystem AT shuqiliu sensorfusionbasedcooperativetrailfollowingforautonomousmultirobotsystem AT zhaoxiawu sensorfusionbasedcooperativetrailfollowingforautonomousmultirobotsystem |