Enhancing autonomous exploration for robotics via real time map optimization and improved frontier costs
Abstract Autonomous exploration and mapping in unknown environments remain pivotal in robotics research. The efficiency of autonomous exploration is often constrained by irrational exploration strategies and incomplete map exploration. This paper proposes an efficient autonomous exploration method b...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-97231-9 |
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| _version_ | 1849726146456846336 |
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| author | Chunyang Liu Dingfa Zhang Weitao Liu Xin Sui Yan Huang Xiqiang Ma Xiaokang Yang Xiao Wang |
| author_facet | Chunyang Liu Dingfa Zhang Weitao Liu Xin Sui Yan Huang Xiqiang Ma Xiaokang Yang Xiao Wang |
| author_sort | Chunyang Liu |
| collection | DOAJ |
| description | Abstract Autonomous exploration and mapping in unknown environments remain pivotal in robotics research. The efficiency of autonomous exploration is often constrained by irrational exploration strategies and incomplete map exploration. This paper proposes an efficient autonomous exploration method based on a frontier strategy, aiming to enhance the performance of ground mobile robots in exploration and mapping tasks. We employ a real-time grid map optimization technique using bilateral filtering and expansion to eliminate inefficient frontiers, improve mapping quality, and enhance the overall efficiency of autonomous exploration. Additionally, we construct a novel frontier cost function that incorporates factors such as path length, sensor measurement range, and information gain. Our approach uniquely combines an autonomous exploration decision model with the Minimum Ratio Travelling Salesman Problem (MRTSP) to maximize the explored area within the shortest possible path. Comparative analyses with classic methods, conducted in both simulated and real environments, demonstrate a 10–30% improvement in exploration efficiency through our approach. |
| format | Article |
| id | doaj-art-1b4e13f7d19f446297f2eb3c4f23bbf3 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1b4e13f7d19f446297f2eb3c4f23bbf32025-08-20T03:10:17ZengNature PortfolioScientific Reports2045-23222025-04-0115111210.1038/s41598-025-97231-9Enhancing autonomous exploration for robotics via real time map optimization and improved frontier costsChunyang Liu0Dingfa Zhang1Weitao Liu2Xin Sui3Yan Huang4Xiqiang Ma5Xiaokang Yang6Xiao Wang7Henan University of Science and TechnologyHenan University of Science and TechnologyHenan University of Science and TechnologyHenan University of Science and TechnologyHenan University of Science and TechnologyHenan University of Science and TechnologyHenan University of Science and TechnologyHenan University of Science and TechnologyAbstract Autonomous exploration and mapping in unknown environments remain pivotal in robotics research. The efficiency of autonomous exploration is often constrained by irrational exploration strategies and incomplete map exploration. This paper proposes an efficient autonomous exploration method based on a frontier strategy, aiming to enhance the performance of ground mobile robots in exploration and mapping tasks. We employ a real-time grid map optimization technique using bilateral filtering and expansion to eliminate inefficient frontiers, improve mapping quality, and enhance the overall efficiency of autonomous exploration. Additionally, we construct a novel frontier cost function that incorporates factors such as path length, sensor measurement range, and information gain. Our approach uniquely combines an autonomous exploration decision model with the Minimum Ratio Travelling Salesman Problem (MRTSP) to maximize the explored area within the shortest possible path. Comparative analyses with classic methods, conducted in both simulated and real environments, demonstrate a 10–30% improvement in exploration efficiency through our approach.https://doi.org/10.1038/s41598-025-97231-9Wheeled robotsMappingAutonomous exploration |
| spellingShingle | Chunyang Liu Dingfa Zhang Weitao Liu Xin Sui Yan Huang Xiqiang Ma Xiaokang Yang Xiao Wang Enhancing autonomous exploration for robotics via real time map optimization and improved frontier costs Scientific Reports Wheeled robots Mapping Autonomous exploration |
| title | Enhancing autonomous exploration for robotics via real time map optimization and improved frontier costs |
| title_full | Enhancing autonomous exploration for robotics via real time map optimization and improved frontier costs |
| title_fullStr | Enhancing autonomous exploration for robotics via real time map optimization and improved frontier costs |
| title_full_unstemmed | Enhancing autonomous exploration for robotics via real time map optimization and improved frontier costs |
| title_short | Enhancing autonomous exploration for robotics via real time map optimization and improved frontier costs |
| title_sort | enhancing autonomous exploration for robotics via real time map optimization and improved frontier costs |
| topic | Wheeled robots Mapping Autonomous exploration |
| url | https://doi.org/10.1038/s41598-025-97231-9 |
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