A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning
This paper presents a depth-based hybrid method to generate safe flight corridors for a memoryless local navigation planner. It is first proposed to use raw depth images as inputs in the learning-based object-detection engine with no requirement for map fusion. We then employ an object-detection net...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/16/7206 |
| _version_ | 1827728698959200256 |
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| author | Thai Binh Nguyen Manzur Murshed Tanveer Choudhury Kathleen Keogh Gayan Kahandawa Appuhamillage Linh Nguyen |
| author_facet | Thai Binh Nguyen Manzur Murshed Tanveer Choudhury Kathleen Keogh Gayan Kahandawa Appuhamillage Linh Nguyen |
| author_sort | Thai Binh Nguyen |
| collection | DOAJ |
| description | This paper presents a depth-based hybrid method to generate safe flight corridors for a memoryless local navigation planner. It is first proposed to use raw depth images as inputs in the learning-based object-detection engine with no requirement for map fusion. We then employ an object-detection network to directly predict the base of polyhedral safe corridors in a new raw depth image. Furthermore, we apply a verification procedure to eliminate any false predictions so that the resulting collision-free corridors are guaranteed. More importantly, the proposed mechanism helps produce separate safe corridors with minimal overlap that are suitable to be used as space boundaries for path planning. The average intersection of union (IoU) of corridors obtained by the proposed algorithm is less than 2%. To evaluate the effectiveness of our method, we incorporated it into a memoryless planner with a straight-line path-planning algorithm. We then tested the entire system in both synthetic and real-world obstacle-dense environments. The obtained results with very high success rates demonstrate that the proposed approach is highly capable of producing safe corridors for memoryless local planning. |
| first_indexed | 2024-03-10T23:35:42Z |
| format | Article |
| id | doaj.art-799f8e7ba00c409f9fc2999b1fc29df3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:35:42Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-799f8e7ba00c409f9fc2999b1fc29df32023-11-19T02:58:20ZengMDPI AGSensors1424-82202023-08-012316720610.3390/s23167206A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless PlanningThai Binh Nguyen0Manzur Murshed1Tanveer Choudhury2Kathleen Keogh3Gayan Kahandawa Appuhamillage4Linh Nguyen5Institute of Innovation, Science and Sustainability, Federation University Australia, Churchill, VIC 3842, AustraliaSchool of Information Technology, Deakin University, Burwood, VIC 3125, AustraliaInstitute of Innovation, Science and Sustainability, Federation University Australia, Churchill, VIC 3842, AustraliaInstitute of Innovation, Science and Sustainability, Federation University Australia, Churchill, VIC 3842, AustraliaInstitute of Innovation, Science and Sustainability, Federation University Australia, Churchill, VIC 3842, AustraliaInstitute of Innovation, Science and Sustainability, Federation University Australia, Churchill, VIC 3842, AustraliaThis paper presents a depth-based hybrid method to generate safe flight corridors for a memoryless local navigation planner. It is first proposed to use raw depth images as inputs in the learning-based object-detection engine with no requirement for map fusion. We then employ an object-detection network to directly predict the base of polyhedral safe corridors in a new raw depth image. Furthermore, we apply a verification procedure to eliminate any false predictions so that the resulting collision-free corridors are guaranteed. More importantly, the proposed mechanism helps produce separate safe corridors with minimal overlap that are suitable to be used as space boundaries for path planning. The average intersection of union (IoU) of corridors obtained by the proposed algorithm is less than 2%. To evaluate the effectiveness of our method, we incorporated it into a memoryless planner with a straight-line path-planning algorithm. We then tested the entire system in both synthetic and real-world obstacle-dense environments. The obtained results with very high success rates demonstrate that the proposed approach is highly capable of producing safe corridors for memoryless local planning.https://www.mdpi.com/1424-8220/23/16/7206depth sensingmemoryless planningsafe flight corridordronesUAV |
| spellingShingle | Thai Binh Nguyen Manzur Murshed Tanveer Choudhury Kathleen Keogh Gayan Kahandawa Appuhamillage Linh Nguyen A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning Sensors depth sensing memoryless planning safe flight corridor drones UAV |
| title | A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning |
| title_full | A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning |
| title_fullStr | A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning |
| title_full_unstemmed | A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning |
| title_short | A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning |
| title_sort | depth based hybrid approach for safe flight corridor generation in memoryless planning |
| topic | depth sensing memoryless planning safe flight corridor drones UAV |
| url | https://www.mdpi.com/1424-8220/23/16/7206 |
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