Failure-Robot Path Complementation for Robot Swarm Mission Planning
Currently, unmanned vehicles are widely used in different fields of exploration. Due to limited capacities, such as limited power supply, it is almost impossible for one unmanned vehicle to visit multiple wide areas. Multiple unmanned vehicles with well-planned routes are required to minimize an unn...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/18/3756 |
| _version_ | 1818573818748207104 |
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| author | Meng-Tse Lee Bo-Yu Chen Wen-Chi Lu |
| author_facet | Meng-Tse Lee Bo-Yu Chen Wen-Chi Lu |
| author_sort | Meng-Tse Lee |
| collection | DOAJ |
| description | Currently, unmanned vehicles are widely used in different fields of exploration. Due to limited capacities, such as limited power supply, it is almost impossible for one unmanned vehicle to visit multiple wide areas. Multiple unmanned vehicles with well-planned routes are required to minimize an unnecessary consumption of time, distance, and energy waste. The aim of the present study was to develop a multiple-vehicle system that can automatically compile a set of optimum vehicle paths, complement failed assignments, and avoid passing through no-travel zones. A heuristic algorithm was used to obtain an approximate solution within a reasonable timeline. The A* Search algorithm was adopted to determine an alternative path that does not cross the no-travel zone when the distance array was set, and an improved two-phased Tabu search was applied to converge any initial solutions into a feasible solution. A diversification strategy helped identify a global optimal solution rather than a regional one. The final experiments successfully demonstrated a group of three robot cars that were simultaneously dispatched to each of their planned routes; when any car failed during the test, its path was immediately reprogrammed by the monitoring station and passed to the other cars to continue the task until each target point had been visited. |
| first_indexed | 2024-12-15T00:17:59Z |
| format | Article |
| id | doaj.art-dd477508633a45458660a58ffdc8ddfc |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-15T00:17:59Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-dd477508633a45458660a58ffdc8ddfc2022-12-21T22:42:25ZengMDPI AGApplied Sciences2076-34172019-09-01918375610.3390/app9183756app9183756Failure-Robot Path Complementation for Robot Swarm Mission PlanningMeng-Tse Lee0Bo-Yu Chen1Wen-Chi Lu2Department of Automation Engineering, National Formosa University, Yunlin 632, TaiwanDepartment of Automation Engineering, National Formosa University, Yunlin 632, TaiwanDepartment of Aeronautical Engineering, National Formosa University, Yunlin 632, TaiwanCurrently, unmanned vehicles are widely used in different fields of exploration. Due to limited capacities, such as limited power supply, it is almost impossible for one unmanned vehicle to visit multiple wide areas. Multiple unmanned vehicles with well-planned routes are required to minimize an unnecessary consumption of time, distance, and energy waste. The aim of the present study was to develop a multiple-vehicle system that can automatically compile a set of optimum vehicle paths, complement failed assignments, and avoid passing through no-travel zones. A heuristic algorithm was used to obtain an approximate solution within a reasonable timeline. The A* Search algorithm was adopted to determine an alternative path that does not cross the no-travel zone when the distance array was set, and an improved two-phased Tabu search was applied to converge any initial solutions into a feasible solution. A diversification strategy helped identify a global optimal solution rather than a regional one. The final experiments successfully demonstrated a group of three robot cars that were simultaneously dispatched to each of their planned routes; when any car failed during the test, its path was immediately reprogrammed by the monitoring station and passed to the other cars to continue the task until each target point had been visited.https://www.mdpi.com/2076-3417/9/18/3756robot swarmpath programmingfailure complementation |
| spellingShingle | Meng-Tse Lee Bo-Yu Chen Wen-Chi Lu Failure-Robot Path Complementation for Robot Swarm Mission Planning Applied Sciences robot swarm path programming failure complementation |
| title | Failure-Robot Path Complementation for Robot Swarm Mission Planning |
| title_full | Failure-Robot Path Complementation for Robot Swarm Mission Planning |
| title_fullStr | Failure-Robot Path Complementation for Robot Swarm Mission Planning |
| title_full_unstemmed | Failure-Robot Path Complementation for Robot Swarm Mission Planning |
| title_short | Failure-Robot Path Complementation for Robot Swarm Mission Planning |
| title_sort | failure robot path complementation for robot swarm mission planning |
| topic | robot swarm path programming failure complementation |
| url | https://www.mdpi.com/2076-3417/9/18/3756 |
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