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Globally Guided Deep V-Network-Based Motion Planning Algorithm for Fixed-Wing Unmanned Aerial Vehicles.
- Source :
-
Sensors (14248220) . Jun2024, Vol. 24 Issue 12, p3984. 17p. - Publication Year :
- 2024
-
Abstract
- Fixed-wing UAVs have shown great potential in both military and civilian applications. However, achieving safe and collision-free flight in complex obstacle environments is still a challenging problem. This paper proposed a hierarchical two-layer fixed-wing UAV motion planning algorithm based on a global planner and a local reinforcement learning (RL) planner in the presence of static obstacles and other UAVs. Considering the kinematic constraints, a global planner is designed to provide reference guidance for ego-UAV with respect to static obstacles. On this basis, a local RL planner is designed to accomplish kino-dynamic feasible and collision-free motion planning that incorporates dynamic obstacles within the sensing range. Finally, in the simulation training phase, a multi-stage, multi-scenario training strategy is adopted, and the simulation experimental results show that the performance of the proposed algorithm is significantly better than that of the baseline method. [ABSTRACT FROM AUTHOR]
- Subjects :
- *REINFORCEMENT learning
*ALGORITHMS
*DRONE aircraft
*VERTICALLY rising aircraft
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 24
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- Sensors (14248220)
- Publication Type :
- Academic Journal
- Accession number :
- 178190660
- Full Text :
- https://doi.org/10.3390/s24123984