On Trajectory Homotopy to Explore and Penetrate Dynamically of Multi-UAV
This paper examines a trajectory homotopy optimization framework for multiple unmanned aerial vehicles (multi-UAV) to solve the problem of dynamic penetration mission planning (PMP) with hostile obstacles and perception constraints. Constrained problems are usually more challenging and difficult to...
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Published in | IEEE transactions on intelligent transportation systems Vol. 23; no. 12; pp. 24008 - 24019 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | This paper examines a trajectory homotopy optimization framework for multiple unmanned aerial vehicles (multi-UAV) to solve the problem of dynamic penetration mission planning (PMP) with hostile obstacles and perception constraints. Constrained problems are usually more challenging and difficult to solve with some practical constraints and requirements. To improve the efficiency of the solution for the penetration path, a novel variable-time mechanism has been constructed to adapt to the updated delay time of unknown target search (UTS) and dynamic trajectory planning (DTP) two stages. The occupancy grid maps are established by a Gaussian probability field (GPF) for predicting the positions of enemy UAVs. To fully consider the hostile obstacle constraint, a hybrid adaptive obstacle avoidance approach dynamic window PRM (DW-PRM) is designed to shorten the planned path. The penetration strategy algorithm (SG) is developed based on the proposed strategy set and decision tree. To improve the ability of dynamic obstacle avoidance, the multiple coupled penetration homotopy trajectory is addressed with a turning radius constraint. The simulation results indicated that the penetration homotopy framework for multi-constraints can solve the multi-UAV PMP problem. |
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ISSN: | 1524-9050 1558-0016 |
DOI: | 10.1109/TITS.2022.3195521 |