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STL-based multi-agent motion planning for multiple tasks with complex logic
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AI
DOI：10.1016/j.actaastro.2025.09.014.png)
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Abstract
En 中文
• MAMT-STL sharply reduces the complexity of complex task constraints. • Task selection variables eliminate infeasibility risks in saturated scenarios. • Gap-based method simplifies inter-task order constraints for higher efficiency.
Keywords:
Multi-agent system
Signal temporal logic
Task assignment
Trajectory planning
Complex logic