Hierarchical planning guided by genetic algorithms for multiple haps in a time-varying environment

Abstract

A hierarchical task planning structure is favorable for its capability to accommodate constraints at different abstraction levels and also for the similarity of its planning approach as a human. This structure is adopted for the task planning for multiple HAPS. However, the combinatorial search problem grows with the presence of multiple agents. This work proposes a method to guide the decomposition of the tasks down the hierarchy with genetic algorithm in order to find quality plans within limited time.