Design of Robust Behavior Tree Control Architecture for Agents in Dynamic Task Environment

doi:10.16182/j.issn1004731x.joss.24-0584

Abstract

Abstract:

In recent years, the environment in which agents perform tasks has become more open and dynamic, which puts forward higher requirements for the robustness of task planning and behavior scheduling of agents. As a classic behavior control architecture, behavior tree has the characteristics of modularity, behavior parameterization, and structure of both plan representation and reaction, which can effectively support the behavior representation, decision making and scheduling of agents. Based on the hybrid behavior strategy, this paper proposes a robust behavior tree control architecture for dynamic task environment to realize the prudent decision-making and reactive control of agents. The robust behavior tree control architecture combines the prudent planning algorithm and reactive rules of the agent. A behavior tree code framework of the architecture is accordingly designed, and the planning subtree and the Loopnode are customized to support the fast implementation of the behavior tree to complete the switch between planning and reaction within the tree, which supports users to implement behavior tree quickly and standardly based on the customized structure. By the modeling and simulating of the agent object in a typical attack and defense game case, the paper verifies the rationality of the proposed behavior tree control architecture. Compared with the classical reactive behavior tree strategy, the robust behavior tree can flexibly switch between execution of the plan and reactive strategies, improve the blue agent's penetration win rate to 90% in typical scenarios, shorten task execution time by 24.88%, and improve the effectiveness of agent behavior strategy and modeling efficiency.

Key words: dynamic environment, behavior tree control architecture, hybrid behavior decision, node customization, path planning

CLC Number:

TP391.9

Wang Qiwei, Zhang Qi, Yang Shuo, Peng Yong. Design of Robust Behavior Tree Control Architecture for Agents in Dynamic Task Environment[J]. Journal of System Simulation, 2025, 37(1): 54-65.

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参数名称	参数大小	参数类型	含义
红蓝方运动速度/ (m/s)	25	智能体状态参数	双方智能体移动速度
智能体最大感知范围/ m	30	智能体状态参数	该范围内对红方进行反应
地图面积/m²	490×490	环境参数	智能体运动的范围参考
静态障碍物面积/ m²	20×20	环境参数	每个障碍物块的大小
目标位置	(20，20)	目标参数	目标的中心坐标
仿真总时长/s	300	时间参数	限制单次实验的最大时长

数据名称	反应树数值	健壮树数值
单回合平均运行时长/s	22.89	18.33
运行至最大时间回合数	5	0
蓝方胜利回合数	40	45

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