Accuracy Preserving Interpretability with Hybrid Hierarchical Genetic Fuzzy Modeling: Case of Motion Planning Robot Controller

I. Kallel, N. Baklouti, A. Alimi
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引用次数: 14

Abstract

Design of robot controller for motion planning, using fuzzy logic control, requires formulation of rules that are collectively responsible for necessary levels of intelligent behaviors. To ensure the model interpretability, this collection of rules can be naturally decomposed and efficiently implemented as a hierarchical fuzzy model. This paper describes how this can be done using hybrid hierarchical genetic fuzzy modeling. The idea is to combine, in a hierarchical design, "mapping" for sub-goal behavior (SGB), and "reactivity" for local avoiding obstacles behavior (LAOB), to have at the same time, an interpretable and precise communicating system for robot motion planning controller. The design of each fuzzy unit of the hierarchical model is automatically ensured by MAGAD-BFS method (multi-agent genetic algorithm for the design of beta fuzzy systems), promoting itself as an interpretability-accuracy trade-off. A proposed reduced version of generalized local Voronoi diagram (RGLVD) comes to guarantee a high degree of precision for robot motion to attempt destinations (sub-goals). Compared to the navigation using only fuzzy rules controller, the hybrid hierarchical model is more efficient in terms of saving time and optimizing path
基于混合层次遗传模糊模型的精度保持可解释性:以运动规划机器人控制器为例
使用模糊逻辑控制进行机器人运动规划控制器的设计,需要制定规则,这些规则共同负责必要的智能行为层次。为了保证模型的可解释性,该规则集合可以被自然分解并有效地实现为层次模糊模型。本文描述了如何使用混合层次遗传模糊建模来实现这一目标。该思想是在分层设计中结合子目标行为(SGB)的“映射”和局部避障行为(LAOB)的“反应性”,同时为机器人运动规划控制器提供一个可解释和精确的通信系统。分层模型的每个模糊单元的设计由MAGAD-BFS方法(用于设计beta模糊系统的多智能体遗传算法)自动确保,从而促进了可解释性与准确性之间的权衡。提出了广义局部Voronoi图(RGLVD)的简化版本,以保证机器人运动到尝试目的地(子目标)的高度精度。与仅使用模糊规则控制器的导航相比,混合层次模型在节省时间和优化路径方面更有效
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