Multi-behaviors coordination controller design with enzymatic numerical P systems for robots

IF 5.8 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xueyuan Wang, Gexiang Zhang, Xiantai Gou, Prithwineel Paul, Ferrante Neri, Haina Rong, Qiang Yang, Hua Zhang
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引用次数: 26

Abstract

Membrane computing models are parallel and distributed natural computing models. These models are often referred to as P systems. This paper proposes a novel multi-behaviors co-ordination controller model using enzymatic numerical P systems for autonomous mobile robots navigation in unknown environments. An environment classifier is constructed to identify different environment patterns in the maze-like environment and the multi-behavior co-ordination controller is constructed to coordinate the behaviors of the robots in different environments. Eleven sensory prototypes of local environments are presented to design the environment classifier, which needs to memorize only rough information, for solving the problems of poor obstacle clearance and sensor noise. A switching control strategy and multi-behaviors coordinator are developed without detailed environmental knowledge and heavy computation burden, for avoiding the local minimum traps or oscillation problems and adapt to the unknown environments. Also, a serial behaviors control law is constructed on the basis of Lyapunov stability theory aiming at the specialized environment, for realizing stable navigation and avoiding actuator saturation. Moreover, both environment classifier and multi-behavior coordination controller are amenable to the addition of new environment models or new behaviors due to the modularity of the hierarchical architecture of P systems. The simulation of wheeled mobile robots shows the effectiveness of this approach.
机器人酶解数值P系统的多行为协调控制器设计
膜计算模型是一种并行分布的自然计算模型。这些模型通常被称为P系统。针对未知环境下自主移动机器人的导航问题,提出了一种基于酶解数值P系统的多行为协调控制器模型。构建了环境分类器来识别迷宫环境中的不同环境模式,构建了多行为协调控制器来协调机器人在不同环境中的行为。提出了11个局部环境的感知原型,设计了只需要记忆粗略信息的环境分类器,解决了障碍物清除能力差和传感器噪声大的问题。在不需要详细的环境知识和繁重的计算负担的情况下,开发了切换控制策略和多行为协调器,以避免局部最小陷阱或振荡问题,并适应未知环境。针对特定环境,基于李雅普诺夫稳定性理论构建了串行行为控制律,以实现稳定导航和避免执行器饱和。此外,由于P系统层次结构的模块化,环境分类器和多行为协调控制器都可以适应新的环境模型或新的行为。对轮式移动机器人的仿真表明了该方法的有效性。
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来源期刊
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering 工程技术-工程:综合
CiteScore
9.90
自引率
21.50%
发文量
21
审稿时长
>12 weeks
期刊介绍: Integrated Computer-Aided Engineering (ICAE) was founded in 1993. "Based on the premise that interdisciplinary thinking and synergistic collaboration of disciplines can solve complex problems, open new frontiers, and lead to true innovations and breakthroughs, the cornerstone of industrial competitiveness and advancement of the society" as noted in the inaugural issue of the journal. The focus of ICAE is the integration of leading edge and emerging computer and information technologies for innovative solution of engineering problems. The journal fosters interdisciplinary research and presents a unique forum for innovative computer-aided engineering. It also publishes novel industrial applications of CAE, thus helping to bring new computational paradigms from research labs and classrooms to reality. Areas covered by the journal include (but are not limited to) artificial intelligence, advanced signal processing, biologically inspired computing, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, intelligent and adaptive systems, internet-based technologies, knowledge discovery and engineering, machine learning, mechatronics, mobile computing, multimedia technologies, networking, neural network computing, object-oriented systems, optimization and search, parallel processing, robotics virtual reality, and visualization techniques.
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