A Design Framework of Exploration, Segmentation, Navigation, and Instruction (ESNI) for the Lifecycle of Intelligent Mobile Agents as a Method for Mapping an Unknown Built Environment

被引:0
作者
Chu, Junchi [1 ,2 ]
Tang, Xueyun [1 ,3 ]
Shen, Xiwei [1 ]
机构
[1] Univ Nevada, Sch Architecture, Las Vegas, NV 89154 USA
[2] Brown Univ, Dept Comp Sci, Providence, RI 02912 USA
[3] Rhode Isl Sch Design, Interior Architecture Dept, Providence, RI 02903 USA
关键词
artificial intelligence; autonomous agent; unknown built environment; hierarchical framework; path finding; robotic system design; STAR ALGORITHM;
D O I
10.3390/s22176615
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Recent work on intelligent agents is a popular topic among the artificial intelligence community and robotic system design. The complexity of designing a framework as a guide for intelligent agents in an unknown built environment suggests a pressing need for the development of autonomous agents. However, most of the existing intelligent mobile agent design focus on the achievement of agent's specific practicality and ignore the systematic integration. Furthermore, there are only few studies focus on how the agent can utilize the information collected in unknown build environment to produce a learning pipeline for fundamental task prototype. The hierarchical framework is a combination of different individual modules that support a type of functionality by applying algorithms and each module is sequentially connected as a prerequisite for the next module. The proposed framework proved the effectiveness of ESNI system integration in the experiment section by evaluating the results in the testing environment. By a series of comparative simulations, the agent can quickly build the knowledge representation of the unknown environment, plan the actions accordingly, and perform some basic tasks sequentially. In addition, we discussed some common failures and limitations of the proposed framework.
引用
收藏
页数:18
相关论文
共 42 条
[1]  
[Anonymous], 2013, T ASSOC COMPUT LING, DOI [DOI 10.1162/TACL_A_00220, DOI 10.1162/TACLA00220]
[2]  
Bai S, 2017, IEEE INT C INT ROBOT, P2379, DOI 10.1109/IROS.2017.8206050
[3]  
Binos Tania, 2021, Australasian Journal of Information Systems, DOI 10.3127/ajis.v25i0.2845
[4]  
Bormann R, 2016, IEEE INT CONF ROBOT, P1019, DOI 10.1109/ICRA.2016.7487234
[5]  
Bundy A., 1984, Breadth-first search, DOI DOI 10.1007/978-3-642-96868-625
[6]  
Collins T, 2007, MED C CONTR AUTOMAT, P1592
[7]  
Dibia V, 2020, Arxiv, DOI arXiv:2007.15211
[8]   Path planning with modified A star algorithm for a mobile robot [J].
Duchon, Frantisek ;
Babinec, Andrej ;
Kajan, Martin ;
Beno, Peter ;
Florek, Martin ;
Fico, Tomas ;
Jurisica, Ladislav .
MODELLING OF MECHANICAL AND MECHATRONIC SYSTEMS, 2014, 96 :59-69
[9]  
Fermin-Leon L., 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA), P2554, DOI 10.1109/ICRA.2017.7989297
[10]  
Fickinger A, 2021, Arxiv, DOI arXiv:2107.07394