Robot Navigation Algorithm Based on Sensor Technology and Iterative Maximum a Posteriori Estimation | Zendy

Na Zheng | Zendy; Yanli Du | Zendy; Bai Qing-hua | Zendy

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Robot Navigation Algorithm Based on Sensor Technology and Iterative Maximum a Posteriori Estimation

Author(s) -

Na Zheng,

Yanli Du,

Bai Qing-hua

Publication year - 2019

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2019.p0282

Subject(s) - computer science , wireless sensor network , mobile robot , extended kalman filter , robot , real time computing , maximum a posteriori estimation , a priori and a posteriori , algorithm , odometer , artificial intelligence , kalman filter , maximum likelihood , mathematics , philosophy , statistics , epistemology , computer network

The hybrid sensor network is mainly composed of static and dynamic sensor nodes. The dynamic node is the mobile robot with wireless sensor module installed. This paper proposes a robot navigation algorithm based on sensor technology and iterative maximum a posteriori estimation. It uses Kalman filter and least-squares fitting to improve RSSI measurement accuracy and the mobile robot only needs to use the received signal strength (RSSI) and odometer information to realize autonomous navigation in the sensing area. Moreover, static nodes are randomly deployed in the sensing area without a priori location information. Therefore, this algorithm has the advantages of low cost and ease of deployment. Both simulation and outdoor field experiments show the performance and effectiveness of the algorithm.

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