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Table 3 Comparisons of localization and navigation system of indoor mobile robots

From: Wi-Fi indoor positioning and navigation: a cloudlet-based cloud computing approach

Author Localization Cognition Operation
Corderio et al. [47] Odometry Line following A robot can autonomously move following the desired trajectory while avoiding detected obstacles based on depth images
Bessa et al. [48] Pattern recognition techniques in omnidirectional images Artificial neural networks in omnidirectional images A robot uses pattern recognition techniques in omnidirectional images to estimate the localization of the robot
Zhang et al. [49] QR code Path planning is performed using the Dijkstra algorithm and dynamic window approach QR codes are used as landmarks to provide global pose references for mobile robot localization and navigation
Uses a Laser Ranger Finder (LRF) to avoid collisions
Mota et al. [50] Cards and RFID reader Line following and dynamics of the Petri nets A robot is equipped with three infrared sensors to detect and follows a black line connecting each card
A robot moves until it passes over cards with RFID
At each intersection, a robot performs actions, such as turning right or left according to the map defined in its algorithm. Next, it goes straight to the next card
Our work A cloudlet-based cloud computing approach Path planning is made by Dijkstra algorithm and consists of Internet Protocols (IPs) address of APs A robot is equipped with Raspberry Pi as a wireless access point to connect to APs
Cloudlets are deployed at APs A robot moves until it reaches stable segment of AP defined in its path planning
At each stable segment of AP, a robot performs actions, such as turning right or left or going straight according to movement decision algorithm