Abstract
Indoor robots, in particular AI-enhanced robots, are enabling a wide range of beneficial applications. However, great cyber or physical damages could be resulted if the robots’ vulnerabilities are exploited for malicious purposes. Therefore, a continuous active tracking of multiple robots’ positions is necessary. From the perspective of wireless communication, indoor robots are treated as radio sources. Existing radio tracking methods are sensitive to indoor multipath effects and error-prone with great cost. In this backdrop, this paper presents an indoor radio sources tracking algorithm. Firstly, an RSSI (received signal strength indicator) map is constructed based on the interpolation theory. Secondly, a YOLO v3 (You Only Look Once Version 3) detector is applied on the map to identify and locate multiple radio sources. Combining a source’s locations at different times, we can reconstruct its moving path and track its movement. Experimental results have shown that in the typical parameter settings, our algorithm’s average positioning error is lower than 0.39 m, and the average identification precision is larger than 93.18% in case of 6 radio sources.
Highlights
Indoor robots are becoming increasingly popular in the market in view of their beneficial applications, ranging from navigating and sweeping to healthy-caring
All experiments are conducted in Room 701, Communication Hall, Army Engineering University. e floor plan of the room is shown in Figure 3; the size of room is 11.2 m × 10.4 m; and the vertical and horizontal distances between two neighboring monitors are both 0.8 m [17]. e size of established RSSI map is 700 × 525 pixels and the output image’s size is normalized as 227 × 227 pixels to accelerate the training process
A straightforward presentation of the radio source recognition results in one single timeslot is shown in the images output layer in Figure 1(b), where 6 radio sources
Summary
Indoor robots are becoming increasingly popular in the market in view of their beneficial applications, ranging from navigating and sweeping to healthy-caring. In combination with artificial intelligence (AI), they are anticipated to drastically change people’s daily lives. These with advantages, indoor robots still face severe cyber and physical threats due to their hardware and software vulnerabilities [1]. Even the simplest cleaning robots could be manipulated to launch aggressive physical attacks, such as assassinating revealed in [2] and eavesdropping on private conversations in [3,4,5,6,7,8,9]. Even the simplest cleaning robots could be manipulated to launch aggressive physical attacks, such as assassinating revealed in [2] and eavesdropping on private conversations in [3,4,5,6,7,8,9]. erefore, it is essential to track the nonstationary robots for security besides traditional efficiency concerns
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