Performance of sensors mounted on a robotic platform for personnel detection

Abstract

Multi-modal sensor suite mounted on a mobile platform such as a robot has several advantages. The robot can be sent into a cave or a cleared building to observe and determine the presence of unwanted people prior to entering those facilities. The robotic platform poses several challenges, for example, it can be noisy while it is in motion. Its electrical activity might interfere with the magnetic and electric field sensors. Its vibrations may induce noise into seismic sensors. We study the performance of acoustic, seismic, passive infrared (PIR), magnetic, electrostatic and video sensors for detection of personnel mounted on a robotic platform such as a Packbot. The study focuses on the quality of sensor data collected. In turn, the study would determine whether additional processing of data is required to mitigate the platform induced noise for detection of personnel. In particular, the study focuses on the following: Whether different sensors interfere with one another operating in close proximity, for example, the effect on magnetic and electrostatic sensors. Comparison of personnel detection algorithms developed for mobile platform and stationary sensor suite in terms of probability of detection, false alarms, and effects of fusion.