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We consider the combined problem of frontier exploration in a complex indoor environment while seeking a radio source. To do this in an efficient manner, we incorporate radio signal strength (RSS) information into the exploration algorithm by locally sampling the RSS and estimating the 2-D RSS gradient. The algorithm exploits the local motion to collect RSS samples for gradient estimation and seeks to explore in a way that brings the robot to the signal source. This strategy avoids random or exhaustive exploration. An indoor experiment demonstrates the exploration algorithm that uses this information to dynamically prioritize candidate frontiers and traverse to a radio source. Simulations, including radio propagation modeling with a ray-tracing algorithm, enable study of control algorithm tradeoffs and statistical performance.

This paper considers the question: As a receiver moves along a trajectory, is its received signal strength (RSS) increasing or decreasing? This is an important question for mobile receivers to answer in order to maintain or improve connectivity within the network. When signal strength maps are unavailable a priori, a receiver decides the answer based on its observations. We derive the maximum likelihood estimate (MLE) of signal strength at a particular location in Rayleigh/Rician fading and additive Gaussian noise. We estimate the slope of the gradient by making RSS estimates at multiple locations. By making estimates of the MLE variance, we can derive confidence in the gradient, which can be used to decide future movements.