Robotic explorers are essential tools for gathering information about regions that are inaccessible to humans. For applications like planetary exploration or search and rescue, robots use prior knowledge about the area to guide their search. Ergodic search methods find trajectories that effectively balance exploring unknown regions and exploiting prior information. In many search based problems, the robot must take into account multiple factors such as scientific information gain, risk, and energy, and update its belief about these dynamic objectives as they evolve over time. However, existing ergodic search methods either consider multiple static objectives or consider a single dynamic objective, but not multiple dynamic objectives. We address this gap in existing methods by presenting an algorithm called Dynamic Multi-Objective Ergodic Search (D-MO-ES) that efficiently plans an ergodic trajectory on multiple changing objectives. Our experiments show that our method requires up to nine times less compute time than a naïve approach with comparable coverage of each objective.

This paper applies channel sounding measurements to enable physical-layer security coding. The channel measurements were acquired in an indoor environment and used to assess the secrecy capacity as a function of physical location. A variety of Reed-Muller wiretap codes were applied to the channel measurements to determine the most effective code for the environment. The results suggest that deploying physical-layer security coding is a three-point design process, where channel sounding data guides 1) the physical placement of the antennas, 2) the power settings of the transmitter, and 3) the selection of wiretap coding.

In this paper we provide a real-world test of physical-layer security using channel sounding techniques in an indoor wireless network. We consider the possibility of exploiting issues that arise in practical receivers to manufacture a discrete memoryless wiretap channel model from the Gaussian case, and show how the secrecy capacity of the Gaussian wiretap channel model changes as these issues are considered. Results indicate that the secrecy capacity is a function of the manufactured channel model, physical antenna location, and power settings at the transmitter. These parameters can be optimized to maximize the potential for secrecy in the network.