Mobile sensor nodes are an ideal solution for efficiently collecting measurements for a variety of applications. Mobile sensor nodes offer a particular advantage when measurements must be made in hazardous and/or adversarial environments. When mobile sensor nodes must operate in hostile environments, it would be advantageous for them to be able to avoid undesired interactions with hostile elements. It is also of interest for the mobile sensor node to maintain low-observability in order to avoid detection by hostile elements. Conventional path-planning strategies typically attempt to plan a path by optimizing some performance metric. The problem with this approach in an adversarial environment is that it may be relatively simple for a hostile element to anticipate the mobile sensor node’s actions (i.e. optimal paths are also often predictable paths). Such information could then be leveraged to exploit the mobile sensor node. Furthermore, dynamic adversarial environments are typically characterized by high-uncertainty and highcomplexity that can make synthesizing paths featuring adequate performance very difficult. The goal of this work is to develop a path-planner anchored in info-gap decision theory, capable of generating non-deterministic paths that satisfy predetermined performance requirements in the face of uncertainty surrounding the actions of the hostile element(s) and/or the environment. This type of path-planner will inherently make use of the time-tested security technique of varying paths and changing routines while taking into account the current state estimate of the environment and the uncertainties associated with it.
© 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
anti-tamper; anti-theft; cyber-physical security; ground robot; info-gap decision theory; mobile sensor nodes; robotics; unmanned systems