Sensor selection for detecting deviations from a planned itinerary

Hazhar Rahmani, Dylan A. Shell, Jason M. O'Kane.
Preprint, 2021.


Suppose an agent asserts that it will move through an environment in some way. When the agent executes its motion, how does one verify the claim? The problem arises in a range of contexts including in validating safety claims about robot behavior, applications in security and surveillance, and for both the conception and the (physical) design and logistics of scientific experiments. Given a set of feasible sensors to select from, we ask how to choose sensors optimally in order to ensure that the agent's execution does indeed fit its pre-disclosed itinerary. Our treatment is distinguished from prior work in sensor selection by two aspects: the form the itinerary takes (a regular language of transitions) and that families of sensor choices can be grouped as a single choice. Both are intimately tied together, permitting construction of a product automaton because the same physical sensors (i.e., the same choice) can appear multiple times. This paper establishes the hardness of sensor selection for itinerary validation within this treatment, and proposes an exact algorithm based on an ILP formulation that is capable of solving problem instances of moderate size. We demonstrate its efficacy on small-scale case studies, including one motivated by wildlife tracking.



  title        = {Sensor selection for detecting deviations from a planned itinerary},
  author       = {Hazhar Rahmani, Dylan A. Shell, Jason M. O'Kane},
  booktitle    = {Preprint},
  year	       = {2021},
  note	       = {}

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