An Investigation of Search Algorithms for Aerial Reconnaissance of an Area Target


  • Rory Blakenship
  • James Bluman
  • Josiah Steckenrider



Lissajous curves, drones, Unmanned Aerial System (UAS), Optimal Search Patterns


As drone technology becomes increasingly accessible in commercial and defense sectors, it is important to establish efficient ways of employing the technology to leverage its inherent advantages. In the context of a chemical, biological, radiological, and nuclear (CBRN) attack, an unmanned aerial system (UAS) can provide an understanding of the area affected by contaminants in a faster and safer way than a manned reconnaissance mission. Commonly used deterministic paths provide comprehensive coverage but they can require a substantial amount of time to reach each sector within a search space. The recently proposed Lissajous search pattern provides easily tunable parameters that can be adjusted according to the search space and anticipated size of the target. This paper provides an evaluation of Lissajous patterns against canonical search patterns and investigates ways of maximizing their efficiency for various target sizes.


Araujo, J. O., Valente, J., Kooistra, L., Munniks, S., & Peters, R. J. (2020). Experimental flight patterns evaluation for a UAV- based air pollutant sensor. Micromachines, 11(8), 768.

Army, U. S. (1986). FM 3-6 AMF 105-7 FM 7-11-H. Field Behavior of NBC Agents (Including Smoke and Incendiaries).

Echeveste, D., Lee, A., & Clark, N. (2021). Using Spatial Uncertainty to Dynamically Determine UAS Flight Paths. Journal of Intelligent & Robotic Systems, 101(4), 1-16.

Fishburn, P. C. (1980). Stochastic dominance and moments of distributions. Mathematics of operations Research, 5(1), 94- 100.

IMO. (2016). IAMSAR Manual: International Aeronautical and Maritime Search and Rescue Manual.

Kimball, D., & Davenport, K. (2018). Chemical Weapons: Frequently Asked Question. Arms Control Association.

Kopeikin, A., Heider, S., Larkin, D., Korpela, C., Morales, R., & Bluman, J. E. (2019). Unmanned aircraft system swarm for radiological and imagery data collection. In AIAA Scitech 2019 Forum

Martins, G. H. (1993). A new branch-and-bound procedure for computing optimal search paths. Naval Postgraduate School, Monterey CA.

Munera, A. (2019). Chemical, Biological, Radiological, and Nuclear Operations, US Army Field Manual 3-11, Washington, D.C.

Quirk, J. P., & Saposnik, R. (1962). Admissibility and measurable utility functions. The Review of Economic Studies, 29(2), 140-146.

Rahmes, M., Chester, D., Hunt, J., & Chiasson, B. (2018, May). Optimizing cooperative cognitive search and rescue UAVs. In Autonomous Systems: Sensors, Vehicles, Security, and the Internet of Everything (Vol. 10643, p. 106430T). International Society for Optics and Photonics.

San Juan, V., Santos, M., & Andújar, J. M. (2018). Intelligent UAV map generation and discrete path planning for search and rescue operations. Complexity, 2018.

Steckenrider, J. J. (2021). Adaptive Aerial Localization Using Lissajous Search Patterns. IEEE Transactions on Robotics. Steckenrider, J. J., Leamy, S., & Furukawa, T. (2020, November). Cooperative Aerial Search and Localization Using Lissajous

Patterns. In 2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) (pp. 233-240). IEEE.

Wollan, H. (2004). Incorporating heuristically generated search patterns in search and rescue. University of Edinburgh.



How to Cite

Blakenship, R., Bluman, J., & Steckenrider, J. (2022). An Investigation of Search Algorithms for Aerial Reconnaissance of an Area Target. Industrial and Systems Engineering Review, 10(2), 159-165.