Macroergonomic Perspective of Unmanned Aerial Systems (UAS) Aided Search and Rescue (SAR)

Unmanned aerial systems (UAS), or drones, are widely used in military and commercial industries (Van Tilburg, 2017). UAS has recently been applied to Search and rescue (SAR) missions, resolving many of the original SAR missions issues, such as reaching hard-to-reach areas under extreme weather and reducing time and cost (Dinh et al., 2019). As technology advances, there are also various types of search methods. For example, SARPlan is a decision support system to increase the likelihood of locating the missing person, and CentWits is GPS that uses radiofrequency to determine the most probable location (Niedzielski et al., 2018). Along with other technology like infrared and thermal detection, SAR missions are becoming more successful. A few missing people were successfully found with the help of drones (McRae et al., 2019; Van Tilburg, 2017). 

Because there are multiple types of searching tools to choose from, the operation of UAS requires the cooperation of humans and technology. UAS can be considered an asset when conducting SAR. However, there are also failed cases when humans fail to find the missing persons due to flawed system design, lack of advanced technology, and other uncontrollable circumstances (Drone U, 2018). For this case study, first, SAR using UAS as a sociotechnical system will be explained. Weaknesses and potential issues will be identified using the classifications suggested by Bouarfa et al. (2013), along with recommendations and future directions will be discussed.

System Analysis

Socio-technical system and components

SAR missions do not involve only a group of rescuers to look for missing people. SAR with drones requires collaboration and communication between the Federal Aviation Administration (FAA), as they regulate drones, law enforcement, first responders, coordinators, technology team, pilots, the drones, family of the missing person, and the missing person (Drone U, 2018; McRae et al., 2019). As Bouarfa et al. (2013) define, a complex socio-technical system involves multiple interconnected parts, each with challenging to predict behavior and many stakeholders. Although the mission's ultimate goal is to locate and save the missing person, each component has various responsibilities and goals. For example, the FAA concerns if the drones are within regulation. Law enforcement concerns about the privacy and rights of nearby residents. The family wants the SAR team to do whatever they can to find the person. With the unpredictable nature of each case and circumstance, these goals will further drive what Bouarfa et al. (2013) called the difficult to predict behavior.

Lifecycle of the work system

When the SAR is called for, depending on the case's complexity, the coordinator can choose to deploy single or multiple UAS (Shakhatreh et al., 2018). The SAR team defines the search region (Shakhatreh et al., 2018). This involves gathering information about the missing person from the family (Drone U, 2018). Then the right tool is selected to use with drones that start scanning the target area. The images and videos are then sent to the Ground Control System (GCS), then analyzed by the team to decide which area manned resources should go. A multiple UAS mission involves the same process but requires communication between the drones and GCS to decide and coordinate the optimal trajectory. When the rescuers go out to the field with law enforcement to locate the missing person (Drone U, 2018).

Weaknesses and Recommendations

Emergences

Nominal emergence

Bouarfa et al. (2018) identify four levels of emergence, ranging from predictable to not predictable, even in principle for an air traffic management system. Some system variances and weaknesses involving operator, UAS, and system interactions can be classified into these types of emergence. Nominal emergence represents the simplest level of emergence where everything is predictable. One example of nominal emergence in UAS aided SAR missions that create some system predictable variation is the types of tools available to use along with drones. As mentioned, multiple types of technology are available to use with drones to identify a missing person, including thermal, vision, and other locating tools (Niedzielski et al., 2018). Human interaction with the machine is constant with the occurrence of nominal emergence. One assumption of classifying the various tools available under nominal emergence is the pilot's and coordinators’ basic understanding of the drones and tools available. Therefore, to reinforce their knowledge and ensure they have fundamentals of which tools to utilize under which scenarios, training is necessary. Ribeiro et al. (2021) demonstrate improved UAS operation with real-time AR training. With simulation training, operators can adapt to uncertain environments and increase their familiarity with the different types of tools available.

Weak emergence

Weak emergence is the top-down influence from the organization, which is somewhat predictable in principle (Fouarfa et al., 2018). One weak emergence in the UAS aided SAR missions is the coordinator's commands, especially when the mission includes multiple UAS deployments. Although the coordinators have shared knowledge with the pilots with everyone in the team, they can exhibit uncertainties that ultimately contribute to the system due to decision-making habits and perception of various situations. One example being culture's influence on an individual's risk perception (Marshall, 2020). Other experiences, training, and upbringing can all contribute to differences in judgment of the same situation. Because pilots' actions are based on the coordinators' commands, the commands ultimately affect how the pilots will operate the drones. 

Similarly, another weak emergence of pilots flying drones is that pilots are primarily volunteers. Many of the SAR missions are volunteer-based (Niedzielski et al., 2018). Although the FAA has guidelines and certificates required for drone pilots (Federal Aviation Administration, 2021), as with the coordinators, pilots' background training, experiences, and cultural upbringing can affect their performance and attitude about the missions; hence, their interaction with the UAS. Another human factor that relates to the pilots is emotions. As Drone U (2018) points out, some pilots exhibit emotions during rescue missions. This could be attributed to personality, but more significantly by experience. Past research shows that rescuers can have altered emotional states during emergency rescue missions. Individual factors such as professional knowledge, training, experience, and personality all affect the emotional perception of rescuers; unstable emotions can then lead to emotional behavior and altered cognitive responses (Lu et al., 2014). As with other recommendations, Lu et al. (2014) also report that enhancing professional knowledge and training can effectively reduce such influences that can lead to unpredictable behaviors of rescue workers when operating UAS.

Multiple emergence

As the UAS aided SAR missions involve multiple components, multiple emergence is observed in every rescue mission with unpredictable factors. Multiple emergence is represented by multiple feedback loops that are not predictable and chaotic (Niedzielski et al., 2018). The major uncertainty that adds to the complexity of the system is the weather. Weather conditions are a continuous challenge to UAS because they cause deviations in the coordinated sets of predetermined paths (Shakhatreh et al., 2018). This sudden alteration in path leaves pilots no time to prepare, exacerbated if they are unfamiliar with the procedure or toolsets. In some cases, the missions might fail due to adverse weather as UAS is limited in the environment with tight canyons and thick trees (Van Tilburg, 2017). Environmental hazards present another difficulty in predicting the nature of the mission. UAS should avoid environmental hazards and collisions with other UAS (Waharte & Trigoni, 2010). This can depend on the weather or years of experience of the pilot.

Aside from the weather, the story of the missing person also differs with each mission. The story and the reason for going on the trip will affect how the SAR team will approach the situation. For example, if a person chooses to go missing because they are attempting to commit suicide, the mission will be much more difficult because they do not want to be found (Drone U, 2018). Depending on the situation, if more information is received from the family regarding the missing person, the coordinator could decide to deploy more UAS to the field, affecting the trajectory and communication between each pilot and the entire team. Multiple emergence cannot be prepared for as multiple emergence is by definition unpredictable and chaotic. The most common and effective way to prepare for unpredictability is to introduce unpredictability in regular training. Landsberg et al. (2012) propose using simulated training with added uncertainty that adapts as the operator's knowledge enhances. They also point out that training should focus on operators and the training system itself as different instructional interventions are best for various types of tasks and learning objectives, such as decision-making or motor skills. 

Strong emergence

Strong emergence represents the appearance of a completely new system and is not predictable, even in principle (Niedzielski et al., 2018). The greatest weakness out of the control of anyone in the UAS aided SAR missions is the advancement of technology, which presents itself as another complex sociotechnical system. Drones are capable of flying over hard-to-reach areas for manned resources. However, technical limitations such as image capturing, processing, scanning, and other infrared or thermal technology require the help of other researchers and present as the most significant challenge for UAS in SAR missions until today. The deficiency of technology limitation can cause a decrease in accuracy or a delay in locating the person. As Shakhatreh et al. (2018) state, with the current technology, after the UAS finishes capturing images, human rescuers scan the images with human eyes, identifying any possibility of living humans, and deploy manned resources. Drone U (2018) further discusses the difficulty and human limitation in scanning and identifying humans in bird-eye view a few hundred feet above the ground. These difficulties lead to mistakes and omission, and eventually failure of the mission (Drone U, 2018). 

As strong emergence is entirely out of the control of the SAR complex sociotechnical system, human operators can only focus on the currently available set of tools and excel utilizing those tools as much as possible. Limited technology becomes the immediate research trend, and future direction for improving UAS aided SAR missions. One area of study is implementing machine learning (ML) in image capture, determining the exact frame at which the person might be located, increasing accuracy and efficiency (Shakhatreh et al., 2018). Energy limitation is another weakness that slows the mission. Battery life for drones provides less than one hour of continuous operation (McRae et al., 2019). This requires a longer mission duration if the coverage area is large, further delaying identifying the missing person and interrupting pilots’ operations. 

Conclusion

As UAS becomes more accessible, it is also applied to SAR missions. UAS decreases the cost and increases the efficiency of SAR. More importantly, it places rescuers in less danger while increasing the likelihood of locating the missing person as UAS allows scanning a greater coverage of the area. However, as with many other operative missions, UAS aided SAR as a complex and dynamic socio-technical system. Human behaviors and the overall system evolution are highly unpredictable, affecting how human operators from different components interact with each other or use the technology. The weaknesses are divided into four levels. For the two predictable levels and even multiple emergence, simulation with uncertainty training is the most effective in preparing the operators to act under uncertainty and for the system to operate smoothly. Examples of strong emergence in UAS aided SAR missions are mainly represented by technology limitations that prevent rescuers from utilizing the technology to the fullest potential, which should be the future area of study for UAS aided SAR.

References

Bouarfa, S., Blom, H. A. P., Curran, R., & Everdij, M. H. C. (2013). Agent-based modeling and simulation of emergent behavior in air transportation. Complex Adaptive Systems Modeling, 1(1), 1-26. https://doi.org/10.1186/2194-3206-1-15

Chen, J., Li, S., Liu, D., & Li, X. (2020). AiRobSim: Simulating a multisensor aerial robot for urban search and rescue operation and training. Sensors (Basel, Switzerland), 20(18), 5223. https://doi.org/10.3390/s20185223

Dinh, T. D., Pirmagomedov, R., Pham, V. D., Ahmed, A. A., Kirichek, R., Glushakov, R., & Vladyko, A. (2019). Unmanned aerial system–assisted wilderness search and rescue mission. International Journal of Distributed Sensor Networks, 15(6), 155014771985071. https://doi.org/10.1177/1550147719850719

Drone U. (2018, July 18). BONUS: How to conduct search and rescue missions with drones. YouTube. https://www.youtube.com/watch?v=dklwfDaiFjU

Federal Aviation Administration. (2021, May 19). Become a Drone Pilot. https://www.faa.gov/uas/commercial_operators/become_a_drone_pilot/

Landsberg, C. R., Astwood, R. S., Van Buskirk, W. L., Townsend, L. N., Steinhauser, N. B., & Mercado, A. D. (2012). Review of adaptive training system techniques.Military Psychology, 24(2), 96-113. https://doi.org/10.1080/08995605.2012.672903

Lu, J., Yang, N., Ye, J., & Wu, H. (2014). The influence paths of emotion on the occupational safety of rescuers involved in environmental emergencies- systematic review article. Iranian Journal of Public Health, 43(11), 1478-1485. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4449496/pdf/IJPH-43-1478.pdf

Marshall, T. M. (2020). Risk perception and safety culture: Tools for improving the implementation of disaster risk reduction strategies. International Journal of Disaster Risk Reduction, 47, 101557. https://doi.org/10.1016/j.ijdrr.2020.101557

McRae, J. N., Gay, C. J., Nielsen, B. M., & Hunt, A. P. (2019). Using an unmanned aircraft system (drone) to conduct a complex high altitude search and rescue operation: A case study. Wilderness & Environmental Medicine, 30(3), 287-290. https://doi.org/10.1016/j.wem.2019.03.004

Niedzielski, T., Jurecka, M., Miziński, B., Remisz, J., ŚLopek, J., Spallek, W., Witek-Kasprzak, M., Kasprzak, U., & Świerczyńska-Chlaściak, M. (2018). A real-time field experiment on search and rescue operations assisted by unmanned aerial vehicles. Journal of Field Robotics, 35(6), 906–920. https://doi.org/10.1002/rob.21784

Ribeiro, R., Ramos, J., Safadinho, D., Reis, A., Rabadão, C., Barroso, J., & Pereira, A. (2021). Web AR solution for UAV pilot training and usability testing. Sensors (Basel, Switzerland), 21(4), 1456. https://doi.org/10.3390/s21041456

Shakhatreh, H., Sawalmeh, A., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., Othman, N. S., Khreishah, A., & Guizani, M. (2018). Unmanned aerial vehicles: A survey on civil applications and key research challenges.https://doi.org/10.1109/ACCESS.2019.2909530

Van Tilburg, C., MD. (2017). First report of using portable unmanned aircraft systems (drones) for search and rescue. Wilderness & Environmental Medicine, 28(2), 116-118. https://doi.org/10.1016/j.wem.2016.12.010

Waharte, S., & Trigoni, N. (2010). Supporting search and rescue operations with UAVs. Paper presented at the 142-147. https://doi.org/10.1109/EST.2010.31