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Phadke et al. Intell Robot 2023;3:453-78 https://dx.doi.org/10.20517/ir.2023.27 Page 469

Figure 14. An unmanned aerial vehicle (UAV) swarm examining a road for cracks and potholes.

high-quality and accurate sensor data for real-time and post-processing analysis. As such, their resilience
requirements may entirely vary. Area coverage problems are addressed in this study, where efficient data
extraction is required from oblique photos. As survey timelines go, it might not be possible to accurately
cover damaged areas without the effect of sensor tilt, whereas efficient area coverage might require
gathering data for larger areas using predefined points only, resulting in oblique sensor readings. It is then
necessary to post-process the data to extract the maximum accuracy information from the sensor
measurements.

3. NON-APPLICATION SPECIFIC AND FLEXIBLE DEVELOPMENTS
This section highlights methodologies that are not specifically focused on UAV swarms or for specific
application scenarios. However, they do propose unique methodologies for dealing with the many
challenges that UAVs face during operation. These could be applied to any of the scenarios discussed above
as needed to create more efficient outputs. The advantage of examining such research is that they have been
developed with a generalized outlook on the problem statement. Thus, they can effectively be scaled and
applied to any application-specific scenario to effectively increase overall resiliency. Table 6 categorizes
generalized work on UAV implementation that increases operational resilience.

This section covers all the modules that were recognized in UAV swarm operations above. Formation
control of swarm agents, as they move in the operational space, is a vital area for resilience integration.
Inter-agent collisions can lead to a cascaded failure of the entire swarm. An increase in the distance between
agents as they navigate obstacles can also impact connectivity between them. Implementations such as those
in article introduce formation control appr Self-organization is an important characteristic of UAV
[106]
swarms and involves the ability of agents to recognize other agents as those of the swarm itself or outsiders.
This awareness leads to the development of better formation policies and inherent security against external
agents. Evolutionary hybrid algorithms have seen a high rate of success for such ideas.
[107]
Task planning for agents with distinctive characteristics and goals involves interfacing multiple protocols
with each other. One such example is article , which addresses task planning problems for a swarm of
[108]
heterogeneous UAVs, where the swarm agents are defined as having different operational capabilities. A
multi-type-task allocation algorithm is introduced that considers different mission requirements and the
individual ability of each agent of the swarm, thus addressing the task planning module along with the agent