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Page 468 Phadke et al. Intell Robot 2023;3:453-78 https://dx.doi.org/10.20517/ir.2023.27

of relevant resilient metrics to measure incorporated resilience are important steps of such swarm
[97]
development. Distributed sensing using multiple sensors in a target area has been examined in . Optimal
area coverage, as discussed above, recognizes a tradeoff between the area to be covered and inter-agent
network strengths. Additionally, approaches may address the issue of preventing multiple UAVs from
covering the same area as a way of addressing the maximum area coverage problem. By reducing repeat
passes, one can decrease the time it takes for a swarm to completely survey an area, particularly in scenarios
where a single pass data collection flight is perceived to be enough. Addressing task assignment with
[98]
optimal coverage, a study proposed a unique optimized waypoint-defining technique.
Area considerations are not only limited to maximum area coverage problems. Persistent interactions with
[99]
the environment are also required for extremely specific applications such as in , which uses LiDAR data
collected during the survey to also calculate emergency landing spots for the UAV to land in case of any
occurrence where the UAV needs to land but is unable to return to base. A combination of onboard data
processing in real-time using resource allocation and area coverage is addressed here. Such
implementations, when scaled to larger swarms, can help assist swarm agents facing issues to land in
optimal zones rather than losing control altogether. Another issue that comes with long-term persistent
interactions with the environment is energy efficiency. A swarm that performs quick operations over a small
region requires significantly less power than one that operates over a considerable time or area. The power
management and fuel source systems on such deployments may be adjusted proportionally. Long-term
surveillance, reconnaissance, and mapping functionality may require advanced energy and fuel optimization
techniques. This can be accomplished by either designing fuel stations or recharging locations at strategic
locations that enable agents to land and refuel . This involves the collaboration of optimization techniques
[100]
for maximum area coverage by minimum fuel stations, plus the design of appropriate energy-aware
protocols .
[101]

Surveying using remote sensing and photogrammetry principles often involves the usage of sensors to
create extensive point clouds of the surveyed area for analysis and further processing. A study by used
[102]
heterogeneous unmanned robotic systems to propose a framework for the registration and segmentation of
point clouds of complex terrain. They use a multi-module system where a combination of UAV-UGV each
produces point clouds. The UGV uses a laser range finder, and the UAV produces a point cloud from
images using SfM (Structure from Motion) photogrammetry. A collaborative mapping scenario between
Micro UAV-UGV [103,104] is another such example where different operational space vehicles collect and
augment data for richer mapping outputs. Such implementations require robust formation control and data
exchange and fusion policies to be in place. In this study, key areas of resilience incorporation are
communication, navigation, and resource allocation. Observations to support the claims of this study are
validated when such novel methodologies implement robust algorithms for processing sensor fusion and
communication, whereas omitting developments to the physical or cyber security of swarms. Survey
missions may use behavior-based control for network optimization, positioning, and even for the loss of a
portion of the swarm, creating robust packages of disruption handling mechanisms. This can improve the
efficiency of surveys in terms of time taken and data quality. It is interesting to note that by the classification
set by this study, while victim detection in a post-earthquake scenario would be classified as a SAR target
problem and be placed in the previous section, detection, and survey of damage done to structures and
infrastructure would be classified in this section. Figure 14 shows a UAV swarm conducting an
infrastructure examination operation.

This again highlights the variety of UAV applications and their specific resiliency requirements. While SAR
problems are exploratory and time-constrained, problems such as for building damage assessment need
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