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Page 462 Phadke et al. Intell Robot 2023;3:453-78 https://dx.doi.org/10.20517/ir.2023.27

detection IDS, a joint resource allocation and secure protocol can work despite experiencing downlinks and
[50]
in the presence of eavesdroppers yet still provide efficient communication support to ground users .
2.2. Search and rescue of targets
While resiliency components highlight the SS-SAR features needed, this section refers to application-
specific SAR (AS-SAR) uses. It is first necessary to establish a differentiation between the two methods. AS-
SAR deals with the process of using a UAV swarm to effectively search an area for a specific target. Such
target search examples include searching for lost or trapped miners in underground mines , detecting
[51]
forest fires [52-59] , and marine rescue scenarios [60-63] . The segregation of the two approaches is summarized in
Figure 9.

SS-SAR is a different research area from AS-SAR. While both are exploratory problems, the former is
concerned with internal agents while the latter has the search for an external target as its end goal. SS-SAR
frameworks have the sole purpose of keeping track of the individual agents that make up the swarm. If an
agent of the swarm is lost, the other agents attempt to locate and rescue the fallen agent. It can, thus, be
comprehended that while AS-SAR is a use-case scenario of UAV swarms, SS-SAR is a resiliency component
within itself. The availability and efficacy of SS-SAR frameworks contribute to the overall increase or
decrease in swarm resilience.

AS-SAR varies widely in terms of methodology. Researchers often employ a wide range of approaches,
including game theory, deep learning, and probabilistic approaches, such that it is impossible to condense
them under a single framework. SS-SAR approaches, on the other hand, being a relatively novel field, have a
generalized workflow that has been proposed.

Figure 10 shows an AS-SAR scenario where a UAV swarm coordinates to search a region for a missing
person. Figure 11 depicts an SS-SAR scenario where any swarm agents that fall into distress themselves
while conducting an AS-SAR mission may be rescued. This is done using pose checks, agent well-being
checks, and reconnection protocols. If immediate rescue is not possible, SS-SAR opens up avenues such as
marking the location of the fallen agent for possible retrieval later on. Comparing Figures 10 and 11, a
difference between the two scenarios is realized. Current work by the author focuses on developing the
aforementioned SS-SAR protocols to realize the proposed SS-SAR scenario as a novel area of research.
Figure 12 shows a part of the experiments that the authors are conducting to test distressed agent recovery
in simulated environments .
[64]

[64]
Figure 13 presents an SS-SAR framework for rescuing distressed UAV swarm agents . It comprises several
stages for agent tracking and initiating rescue protocols. The advantage of such frameworks is their modular
nature. Modules can be swapped or upgraded as per factors such as mission requirements and agent
capability. Section 1 uses periodic “hello messages” from agents labeled as HBS (Heartbeat Signals) to track
agent well-being. Further modules perform static and dynamic obstacle checks near distressed agents to
determine causes of failure, followed by higher-level system checks for battery, network connection, and
hardware integrity. The last stages involve agent recovery procedures or loss procedures and task
reassignment.

While it was important to highlight the difference between SS-SAR and AS-SAR, this study focuses on
reviewing AS-SAR research and the various methodologies that have been implemented to make such
scenarios more robust and effective. Table 4 organizes referred works by their major resiliency module
focus. Victim SAR scenarios have often relied on large teams of people searching for the victim through