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Phadke et al. Intell Robot 2023;3:453-78 https://dx.doi.org/10.20517/ir.2023.27 Page 461

Table 3. Categorization of referenced studies by the major resilience module/component that they consider (adversarial
environment)
Resilience component/module highlighted Referenced study
Area coverage [45]
Agent security (physical) [40,42,44]
Path planning, collision avoidance [40,43]
Agent property (heterogeneity) [44,45,47]
Resource allocation/task reassignment [44,45]
Formation control [41,46]
Network security [50]

This technique can also be scaled and applied to swarm systems to track cooperative swarm agents for
collision avoidance and external dynamic and static obstacle avoidance. A similar technique using visual
sensing has been used in to detect cooperative UAVs in swarms. This is an effective method for inter-
[43]
agent collision avoidance, and the technique can also be expanded to track any external UAV entering the
proximity of the swarm. This is especially useful in perimeter protection and defense strategy, where a
swarm of UAVs can effectively form a perimeter around an area to be protected. Any external UAV
attempting entry can be detected and actively tracked for other defensive establishments to destroy. Pursue-
evader applications using UAV agents are also a possibility in the military domain. Applications involve the
use of UAV swarms to collectively pursue other UAV targets to jam their communications, impede
progress, or intentionally collide with them to bring them down. Development in this field is ongoing, but
[44]
innovative work was done in that combines evader-pursuer algorithms with the possibility that the two
parties being tracked may be heterogeneous in terms of their flight capabilities and accounts for it by
proposing Apollonius algorithms to efficiently detect evaders by resource allocation.

Combination studies such as this comprehensively address agent heterogeneity, resource allocation, and
swarm security components under one application scenario. A similar study conducted in proposes
[45]
autonomous unmanned heterogeneous vehicles for persistent monitoring in defense and monitoring high-
value targets such as military installation camps. Using a variety of quadcopters and fixed-wing agents, the
proposed framework can also track static and dynamic ground targets. When entering adversarial
environments, it can be expected that UAV swarms may lose connection with ground control or space
segments, resulting in temporary or permanent control or navigation signal loss. The key focus was the
development of enabling technology to address task assignment, coverage, and swarm management policies
in such scenarios. Bearing-based formation control methods, such as [46,47] , may use neighboring agents,
ground control planes, and tertiary data to align themselves and prevent immediate mission failure. This
allows both ground control and the swarm additional time to attempt signal reconnection. While some
methods study single-space operational swarms only, certain approaches expand the formation control and
management policies to multi-operational space heterogeneous agents . However, it cannot be assumed
[47]
that all heterogeneous agents are for support purposes. In problems such as these, the heterogeneous agents
are the advertisers of the UAV swarm. With the rapid development of surface-to-air missiles, swarms also
have to consider the occurrence of land-based malicious entities such as missiles and jammers that are
focused on damaging aerial swarms. A consensus algorithm is proposed in for a swarm of herding UAVs
[48]
that have to deal with land-based anti-aircraft vehicles. Continuous tracking of heterogeneous targets is
such a broad domain that it requires additional development, as demonstrated in .
[49]

While secure network communication is a basic requirement of all swarms, both energy-efficient and secure
UAV communications are a primary concern during warfare. Working in conjunction with anomaly