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Phadke et al. Intell Robot 2023;3:453-78 https://dx.doi.org/10.20517/ir.2023.27 Page 463

Table 4. Categorization of referenced study by the major resilience module/component they consider (SAR)
Resilience component/module highlighted Referenced study
Network coverage [69,76]
Area coverage [68,69,76]
Path planning, collision avoidance [62,79]
Agent property (heterogeneity) [68,79]
Resource allocation/task reassignment [80]
Formation control [68,76]
SAR: search and rescue.

Figure 9. A differentiation between swarm-specific and application-specific search and rescue (SAR).

building rubble, forests, and water. The last known location of the missing person is often triangulated and
searched manually. Post-disaster locations are typically manually and meticulously gone through for days to
look for live victims trapped or injured. Due to the nature of such scenarios, time constraints are of the
utmost importance. The advent of remotely operated robots on land, water, and air has rapidly seen their
inclusion in SAR missions. Often, a swarm of such robots can effectively cover a larger area in less time.
Additionally, multiple passes over a single area are possible as an added advantage. Target detection using
sensors is the most prevalent choice for this methodology, with vision sensors being the primary choice for
victim detection [65,66] . Speed and efficiency factors of a SAR operation can depend on the extent of the
environmental knowledge of the search area.

Swarm agent heterogeneity can be implemented in many ways via the choice of swarm hardware, area of
operation, and agent characteristics. A UGV (Unmanned Ground Vehicle) can provide efficient and low-
error information such as terrain, surface, and elevation, including the presence of obstacles and their
[67]
dimensions . Multiple quadcopters performing post-tsunami swarming maneuvers to assist in SAR use
control systems that defined simple behaviors based on UAV personality type. This addresses the
[68]
heterogeneity by agent nature of swarms . The speed of victim detection is also an indirect function of the
maximum area coverage. The faster the swarm of drones covers the target area, the higher the probability of
the target being detected. As such, maximum area coverage optimization problems using mobile nodes and
the associated network coverage problem need to be addressed. Adjacent agents need to ensure that they