## 4 Result of the Systematic Literature Review
### Research Strategy Analysis
#### 4.4.2 Behavior-Based Approach  
The idea of behavior-based SR is inspired by the implementation of behavior-based AI theory using an architecture called the \"subsumption architecture\" by [PERSON] et al. (2018). The robots' behaviors \"subsume\" each other depending on the results from a variety of inputs, such as vision and pressure sensor data. At any given time, only one behavior will be activated. The active behavior varies based on the gained sensor data. One of the earliest works of behavior-based formation control was applied to groups of robots by [PERSON] and [PERSON] [PERSON] and [PERSON] (2018). Their work successfully integrated formation behaviors with navigation and obstacle avoidance both in simulation and on a set of land-based ground vehicles. The behavior-based concept has continuously been developed throughout the years and has been implemented in the SR fields. [PERSON] and [PERSON] in [PERSON] and [PERSON] (2018) proposed a swarm robot searching strategy of multiple targets in obstructive environments. The searching strategy was named as group explosion strategy (GES) and has been inspired by the explosion phenomenon behavior in nature. Each swarm agent is self-adaptively divided into small groups and performs the target searching process independently. Through its advantages of quick convergence from intra-group cooperation and capability of multiple targets searching in parallel from intergroup cooperation, GES simulation outputs portray a great efficiency in energy consumption and target searching time. The proposed strategy also shows great stability in obstructive environments. Research work performed by [PERSON] and [PERSON] in [PERSON] and [PERSON] in [PERSON] and [PERSON] (2018) gives attention to the basic search problem of multi-target search in several conditions of environmental restrictions. They proposed the IGES to solve the multi-target search problem. The strategy was an improved version of the previous GES [PERSON] and [PERSON] (2018) and was inspired by a firework explosion phenomenon behavior with three restriction-handling strategies. The proposed IGES strategy has been tested in the simulation and compared with the GES and RPSO methods. The simulation outcomes show that IGES is more efficient and has great stability during the searching process compares to GES and RPSO methods. The strategy also has greater compatibility with other restriction-handling strategies. A simple cooperative strategy as the baseline of target searching problems with a new type of object (decoys) was proposed by [PERSON] et al. [PERSON] et al. (2018) and was also inspired by the firework explosion phenomenon behavior. In the paper, decoys were also considered as targets that can be sensed but cannot be collected by the robots. This becomes a problem that needs to be considered in the multi-target search task. The method introduced a simple cooperative strategy to solve the problem by comparing it with a non-cooperative strategy as the baseline. The strategies can be integrated with other searching algorithms and provide a solution for avoiding decoys. Simulation results portray that the strategy has almost similar computation overload compared to other strategies, however has better performance in iterations and visiting times of decoys. The strategy shows great adaptiveness to large-scale problems and functions more efficiently when the quantity of decoys or robots increases. The implementation of a \"sweep cleaning\" protocol in a swarm of flying robots has been carried out by [PERSON] et al. [PERSON] et al. (2018). The research implements an idea from sweep cleaning behavior. The \"dynamic cleaners\" problem is one of the attractive applications proposed by the research community in the SI field. The swarm converges in the given area and searches the target area where the contamination spreads. Based on the simulation outcomes, the accuracy of the introduced strategy is 89.8%, and the cleaning time process decreases by 12.87% for each single agent incrementation.