# Signal Source Localization of Multiple Robots Using an Event-Triggered Communication Scheme
## 1 Introduction  
Signal source localization can be widely found in nature and society [1; 2; 3; 4; 5; 6; 7]. For example, some bacteria are able to find chemical or light sources through the perception of the external environment [1]. Moreover, reproducing this kind of behavior in mobile robots can be used to perform some complex missions such as monitoring environments [2; 3; 8; 9], searching and rescuing victims [10], and so on. How to deal with the problem of signal source localization has attracted increasing interest from scientists and engineers and involves two aspects of study. One aspect is to estimate the possible positions of signal sources, while the other aspect is to control robots to locate signal sources based on the estimated positions [2; 3]. For a single robot, some approaches have been proposed for the problem of signal source localization. For example, in [11], the SPSA (Simultaneous Perturbation Stochastic Approximation) method was designed to control the mobile robot to locate a signal source. In [12; 13], the extremum seeking technique, originally developed for adaptive control, was also applied in signal source localization. In [14], a source probability estimation approach was proposed to control the robot to locate the signal source by using the information on signal strength and direction angle. However, the aforementioned approaches need the robot to take more time to collect measurements at different locations. Moreover, some search trajectories generated by these approaches are usually unnecessary. Compared with the single robot, due to the wide detection range and simultaneous sampling, multi-robot systems have received much attention for the problem of signal source localization (see Figure 1) [15; 16; 17; 18; 19; 20; 21]. Usually, the integrated gradient estimation of the signal strength distribution is a common method to estimate the possible position of the signal source, which means that multiple robots simultaneously obtain the measurements at different locations and give the movement direction such that some unnecessary trajectories are neglected [18; 22; 23]. For example, in [18], [PERSON] approximated the signal strength gradient at the formation centroid via a Finite-Difference (FD) scheme and proposed distributed control strategies for localizing a noisy signal source. In [2], [PERSON] used a radial basis function network to model the search environment and guided the robots to move toward the signal source based on gradient information provided by the environment model. Correspondingly, some cooperative control approaches [2; 3] have been developed in terms of consensus control theory [23; 24; 25; 26]. Moreover, the idea of cooperative control is further extended to deal with the management of crisis situations [27]. For example, in [28], [PERSON] proposed a coordination approach among citizens for locating the sources of problems by using peer-to-peer communication and a global map. It should be pointed out that two issues may arise in the aforementioned approaches for the problem of signal source localization. One issue is that the gradient estimation method is easily influenced by noises so as to fall into local optima [29]. For this issue, a particle filter approach can be employed to deal with the uncertainty problem raised by noises. The other issue is that the communication resources in multi-robot systems are constrained, i.e., each robot has a limited communication bandwidth. For this issue, an event-triggered scheme can be used to reduce communication times for each robot. It is worth mentioning that there are some event-triggered rules that have been proposed [2; 30; 31; 32] for multi-robot systems. However, these kinds of event-triggered rules only save computational resources. For multi-robot systems, continuous communication schemes still need to be used to hold system stability.