## 2 State of the Art  
Networking of wireless mobile robot nodes has been well-studied in the literature [17, 18, 19, 20, 21, 22, 23, 24]. In particular, the use of relay nodes with principal features, such as self-configuration, self-healing and wireless tethering, is exploited in [13, 16, 20, 21]. For instance, [PERSON] _et al._[18] propose a global search-based method, adopting a stochastic model for mobility planning of a group of robotic nodes to increase robustness in wireless networking. The role of the RSS has been significant in most of the research in wireless communication performance optimization. Much research has been done on using the RSS in multi-robot wireless tethering and wireless localization [19, 22, 23, 25]. In [22], a data-driven probabilistic model is used for RSS-based localization and wireless tethering algorithms. Radio source seeking techniques have been studied using RSS gradient [15, 19] and angle of arrival methods [25]. Gradient-based methods have proven to be promising for improving the wireless connectivity of mobile nodes [15, 17, 19, 26]. The authors in [17] used aerial robots as relay nodes and used antenna diversity for autonomously positioning the relay node using a gradient search technique, _i.e._, sampling the RSS from multiple antennas (in different directions) that are connected to a receiver and obtaining the direction of the highest RSS. [PERSON] _et al._[27] also study the use of antenna spatial diversity in building wireless coverage maps and using it for optimal deployment of a wireless network. We have previously demonstrated in [15] that the temporal influence in RSS measurements cannot be neglected, and therefore, using time-independent approaches could boost the spatial diversity performance (in comparison to the time-dependent approaches used in [17, 27]). Most of the research solutions possess drawbacks, such as the use of customized hardware with changes at the physical layer in the network stack of the wireless router (for instance, using a motorized base for rotating directional antennas). The dependency on customized hardware design could be avoided by using algorithms acting directly in the application layer that can easily be integrated and shared. To the best of the authors knowledge, previous research had not utilized multiple receivers (for redundancy and spatial RSS sensing) and time-independent methods for improving the RSS [15]. In this paper, we borrow the concepts from [16, 17, 23, 27] while intending to remove the hurdles and drawbacks observed in these state-of-the-art methods. The proposed method is novel in the following way compared to the state of the art (which also shows how we depart from others): no prior knowledge or model of the radio environment is required; a combination of spatial and temporal pre-processing techniques is used to deal with the measurement noise and stochastic nature of the RSS and to mitigate fading effects; multiple receivers spatially distributed on-board the relay node are used instead of using multiple antennas connected to the same receiver, therefore avoiding the need to switch between different antennas connected to the same receiver and also making the algorithm applicable directly at the application layer. We evaluate our proposed algorithm with simulations and experiments using a position controller on a robot that can work under both LOS and NLOS conditions and even when the RSS gradient measurements are noisy. It has been shown in [28] that using multiple sensors improves redundancy and reduces the data loss rate. It is worth noting that the application of interest in [28] is a static bio-medical data collection system, and all of the redundant devices are widely spread, whereas in our method, all of the redundant sensors are placed in one moving (relay) node. Nevertheless, the redundancy advantage can still be proven, as shown in [27], that by using a multi-antenna (in our case, multi-sensor) spatial RSS sampling method, wireless network capacity could be improved.