###### Abstract  
Cloud robotics and advanced communications can foster a step-change in cooperative robots and hybrid wireless sensor networks (H-WSN) for demanding environments (e.g., disaster response, mining, demolition, and nuclear sites) by enabling the timely sharing of data and computational resources between robot and human teams. However, the operational complexity of such multi-agent systems requires defining effective architectures, coping with implementation details, and testing in realistic deployments. This article proposes X-IoCA, an Internet of robotic things (IoRT) and communication architecture consisting of a hybrid and heterogeneous network of wireless transceivers (H2 WTN), based on LoRa and BLE technologies, and a robot operating system (ROS) network. The IoRT is connected to a feedback information system (FIS) distributed among multi-access edge computing (MEC) centers. Furthermore, we present SAR-IoCA, an implementation of the architecture for search and rescue (SAR) integrated into a 5G network. The FIS for this application consists of an SAR-FIS (including a path planner for UGVs considering risks detected by a LoRa H-WSN) and an ROS-FIS (for real-time monitoring and processing of information published throughout the ROS network). Moreover, we discuss lessons learned from using SAR-IoCA in a realistic exercise where three UGVs, a UAV, and responders collaborated to rescue victims from a tunnel accessible through rough terrain. cloud robotics; edge computing; 5G communications; hybrid wireless sensor network; robot teams; architectures for cooperation; search and rescue; UGV; UAV; path-planning +
Footnote †: journal: Sensors  
0000-0001-5000-0000-0000-0000-0000-00000-0000-00000-00000-0000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-000000-00000-000000-000000-00000-000000-000000-000000-000000-000000-000000-000 Current efforts are focused on extending cloud robotics to dynamic environments by trying to favor multi-access edge computing (MEC) for real-time applications [7]. Furthermore, recent architectures have transferred concepts from the field of computing to robotics [8]. Thus, [9] offered an architecture based on devices very close to vehicles (dew robotics), while [10] proposed to have computing capacity close to communications (fog robotics). In both cases, the main aim was to match the computational capacity to the communications capabilities. Networked robot cooperation in field applications such as agriculture [11] and disaster robotics [12] can benefit specially from advances in cloud robotics. In the case of search and rescue (SAR) missions, the combination of static and mobile nodes can be very useful [13; 14; 15]. Furthermore, nodes with complex sensors, e.g., light detection and ranging (LiDAR), can be integrated with part of the processing being carried out at the node (dew computing). In general, a wireless sensor network (WSN) in which static and fixed nodes coexist is defined as a hybrid wireless sensor network (H-WSN) [16]. However, mobile nodes pose a challenge in terms of their integration with WSNs, where effectiveness depends largely on energy consumption, coverage, and connectivity [17; 18]. In this sense, incorporating cloud computing to mobile ad hoc networks (MANETs) can contribute to overcoming limitations in storage capacity, battery life, and processing power [15]. As for coverage, routing protocols can adapt the network to changing link conditions in MANETs [19; 20], while heterogeneous H-WSNs have been addressed with ad hoc strategies for deploying mobile nodes according to coverage needs, such as mobile sink nodes [21; 22; 23; 24; 25; 26]. Regarding connectivity, communication issues such as quality of service (latency, bandwidth, and energy efficiency), stability or total disruption, and security can be solved, or at least mitigated, with the emergent fifth generation of mobile networks (5G) [27]. Furthermore, the performance of MEC in 5G networks could enable seamless communication between vehicles, sensors, and computing [28].