The traditional aviary decontamination process involves farmers applying pesticides to the aviary’s ground. These agricultural defenses are easily dispersed in the air, making the farmers susceptible to chronic diseases related to recurrent exposure. Industry 5.0 raises new pillars of research and innovation in transitioning to more sustainable, human-centric, and resilient companies. Based on these concepts, this paper presents a new aviary decontamination process that uses IoT and a robotic platform coupled with ozonizer (O₃) and ultraviolet light (UVL). These clean technologies can successfully decontaminate poultry farms against pathogenic microorganisms, insects, and mites. Also, they can degrade toxic compounds used to control living organisms. This new decontamination process uses physicochemical information from the poultry litter through sensors installed in the environment, which allows accurate and safe disinfection. Different experimental tests were conducted to construct the system. First, tests related to measuring soil moisture, temperature, and pH were carried out, establishing the range of use and the confidence interval of the measurements. The robot’s navigation uses a back-and-forth motion that parallels the aviary’s longest side because it reduces the number of turns, reducing energy consumption. This task becomes more accessible because of the aviaries’ standardized geometry. Furthermore, the prototype was tested in a real aviary to confirm the innovation, safety, and effectiveness of the proposal. Tests have shown that the UV + ozone combination is sufficient to disinfect this environment.

The practicality allied with technological and logistical advances led customers increasingly to join e-commerce. This phenomenon was even more expanded during the COVID-19 pandemic. Because of the growing demand for logistic services, warehouses must adapt and seek faster and more efficient processes. A means to achieve that is to use mobile robots in transportation-related tasks. However, their effectiveness is only achieved if the system has an efficient work distributor. This research developed a centralized coordination architecture using the island model genetic algorithm for multi-robot logistic task allocation. The coordination receives, allocates, and monitors tasks performed by robots with different payload capacities and average speeds. The architecture was developed upon the robotic operating system. Thus, any robot with a robotic operating system-based internal system can work under the coordination of this architecture. On the results, the task scheduler, developed in previous work, had the evaluation complemented. The scheduler based on the island model genetic algorithm allocates more tasks than the standard genetic algorithm when using the same heuristic. Regarding coordination architecture, the system successfully managed a group of robots in a simulated environment. It also detected and notified failures during task execution. Therefore, this system provides a complete task scheduling, allocation, and monitoring solution.