Abstract Mobile robots such as unmanned aerial vehicles (drones) can be used for surveillance, monitoring and data collection in buildings, infrastructure and environments. The importance of accurate and multifaceted monitoring is well known to identify problems early and prevent them escalating. This motivates the need for flexible, autonomous and powerful decision-making mobile robots. These systems need to be able to learn through fusing data from multiple sources. Until very recently, they have been task specific. In this paper, we describe a generic navigation algorithm that uses data from sensors on-board the drone to guide the drone to the site of the problem. In hazardous and safety-critical situations, locating problems accurately and rapidly is vital. We use the proximal policy optimisation deep reinforcement learning algorithm coupled with incremental curriculum learning and long short-term memory neural networks to implement our generic and adaptable navigation algorithm. We evaluate different configurations against a heuristic technique to demonstrate its accuracy and efficiency. Finally, we consider how safety of the drone could be assured by assessing how safely the drone would perform using our navigation algorithm in real-world scenarios.

This paper focuses on Service Level Agreements (SLAs) for industrial applications that aim to port some of the control functionalities to the cloud. In such applications, industrial requirements should be reflected in SLAs. In this paper, we present an approach to integrate safety-related aspects of an industrial application to SLAs. We also present the approach in a use case. This is an initial attempt to enrich SLAs for industrial settings to consider safety aspects, which has not been investigated thoroughly before.