They can operate in areas unsafe or inaccessible to humans. These drones can be used autonomously or operated by a human drone pilot to detect and locate anomalies or perform search and rescue. One solution to the mobility and flexibility issues is to mount the sensors on robotic/autonomous systems such as unmanned aerial vehicles (UAVs) often referred to as drones. Many current sensors, including IoT sensor systems, are static with fixed mountings. Sensor monitoring for environments, infrastructure and buildings needs to be mobile, flexible, robust and have the ability to be used in a broad range of environments. In the sensor monitoring application domain, an anomaly is indicative of a problem that needs investigating further such as a gas leak where the gas reading detected by the sensors is elevated above normal background readings for that particular gas. a sensor reading that appears to be inconsistent with the remainder of the set of sensor readings. In this context, we define an anomaly as an outlying observation that appears to deviate markedly from other members of the sample in which it occurs, i.e. The sensors take measurements of specific chemical concentrations or infrared or thermal imaging levels which can then be analysed to detect anomalies. There are other less dramatic applications such as agricultural, construction and environmental monitoring.

These include the detection and identification of chemical leaks, gas leaks, forest fires, disaster monitoring and search and rescue. Rapid and accurate sensor analysis has many applications relevant to society today (see for example, ). 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. We evaluate different configurations against a heuristic technique to demonstrate its accuracy and efficiency. 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. In hazardous and safety-critical situations, locating problems accurately and rapidly is vital. 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. Until very recently, they have been task specific. These systems need to be able to learn through fusing data from multiple sources. This motivates the need for flexible, autonomous and powerful decision-making mobile robots. The importance of accurate and multifaceted monitoring is well known to identify problems early and prevent them escalating. Mobile robots such as unmanned aerial vehicles (drones) can be used for surveillance, monitoring and data collection in buildings, infrastructure and environments.

Uav drone game