Event-based energy optimum route planning in the context of Unmanned Aerial Vehicles for multi-objective exploration missions

The shortest routing problem is revisited for the case of a dynamically changing environment in an Unmanned Aerial Vehicle (UAV) exploration mission. The latter is represented by energy graphs motivated by [1] and [2]. It is assumed that information about the map is received while on flight due to environmental events. In addition, UAVs are required, while on mission, to “explore” the map which involves extracting as much intelligence as possible and traversing it in the most safe flyable manner. Hence, the UAV should be capable of integrating knowledge from a variety of sources and re-plan its mission, in order to fulfil objectives. Furthermore, prior knowledge is considered to reduce computational costs. Depending on the decision making process, the notion of a “temporary” optimum path can be of physical and functional sense. The problem is modeled as a multistage decision making process, where each stage is triggered by an event. Given the current availability between paths, the objective is to devise a policy that leads from an origin or current known location to a destination node while traversing the unknown region of interest with the minimal energy requirements.