A formal model for planning and controlling search and rescue actions at sea

Abstract

Abstract.Recently, during search and rescue actions at sea, Unmanned Aerial Vehicles (UAVs) have been used. Onboard decision capabilities allow an UAV vehicle to reach the entity that is in distress at sea. UAVs are launched within a few minutes to begin search actions. When the exact location of the injured entity is detected, a rescue action should begin. According to the collected information about the vessel's position, manoeuvrability, and velocity, the control centre determines which vessel is to be engaged in the rescue action. This highly autonomous system can be described as a discrete event system. Certain states of such systems, such as collisions, are undesirable. This paper presents implementation of information flow to supervise, control, and monitor the behaviour of the UAVs during the search, to avoid collisions and to communicate with computational onboard sub-systems. Planning algorithms and coloured Petri nets are used to specify different phases of the mission execution. When a certain UAV detects an injured entity, alternative encoded reactions are triggered and a control centre starts implementing the rescue plan. IntroductionUnmanned Aerial Vehicles (UAVs) are becoming a very important tool for Search and Rescue (SAR) operations at sea (Skrzypietz 2010). As it is known, time is critical for saving human life at sea, so any delay can result in potential losses of human 28 Dario Medić, Anita Gudelj, Maja Krčum life. For SAR operations, UAV can be fitted with high resolution cameras, multispectral sensor, thermal sensor, infrared sensors (IR), and hyperspectral sensors. According to the development of UAVs to this date, they can be airborne from one hour to more than 24 hours. It is a known fact that UAVs can be controlled in two ways: at distance and by their previously set route. For the purpose of this paper, UAVs that fly according to their previously set route will be analyzed.Systems with multiple UAVs present significant advantages in different applications by increasing efficiency, performance, and robustness (Alejo et al. 2013, Maza et al. 2011.Some of the problems that need to be solved are: a) How to control the traffic in a way that UAVs moving in opposite directions make as few stops as possible during the passage through the cells in the space? b) How to resolve possible conflicts in case that more vehicles try to acquire the same cell at the same time? c) How to avoid possible deadlocks in the dense traffic?The vehicle's moving through the cells in the space can generally be described as a set of discrete states and events (discrete event dynamic systems -DEDS). These events and states are normally observed by the UAV management system (UAVMS) which receives data from Air Traffic Management systems (ATM), using wireless data communication. Some of these states, such as conflicts and deadlocks are undesirable. In Kezić et al. (2010), the authors used DEDS and Petri net (PN) theory, a well-known tool for analyzing DEDS to resolve some of the above-mentioned problems.In this paper...