Branching to find feasible solutions in Unmanned Air Vehicle Mission Planning Cristian Ramirez-Atencia 1 , Gema Bello-Orgaz 1 , Maria D. R-Moreno 2 , and David Camacho 1 1 Departamento de Ingenier´ ıa Inform´ atica, Universidad Aut´onoma de Madrid, C/Francisco Tom´ as y Valiente 11, 28049 Madrid, Spain cristian.ramirez@inv.uam.es, {gema.bello,david.camacho}@uam.es aida.ii.uam.es 2 Departamento de Autom´ atica, Universidad de Alcal´ a, Carretera Madrid Barcelona, km 33 600, 28871 Madrid, Spain mdolores@aut.uah.es Abstract. Mission Planning is a classical problem that has been tradi- tionally studied in several cases from Robotics to Space missions. This kind of problems can be extremely difficult in real and dynamic scenarios. This paper provides a first analysis for mission planning to Unmanned Air Vehicles (UAVs), where sensors and other equipment of UAVs to perform a task are modelled based on Temporal Constraint Satisfac- tion Problems (TCSPs). In this model, a set of resources and temporal constraints are designed to represent the main characteristics (task time, fuel consumption, ...) of this kind of aircrafts. Using this simplified TCSP model, and a Branch and Bound (B&B) search algorithm, a set of fea- sible solutions will be found trying to minimize the fuel cost, flight time spent and the number of UAVs used in the mission. Finally, some exper- iments will be carried out to validate both the quality of the solutions found and the spent runtime to found them. Keywords: unmanned aircraft systems, mission planning, temporal con- straint satisfaction problems, branch and bound 1 Introduction Unmanned Aircraft Systems (UAS) can take advantage of planning techniques where the application domain can be defined as the process of generating tactical goals for a team of Unmanned Air Vehicles (UAVs). Nowadays, these vehicles are controlled remotely from ground control stations by humans operators who use legacy mission planning systems. Mission planning for UAS can be defined as the process of planning the lo- cations to visit (waypoints) and the actions that the vehicle can perform (load- ing/dropping a load, taking videos/pictures, acquiring information), typically over a time period. These planning problems can be solved using different meth- ods such as Mixed-Integer Lineal Programming (MILP) [14], Simulated Anneal- ing [2], Auction algorithms [8], etc. Usually, these methods are the best way