The fields of industry 5.0 and defense are increasingly based on systems of systems where robotic agents must adapt to humans with whom interactions take place. The use of heterogeneous fleets of agents with perception devices is a godsend that allows, after merging individual information, to propose solutions to the problems of optimizing fleet operations, securing the convoy, improving safety and security for human operators, as well as increasing flexibility following the reconfiguration of situations or the environment. The mutualization of information allows to produce a global view of the situation resulting from the individual perceptions of each robotic or non-robotic agent. Each individual perception module produces an interpretation of the scene which is by nature tainted with uncertainty. The consequences of a fleet deployment in complex or hostile environments must also be considered. The communication link required for information exchange is subject to a bandwidth that can be very limited or even non-existent when the link is broken even temporarily. The viewpoint positions required to create the situation view are also dependent on the quality of the location source information when available. The SCOPES project proposes to develop a solution for the production of a situation view augmented by uncertainty as a source of decision information. The contributions of the project will be : - A representation formalism of the situation view, integrating the different sources of uncertainties, allowing an interpretation by humans. - A robust localization method based on the graph paradigm and the semantic information provided by each agent. - A functional specification and associated datasets for objective and quantitative evaluation of collaborative perception situations thanks to the exploitation of the outstanding technological platforms of the project partners. The SCOPES project will lead to TRL 4 level productions. The interest of the project for the economic actors was recognized by the labeling of the project by NAE.