## 1 Introduction  
From a system engineering view, the study of autonomous systems brings to the table different challenges, mainly:  
* Formulation of effective and realistic requirements that do not constrain the future system operability [1, 2]. * Interfaces' design, check, and control between hardware, software, and the human operator [3]. * Verification of the requirements and validation of the architecture [4, 5, 6, 7]. Autonomous systems have high interactions with their operative environment and must make decisions based on their resources and environment state. Those capabilities drive the overall system behaviour and operations and are hard to capture in conceptual and preliminary design in a set of well-formed requirements [1, 2]. Moreover, autonomous systems combine highly integrated software with physical hardware while interacting with a human operator. This interaction may pose problems during the requirement formulation and the verification and validation processes [3]. In [8], the author highlights how important it is to establish correct interface requirements early in the design of autonomous systems. Autonomous systems operations involve concurrent activities to be processed and performed by tightly coupled subsystems [8]. Given the difficulty of realistically defining an autonomous robotic mission and related requirements, the problem this paper addresses can be formulated as: is it possible to provide an analysis or procedure to capture the interaction between an autonomous system, its environment, its mission goals and its resources during preliminary design phases? With preliminary design phases, the authors identify conceptual studies and concept and technology development studies, as defined by the NASA life cycle standard [9]. Heritage from previous missions can help guide the decisions during initial studies. There are a few examples of spacecraft guided predominately by AI-based processes, such as some successful missions that push toward almost complete autonomy, such as Deep Space 1 [10] or Earth Observing 1 [11]. In addition, different teams are studying AI-agents capabilities on analogue platforms on Earth such as the Field Integrated Design and Operations (FIDO) [12], or the Long Range Science Rover (Rocky) [13] or the Robotic Antarctic Meteorite Search [14]. Those missions look into planning and robotics-related theories during the later implementation phases and operations, not during the initial system design. The interest of this publication is to understand how an autonomous system may behave or plan its mission at a higher design level (namely conceptual and preliminary design). Different round tables and discussions with experts can provide mission system engineers with the knowledge to allocate autonomous functionalities well and define the possible bottlenecks or criticality of different autonomy architectures. On the other hand, providing system engineers with straightforward simulation tools to understand the impact of autonomy at a higher design level can be helpful, and it can speed up the design process. This study proposes a framework to study and validate operational scenarios for autonomous robotic planetary exploration systems at early design stages. The objective is to understand how the system, provided with knowledge about its actions and their impact on different resources, plans its mission and interacts with the environment. The created plan can then be run easily on a robotic platform or a simulation as a further validation round of the computed architecture. Frameworks provide a general structure to be followed; however, they leave the designer room to implement different algorithms at any level. This framework would help the designer in both the mission definition and system sizing. As analysed in [15], if operational models can be executed, it is also possible to debug the described behaviour to match the expected performances or what the engineer meant to capture.