The PARHéRo project explicitly aims to increment synergies between scientific and industrial research to anticipate and control the evolution of heterogeneous robotic platforms in complex, unknown and/or hostile environments. The successful completion of robotic missions is ensured by the high degree of autonomy of the platforms, a central element for their robustness, which is achieved through learning, planning and the supervision of the execution of intelligent behaviour. The project aims firstly at providing autonomous multi-robot platforms with a mission specification language able to express both the objectives and the requirements on the state of the robots and the characteristics of the planning model. In order to test the specification language, the project will aim to generate case studies that are coherent with the applications envisaged for future defence and security systems. The mission specification language is also the means by which the results of the learning, achieved by each of its elements, can be shared in the fleet of heterogeneous robots. Autonomous decision making, or even interactive planning with a human overseeing the mission, strives for platform resilience to unexpected, dangerous, or unpredictable events in the environment. In this context, the use of domain knowledge -- whether prior or on-the-fly during the mission execution phase -- can provide a quicker and better solution to the problems faced. This hybridisation between Automatic Planning in Artificial Intelligence and Machine Learning ensures the robustness, adaptability, and resilience of the fleet of heterogeneous robots, all participating in the same strategic objective. Automated planning and machine learning (in particular Reinforcement Learning) are characterized by complementary views on decision making: the former relies on previous knowledge used to create a model and computation of a solution from this model, while the latter on interaction with the world, and repeated experience. Reinforcement learning, can start without any previous knowledge, and allows robots to robustly adapt to the environment, but often necessitates an infeasible amount of experience. Planning allows robots to carry out different tasks in the same domain, without the need to acquire additional knowledge about the domain or about each one of them, but relies strongly on the accuracy of the model. Furthermore, the search space of a planner with partial knowledge about the environment can grow exponentially in the number of possible states, making the planning process practically unfeasible. However, even a small injection of knowledge from prior learning about the model can greatly improve the performance of the solution search. This a priori knowledge, which can come from learning phases of intelligent behaviour, allows the refinement of meta-heuristics, macro-actions, or even hierarchical task decompositions. Learning techniques can also be used to improve the decisions made by a group of robots, such as mission optimisation against opposing criteria. Whether they are high-level strategies or purely reactive components, the coordination of a fleet of autonomous mobile robots requires the transmission of information learned by each robot based on local information, provided that the robustness conditions of the communication network are maintained. Otherwise, the estimation by each robot of the global situation is necessary to guarantee the autonomy of the robot fleet, and the robustness of the mission.