Privacy-Aware Operation of a Complex Mission

The paper considers the problem of the safe operation of multiple agents with different capabilities and access authorities to effectively and safely accomplish a complex mission. This problem is decomposed into two main sub-problems. The first sub-problem is to obtain the desired configuration of the agent team so that the best coverage of a distributed target is achieved while distinct inaccessible regions are avoided. To achieve this, we first apply the principles of computational fluid dynamics to establish a nonsingular mapping between the motion space and a planning space that excludes all inaccessible regions. We then develop a novel deep neural network forward learning (DNNFL) to abstractly represent the target by a finite number of points specifying the desired configuration of the agent team. The second sub-problem is the mission planning that is defined as the event-triggered Markov Decision Process (ET-MDP) with constrained actions and components that are updated by a deterministic finite automaton.