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Complex aircraft systems are becoming a target for automation. For successful operation, they require both efficient and readable mission execution system. Flight control computer (FCC) units, as well as all important subsystems, are often duplicated. Discrete nature of mission execution systems does not allow small differences in data flow among redundant FCCs which are acceptable for continuous control algorithms. Therefore, mission state consistency has to be specifically maintained. We present a novel mission execution system which includes FCC state synchronization. To achieve this result we developed a new concept of Asynchronous Behavior Tree with Memory and proposed a state synchronization algorithm. The implemented system was tested and proven to work in a real-time simulation of High Altitude Pseudo Satellite (HAPS) mission.
There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Cont
In this work, we address the estimation, planning, control and mapping problems to allow a small quadrotor to autonomously inspect the interior of hazardous damaged nuclear sites. These algorithms run onboard on a computationally limited CPU. We inve
In this paper, we propose Belief Behavior Trees (BBTs), an extension to Behavior Trees (BTs) that allows to automatically create a policy that controls a robot in partially observable environments. We extend the semantic of BTs to account for the unc
Autonomous-mobile cyber-physical machines are part of our future. Specifically, unmanned-aerial-vehicles have seen a resurgence in activity with use-cases such as package delivery. These systems face many challenges such as their low-endurance caused
This paper considers the problem of asynchronous distributed multi-agent optimization on server-based system architecture. In this problem, each agent has a local cost, and the goal for the agents is to collectively find a minimum of their aggregate