This article proposes a novel decentralized two-layered and multi-sensorial based fusion architecture for establishing a novel resilient pose estimation scheme. As it will be presented, the first layer of the fusion architecture considers a set of distributed nodes. All the possible combinations of pose information, appearing from different sensors, are integrated to acquire various possibilities of estimated pose obtained by involving multiple extended Kalman filters. Based on the estimated poses, obtained from the first layer, a Fault Resilient Optimal Information Fusion (FR-OIF) paradigm is introduced in the second layer to provide a trusted pose estimation. The second layer incorporates the output of each node (constructed in the first layer) in a weighted linear combination form, while explicitly accounting for the maximum likelihood fusion criterion. Moreover, in the case of inaccurate measurements, the proposed FR-OIF formulation enables a self resiliency by embedding a built-in fault isolation mechanism. Additionally, the FR-OIF scheme is also able to address accurate localization in the presence of sensor failures or erroneous measurements. To demonstrate the effectiveness of the proposed fusion architecture, extensive experimental studies have been conducted with a micro aerial vehicle, equipped with various onboard pose sensors, such as a 3D lidar, a real-sense camera, an ultra wide band node, and an IMU. The efficiency of the proposed novel framework is extensively evaluated through multiple experimental results, while its superiority is also demonstrated through a comparison with the classical multi-sensorial centralized fusion approach.

This article tackles the challenge of negative pressure adhesion control of a Vortex Robotic (VR) platform, which utilizes a modified Electric Ducted Fan (EDF)-based design for successfully adhering to surfaces of variable curvature. The resulting Vortex Actuation (VA) system is estimated through a switching Autoregressive-Moving-Average with eXternal input (ARMAX) identification, for accurately capturing the throttle to adhesion force relationship throughout its operating range. For safe attachment of the robot on a surface, the critical adhesion is modeled based on the geometrical properties of the robotic platform for providing the required reference forces. Within this work, an explicit controller via the use of a Constraint Finite Time Optimal Control (CFTOC) approach is designed in an offline manner, which results in a lookup table realization that ensures overall system stability in all state transitions. In an effort to further improve the tracking response for arbitrary setup orientations, the adhesion control scheme is extended through a switching EMPC framework. The resulting frameworks are tested through both dynamic simulation and experimental sequences involving: a) adhesion control on a rotating test curved surface and, b) adhesion and locomotion sequences on a water pipe.

Time delays in communication networks are one of the main concerns in deploying robots with computation boards on the edge. This article proposes a multi-stage Nonlinear Model Predictive Control (NMPC) that is capable of handling varying network-induced time delays for establishing a control framework being able to guarantee collision-free Micro Aerial Vehicles (MAVs) navigation. This study introduces a novel approach that considers different sampling times by a tree of discretization scenarios contrary to the existing typical multi-stage NMPC where system uncertainties are modeled by a tree of scenarios. Additionally, the proposed method considers adaptive weights for the multi-stage NMPC scenarios based on the probability of time delays in the communication link. As a result of the multi-stage NMPC, the obtained optimal control action is valid for multiple sampling times. Finally, the overall effectiveness of the proposed novel control framework is demonstrated in various tests and different simulation environments.