In this article, the adhesion modeling and control case of a Vortex Climbing Robot (VCR) is investigated against a surface of variable orientations. The critical adhesion force exerted from the implemented Vortex Actuator (VA) and the VCR's achievable payload are analyzed under 3-DOF rotations of the test surface, while extracted from both geometrical analysis and dynamically-simulated numerical results. A model-based control scheme is later proposed, with the goal of achieving adhesion while the VCR remains immobilized, limiting the power consumption and compensating for disturbances (e.g. moving cables) leading to Center-of-Mass (CoM) changes. Finally, the model-based control scheme is experimentally evaluated, with the VCR prototype on a rotating and moving flat surface. The presented results support the use of the proposed methodology in climbing robots targeting inspection and maintenance of stationary surfaces (flat, curved etc.), as well as future robotic solutions operating on moving structures (e.g. ships, cranes, folding bridges).

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In this article, the development and control of a novel differential drive Vortex Robot (VR) will be presented. Towards the direction of developing a climbing robot for inspection and repair of large infrastructures, a lightweight reliable climbing robot is proposed, being able to carry high payload via an Electric Ducted Fan (EDF) vortex based adhesion actuator. Towards these objectives, the fundamental elements of the overall design will be presented. For the preliminary evaluation of the proposed system, a PID-based control architecture will be analyzed and experimentally evaluated, with the goal of controlling the locomotion properties of the VR, while achieving a constant adhesion regardless the orientation of the robot and the surface's inclination. To further evaluate the robot's efficiency in real-life scenarios, such as the inspection and repair of airplane structures, results acquired via field trials involving a full-scale Boeing 737 will be presented.

The long-term prediction of morphological bed evolution has been of interest to engineers and scientists for many decades. Usually, process-based models are employed to simulate bed-level changes in the scale of years to decades. To compensate for the major computational effort required by these models, various acceleration techniques have been developed, namely input-reduction, model-reduction and behaviour-oriented modelling. The present paper presents a new input-reduction method to obtain representative wave conditions based on the Shields criterion of incipient motion and subsequent calculation of the sediment pick-up rate. Elimination of waves unable to initiate sediment movement leads to additional reduction of model run-times. The proposed method was implemented in the sandy coastline adjusted to the port of Rethymno, Greece, and validated against two datasets consisting of 7 and 20 and 365 days, respectively, using the model MIKE21 Coupled Model FM. The method was compared with a well-established method of wave schematization and evaluation of the model’s skill deemed the simulations based on the pick-up rate schematization method as “excellent”. Additionally, a model run-time reduction of about 50% was observed, rendering this input-reduction method a valuable tool for the medium to long-term modelling of bed evolution.

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.