RoBétArmé Project: Human-robot collaborative construction system for shotcrete digitization and automation through advanced perception, cognition, mobility and additive manufacturing skills.

The importance of construction automation has grown worldwide, aiming to deliver new machineries for the automation of roads, tunnels, bridges, buildings and earth-work construction. This need is mainly driven by (i) the shortage and rising costs of skilled workers, (ii) the tremendous increased needs for new infrastructures to serve the daily activities and (iii) the immense demand for maintenance of ageing infrastructure. Shotcrete (sprayed concrete) is increasingly becoming popular technology among contractors and builders, as its application is extremely economical and flexible as the growth in construction repairs in developed countries demand excessive automation of concrete placement. Even if shotcrete technology is heavily mechanized, the actual application is still performed manually at a large extend. RoBétArméEuropean project targets the Construction 4.0 transformation of the construction with shotcrete with the adoption of breakthrough technologies such as sensors, augmented reality systems, high-performance computing, additive manufacturing, advanced materials, autonomous robots and simulation systems, technologies that have already been studied and applied so far in Industry 4.0. The paper at hand showcases the development of a novel robotic system with advanced perception, cognition and digitization capabilities for the automation of all phases of shotcrete application. In particular, the challenges and barriers in shotcrete automation are presented and the RoBétArmésuggested solutions are outlined. We introduce a basic conceptual architecture of the system to be developed and we demonstrate the four application scenarios on which the system is designated to operate.

The RoBétArmé European project targets the Construction 4.0 transformation of the construction with shotcrete with the adoption of breakthrough technologies such as sensors, augmented reality systems, high-performance computing, additive manufacturing, advanced materials, autonomous robots and simulation systems, technologies that have already been studied and applied so far in Industry 4.0. This paper showcases a case study on which novel robotic systems will be developed for the automation of shotecrete application. The outcomes of this research can be widely used in other application technologies related to the construction domain.

Investigating Habitability with an Integrated Rock-Climbing Robot and Astrobiology Instrument Suite.

A prototype rover carrying an astrobiology payload was developed and deployed at analog field sites to mature generalized system architectures capable of searching for biosignatures in extreme terrain across the Solar System. Specifically, the four-legged Limbed Excursion Mechanical Utility Robot (LEMUR) 3 climbing robot with microspine grippers carried three instruments: a micro-X-ray fluorescence instrument based on the Mars 2020 mission's Planetary Instrument for X-ray Lithochemistry provided elemental chemistry; a deep-ultraviolet fluorescence instrument based on Mars 2020's Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals mapped organics in bacterial communities on opaque substrates; and a near-infrared acousto-optic tunable filter-based point spectrometer identified minerals and organics in the 1.6-3.6 µm range. The rover also carried a light detection and ranging and a color camera for both science and navigation. Combined, this payload detects astrobiologically important classes of rock components (elements, minerals, and organics) in extreme terrain, which, as demonstrated in this work, can reveal a correlation between textural biosignatures and the organics or elements expected to preserve them in a habitable environment. Across >10 field tests, milestones were achieved in instrument operations, autonomous mobility in extreme terrain, and system integration that can inform future planetary science mission architectures. Contributions include (1) system-level demonstration of mock missions to the vertical exposures of Mars lava tube caves and Mars canyon walls, (2) demonstration of multi-instrument integration into a confocal arrangement with surface scanning capabilities, and (3) demonstration of automated focus stacking algorithms for improved signal-to-noise ratios and reduced operation time.

Tactile Sensing and Control of Robotic Manipulator Integrating Fiber Bragg Grating Strain-Sensor.

Tactile sensing is an instrumental modality of robotic manipulation, as it provides information that is not accessible via remote sensors such as cameras or lidars. Touch is particularly crucial in unstructured environments, where the robot's internal representation of manipulated objects is uncertain. In this study we present the sensorization of an existing artificial hand, with the aim to achieve fine control of robotic limbs and perception of object's physical properties. Tactile feedback is conveyed by means of a soft sensor integrated at the fingertip of a robotic hand. The sensor consists of an optical fiber, housing Fiber Bragg Gratings (FBGs) transducers, embedded into a soft polymeric material integrated on a rigid hand. Through several tasks involving grasps of different objects in various conditions, the ability of the system to acquire information is assessed. Results show that a classifier based on the sensor outputs of the robotic hand is capable of accurately detecting both size and rigidity of the operated objects (99.36 and 100% accuracy, respectively). Furthermore, the outputs provide evidence of the ability to grab fragile objects without breakage or slippage e and to perform dynamic manipulative tasks, that involve the adaptation of fingers position based on the grasped objects' condition.

A probabilistic framework for 3D visual object representation.

We present an object representation framework that encodes probabilistic spatial relations between 3D features and organizes these features in a hierarchy. Features at the bottom of the hierarchy are bound to local 3D descriptors. Higher level features recursively encode probabilistic spatial configurations of more elementary features. The hierarchy is implemented in a Markov network. Detection is carried out by a belief propagation algorithm, which infers the pose of high-level features from local evidence and reinforces local evidence from globally consistent knowledge, effectively producing a likelihood for the pose of the object in the detection scene. We also present a simple learning algorithm that autonomously builds hierarchies from local object descriptors. We explain how to use our framework to estimate the pose of a known object in an unknown scene. Experiments demonstrate the robustness of hierarchies to input noise, viewpoint changes, and occlusions.