Digitizing Forests

DIGITIZING FORESTS

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A New Paradigm For The Use of Local Lumber in Non-Standardized Vernacular Architecture, Through Digitization of Resources

Emerging technologies such as computational design tools, mobile robots and 3d-scanning open up possiblities for digitization of material resources, allowing more efficient use of materials. Robotic and digital fabrication methods show the possibilities of exploring non-standardized workflows in an industrialized environment. The following research explores alternative methods to engineered wood processes, with the aim of using locally sourced timber, and reducing the overall waste in engineered wood processes through integrating material resources and fabrication methods in the early design process.

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Valldaura Labs

ABSTRACT

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Wood Urbanism - Daniel Ibanez et al.

Engineered Wood

The development of engineered wood products has contributed to recent advances in the application of wood in architecture by providing stable, consistent material that can be worked with in complex ways. However, this development leads to the paradox of wood being factoryprocessed into regularised products only to subsequently undergo complex subtractive digital fabrication processes to allow that material to be incorporated in geometrically complex organic building forms and structures. Furthermore, wood in its natural form consists of chains of cells – its grain fibres – that are optimally aligned to transmit force.

Once these fibres are cut, the material immediately loses strength, meaning that most fabrication processes act to compromise the existing natural capacity of timber. [1] Of course, the benefits of engineered timber products such as laminated veneer lumber (LVL) and processes like glue-lamination are well known. By gluing veneers or laminas together, mechanical consistency and stability can be achieved which is only possible in un-processed wood through intimate craftsman-like knowledge of

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the behaviour of a given species. Engineered products remove or even-out the ‘flaws’ in timber, such as knots, producing predictability. However, this homogenization of the wood also, arguably, acts to reduce the specific characteristic qualities of timber and reduces it to generic ‘stuff ’ that can be used without really understanding the true, microscopic, living anatomy of the material. The risk is that wood is perceived primarily as a means to achieve shape, rather than as a complex material with specific properties that can be best exploited by understanding its idiosyncrasies rather than averaging them out.

A second paradox inherent in the use of industrially produced engineered wood products comes as a consequence of the tendency towards centralisation of their manufacture. The resultant embodied energy of transportation and processing compromises wood’s unique environmental credentials. Economic forces encourage standardisation and mass-production, and favour consistency of raw material and thus a forestry mono-culture. This is amplified by the fact that timber

Introduction as a material is produced almost anywhere, whereas for example steel production requires a centralised infrastructure. Hence an alternative view of a distributed, localised model of timber production for architecture is seen as more sympathetic to the realities of wood and requiring less energy input.[2]

Reflecting the principles of the maker movement and of dispersed manufacturing, enabled by new low-cost manufacturing and communication technologies, a model of locally focused forestry, processing, new fabrication and construction becomes compelling.

Photo Credits: Alex MacLean

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WOOD AS A NATURAL MATERIAL It’s Important to remember that wood comes from trees. Wood was evolved as a functional tissue of plants and not as a material to satisfy woodworkers. In order to understand the wood better, we must consider tree in different levels - from an entire plant to individual cells. To understand better the grain structure and knots internal structure, it’s important to examine the structure of tree as a living organism.

All trees have certain common characteristics. All are vascular, perennial plants capable of secondary thickening, or adding yearly growth to previous growth. The visible portion of the tree has a main supporting stem or trunk. The trunk is the principal source of wood used by woodworkers.

The tree stem parts are accumulations of countless cells. The cell is the basic structural unit of plant material. Typically, wood cells are elongated. The proportion of length to diameter varies widely among cell types, from short barrel shapes to long, needlelike cells. Cells of similar type or function are collectively referred to as tissue.

No discussion of wood can proceed very far without encountering the word grain. The term grain alone often describes the direction of the dominant longitudinal cells in a tree. Substituting the term grain direction adds clarification. [3]

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CONTEXT

Though apparently static, a tree has a dynamic internalsystem during the growing season. Water from the soil moves up from the roots to the leaves, bringing moisture and nutrients.

Much of this water evaporates to cool the foliage through transpiration. Carbon dioxide from the air and water from the

leaves combine with chlorophyll to produce sugar (with oxygen as a by-product). The remaining water and nutrients combine with the sugar to form sap, which flows down through the inner living bark. [3]

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Diversity in Wood

Wood as a multi-scalar material, presents many challenges for its application and analysis, borne out of its wide variance and individuality. Wood presents several distinct levels of detail, each with its own types of parameters, behaviours and properties, but each also interrelated and dependant on others. Most of the diversity in types of wood, and their resultant behaviours can be attributed to small-scale variations in the cellular composition and growth of trees. [3] The tracheid or wood fibre has multiple roles throughout the trees. The generally parallel arrangement of these cells create one of the most important constants across all types of wood, an anisotropic character. This property has fundamental implications for the use of timber, meaning that different properties will emerge based on different cell orientation.

The use of wood in a particular design context or situation requires an understanding of what the design is asking of the wood, and how this vast parameter space can be navigated to arrive at an appropriate solution. What this means for the design and construction of products or buildings in timber, is that different timber products have

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- Hankinson’s equation describes the relationship between microfibril angle and the compressive strength of wood.

very specific allocations and distributions of wood fibre, each suitable for a particular set of applications. [4]

CONTEXT R EA CT I O N W O OD

TENSION WOOD AND COMPRESSION WOOD: Reaction wood is abnormal wood formed in response to the growth environment inducing mechanical stresses in the tree. It is formed in the main stem of a leaning or inclined tree and in branches. The function of reaction wood is to bring the main stem or branch back to its normal position. Reaction wood is generally considered undesirable in sawn wood products, and is either not permitted or limited in structural grades of lumber.

Reaction wood in softwoods is called compression wood.

It is induced on the compression or lower side of the leaning tree trunk and branch. Compression wood has eccentric growth rings of varying width within the growth ring. It also has little contrast in color between earlywood and latewood within a growth ring. Compression wood properties are different from normal wood. It has a higher density than normal wood, but with comparable strength

values. However, it has lower strength than wood with a comparable density. The longitudinal shrinkage of compression wood is more than in normal wood, inducing warp in lumber, such as bow, crook, and twist.

Abnormal wood in hardwoods is called tension wood. It is found on the tension or upper side of leaning trees and branches. Most tension wood strength values are lower than normal wood of similar density. The tension wood zones in kiln-dried lumber tend to collapse. Tension wood will induce warp in the lumber, such as crook and bow. Tension wood fibers tend to pull out during sawing and planing operations, producing fuzzy or woolly grain.

Tension wood has a slower drying rate than normal wood, resulting in tension wood zones with a higher moisture content than surrounding normal wood. [3]

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DIGITISING FORESTS In this chapter, the research on autonomous navigation of drones as well as photogrammetry scanning method and the result of these methods are discussed.

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Remote Sensing with UAVs

While fabrication with UAVs (Unmanned Aerial Vehicles) has yet to be applied in a bigger architectural context, their potential has been marked in tasks of scanning/ surveying, gathering data about an existing construction or ongoing construction site. The remote inspection or scanning of bridges or tunnels are processes already implemented into the construction industry, demonstrating an alternative in cases where land-based scanning would be impossible or unproductive. [4]. Additionally, visual tracking algorithms implemented on drones in autonomous flight for photo/video purposes has become common throughout AEC [5].

V a l l d a u r a L a b s - P o in t c l o ud

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DEVELOPING A CONTROLLER FOR AUTONOMOUS NAVIGATION OF DRONES

This research presents work pertaining to drone pose estimation and flight calibration, underlining the increased importance of determining accurate localization for UAVs with the purpose of achieving complex autonomous flight operations.

By comparing data from motion trackers with the odometry results, it is possible to calibrate the Proportional Integral Derivative (PID) controller and minimize the localization error. In addition, the proposed workflow utilizes mixed reality as an intuitive and fast tool for path planning. This research anticipates the growth in the utilization of UAVs as a creative design medium and an efficient agent for on-site operations. The research aims to contribute to the methods of precise, automated coordination of drones within such contexts.

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REAL-TIME DENSE-CLOUD FROM LSD SLAM ODOMOETRY METHOD

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DRONE CONTROLLER WORKFLOW

This research introduces a PID Controller for autonomous drone navigation. For drone localization, the protocol implemented is based on Bebop Autonomy (for Parrot Bebop 1.0 and 2.0 drones) built over Parrot’s official ARDroneSDK3, which integrates visual-inertial velocity estimates, reported by Bebop’s firmware, to calculate on-board odometry. This information contains both the position and velocity of the drone.

In this research, the workflow is developed around ROS Kinetic for Ubuntu 16.04 (Xenial) and Grasshopper visual scripting language for Rhino3D. A WebSocket communication protocol is used to send and receive data between the two platforms. There are two main programs running in separate terminals in Linux. One terminal is running Bebop Autonomy driver which works as a ROS core, while the second terminal runs our custom program for the PID controller.

This controller program reads the current pose of the drone from topics published by the Bebop Autonomy driver which contains two vectors, position, and orientation; and compares them with the target pose information that is received from Rhino/Grasshopper.

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The difference between current pose and target pose is the error. The PID controller attempts to minimize the error over time by adjustment of velocity commands for the drone.

After calculating new velocity commands based on the error, these commands are published to the cmd_vel topic of Bebop Autonomy driver. This process happens with a framerate of 5Hz, equaling to the odometry information of the Bebop Autonomy driver. When the error is reduced to a predefined threshold, the target pose updates and the drone moves towards the next target pose.

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PID (Proportional, Integral, Derivative)

PID CALIBRATION

The velocity commands for the drone are the result of our PID controller mechanism. These commands contain six values, three values for linear velocities and three values for angular velocities. Each error is separately calculated with the PID controller. Therefore for each of these values, there are three parameters of the PID (kp, ki, kd) to be fine-tuned. The PID values are dependant on the drone’s specificationsa and environmental factors that the drone experiences, such as wind.

Therefore, there is a need to test different values to fine-tune the PID. In this process, each linear or angular velocity value is calibrated separately. Here is the result of a few tests that were performed in the same condition and with the same waypoints, to calibrate the PID values. The results demonstrate the effect of the PID parameters on the flight path.

DYNAMIC PID CONTROLLER ADJUSTMENT USING REINFORCEMENT LEARNING To adjust drone’s behaviour dynamically, QLearning algorithm that teaches itself based on a system of rewards and punishments was developed. At each step, the system evaluates the state of the drone, chooses

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and executes an action, then evaluates the state again.

Based on the difference between states, the reward is calculated.

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- The diagram on the left describes the workflow of Q Learning for PID adjustment.

- Top right diagram describes the system to define the states for Q learning process.

- The diagram on the left explains the reward system for Q learning.

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SCANNING METHODS

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COMPUTER VISION

As part of our research, we explored different methods to collect information about tree geometries, other than 3d scanning methods. in this part we used a computer vision algorithm to detect 2d layouts of trees in the forest as the drone is flying.

When the drone is moving laterally, the closer the objects are to the camera, the more they move in consecutive frames. using this theory by tracking feature points in frames, we can separate objects in the foreground from background. these maps are useful for both generating 2D layouts of the trees and as well can be used for obstacle avoidance of the drone.

C o m p u ter V i s i on - T r e e D e t e c t ion

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UAV BASED PHOTOGRAMMETRIC MEASUREMENTS OF TREE GEOMETRIES

Tree geometry is an important parameter required to quantify timber resources and is essential in evaluating the economic and ecological value of a forest stand. In particular, height plays an important role in the calculation of individual and total stand volumes, assessing the overall productive capacity of a site and determining the social status of an individual tree’s ability to access resources.

Recent developments in remote sensing technologies, such as LiDAR and Digital Photogrammetry, opened up new possibilities in not only the estimation of individual tree heights, but also in the estimation of tree crown diameter. Tree height and crown diameter can be further utilized to estimate individual tree characteristics, such as stem diameter and volume. [7]

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DIGITISING FORESTS Under Canopy Scanning

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Our research interest lies in building a database from geometries of trees in the forest. Drones flying under the canopy will collect the photos required to apply for photogrammetry method to create a 3d representation of the forest.

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PHOTOGRAMMETRY

Aerial Scanning

Aerial Scanning of the forest using drones is part of the early processes in digitizing the forest, these models represent data about the position of trees, density of the trees, canopy size, creating

topography maps for the forest, finding the trails, estimating water flow patterns and etc. These information are useful for forestry and yield estimation purposes.

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DIGITISING FORESTS

POINTCLOUD PROCESSING SEGMENTATION The photos captured by drones are processed in AGISOFT software to develop the pointcloud.

These pointclouds need to be processed before any type of specific data can be extracted from them. There are several processes that are applied to the raw pointclouds such as noise filtering and segmentation.

The pointclouds are processed in PCL (Point Cloud Library) software. First, the point clouds are filtered to reduce their noise, the noise is the result of inaccuracies in the pointcloud generation, therefore this process cleans the data for furthur processes as well, increasing the accuracy of the data. after noise filtering segmentation algorithms are used to cluster different parts of the pointcloud together, for example ground, trunk, leaves and etc. this process allows us to access each of these clusters separately to extract information for different purposes.

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DIGITIZING FOREST The images on top show the noise filtering process which prepares the point cloud for segmentation, The images in bottom show the result of segmentation, each color specifies a uniqe cluster that enables us to differentiate information in point clouds for further processes. such as extracting information on geometries and etc.

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POINTCLOUD SEGMENTATION

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DIGITIZING FOREST Segmentation of PointClouds allows measurements of different types of data in the forest, Ground level: extracting topography data, vegetation and etc.

Tree Trunks as our main interest in creating a database and canopies.

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FORESTRY

POINTCLOUD PROCESSING

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Precise topography maps extracted from pointclouds, as well as trees geometries data.

DIGITIZING FOREST Information about topography as part of the information that can be extracted from forest pointclouds, providing exact information about trails, prediction of water flows, sub canopy vegetation and etc.

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DATABASE

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DIGITISING FORESTS A catalogue of geometries of trees in form of mesh is created, this information is later analyzed for design purposes.

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B R A NC H ES C A T A L O G UE

DIGITIZING FOREST For each branch, the geometry goes through an analysis process, in this process information such as axis line of the branch is extracted.

One way to sort these geometries is through curvature. Deviation of

the axis line with a straight line that fits through geometry is calculated, in this process we can sort geometries based on their curvature.

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DATABASE

MATCHING GEOMETRIES

In order to use the geometries of trees as design input, found forms, The geometry from design is compared with the skeleton line of the existing branches in our database. In this process each two curves are compared and their deviation from each other is calculated, if on curve is longer than the other one this process happens in different positions of the longer curve to find the part of the longer curve that matches best. The search algorithm in this process is searching for the minimum amount of deviation between two curves, In the end for each curve in the design, we can find the closest geometry in the database. As each tree in database can only be matched with one curve in design, there is a need to optimize the order of finding branches in the database to reduce the overall deviation. for this purpose we used genetic algorithms to find the best order of comparing trees together.

USING TREE GEOMETRIES AS DESIGN INPUT Wood has been used as an architectural material for a long time, but the way it is used is in forms of standardized geometries which is the result of industrialzed processes.

However trees appear in very complex geomteries in nature, In this research we are exploring possibilities to

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use the geometries of trees in design through the help of computational tools such as 3d scanning and digital fabrication in order to reduce the waste in engineered timber processes.

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{ "gps": "41.450095, 2.132208, 237.754238", "date": "03.03.2019", "time_header": "13:59:27", "scanning_device": { "machine": "bebop_parrot_2", "camera": "14mp_fisheye" }, "computation_device": { "machine": "Andrzej_laptop", "computation_time": "00:37:12" }, "tree": { "characteristics": { "volume": "3.54", "height": "11.76", "species": "plane_tree", "cloud_resolution": "2048598" },

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"geometry": { "1": "[[base_id],[x1,y1, z1,r1], [x2, y2, z2,r2], [x3, y3, z3,r3], (...) ,[xn,yn, zn,rn] ]", "2": "[[base_id],[x1,y1, z1,r1], [x2, y2, z2,r2], [x3, y3, z3,r3], (...) ,[xn,yn, zn,rn] ]", "3": "[[base_id],[x1,y1, z1,r1], [x2, y2, z2,r2], [x3, y3, z3,r3], (...) ,[xn,yn, zn,rn] ]",

x, y, z, r

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FORESTRY DATABASE

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Current scanning methods such as Lidar and photogrammetry present accurate 3d models of the environment in form of pointclouds, however these data are extremely heavy to process in the scale of forest. In this research we explored methods to present this data in a much smaller and easier format for further processes.

There are 3 categories of data that are extracted for each tree.

Data about location of the tree, date and time of scan, scanning device and camera specification are extracted. as well geometrical data such as height of tree, number of branches, angle of branches, radius of tree in different points, volume and etc. are saved in format of JSON file to be compatible with databases.

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DIGITISING FORESTS In this chapter, we explore the process and possibilites of processing bespoke timber pieces using robotic bandsaw.

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ROBOTIC FABRICATION

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ROBOTIC BANDSAWN CURVED LUMBER This chapter researches the possibilites of processing and extracting wood from unprocessed and organic shapes of branches with the use of robotic fabrication. In current processes timber is mostly manufactured with CNC milling processes, a different approach was explored by the use of bandsaw and a robotic arm in order to curved wood pieces with much less time and waste compared to milling process.

For this purpose we developed a bandsaw with a resaw capacity of 45cm to be used as the end effector on the robotic arm. Each piece is 3d scanned using photogrammetry method in order to understand the geometry and curvature of the piece. In this setup the possibility of extracting different slices with the same curvature from the same tree was explored.

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ROBOTIC PROCESS

ROBOTIC SETUP

The robotic setup includes a Kuka KR 150 with maximum reach of 3500mm and payload of 150kg utilized with a Bandsaw. There is a turn table as 7th axis for the robot allowing for 360 rotation of piece in front of the robot for better reachability. The tree log is mounted on a frame which has the interior length of 2000mm. in order to calibrate the piece for the robotic process, we use 3 reference points from the scan of the log, those points are manually read from the robot tool and the 3d model is adjusted based on that. The cutting path is based on the axis line of the log in order to follow the curvature of the tree, this process is further explained in next part.

CUTTING PATH

Cutting path for the robotic bandsaw is based on a 3d scanned model of the log to be cut, skeleton line of the branch is extracted which enables us to cut with the curvature and grain direction of the piece enhancing

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the structual performance of the extracted pieces. The path for the bandsaw should always be perpendicular the cut surface and as well needs to minimize rotations to achieve a more clear cut.

FABRICATION The diagram on top shows the process of extracting tangent lines to the surface of the branch which utilizies us to be able to cut perpendiculat to the surface. The picture on bottom left shows the process of cutting as well as our bandsaw. The diagram in the bottom right illustrates the surface of the cut.

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RO B O T I C B A N D SAW OPERAT I ON

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DIGITISING FORESTS Catalogue of pieces extracted from bandsaw operation.

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C U T P I EC ES

FABRICATION Within 3 days of cutting with our robotic bandsaw were able to extract approximately 30 slices with the thickness of 6mm.

In this catalogue each individual piece as well as group of pieces

extracted from one branch is shown. As the cuts were done on both sides of the branch, we were able to extract the core of each branch as well.

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DIGITISING FORESTS CUT SLICES

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Slices extracted from our bandsaw operation, first two pieces are core pieces of each branch which have more thickness, and other pieces are 6mm thick.

FABRICATION

STEAM BENDING The pieces processed in the robotic bandsaw operation are cut along their curvature, therefore these curved pieces keep the grain structure. Forming processes of wood, specially steam bending, are highly dependent on the grain structure. The better preserved the grain structure in the piece the more stability and possibilites for bending exist.

In this experiment we explored methods to steam bend oak pieces without the use of mold and clamping systems but by using springs that would pull the piece to desired geometry.

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CASE STUDY

CASE STUDY In previous chapters, Digitizing Forest and Fabrication, we discussed methods to scan the forest and trees, managing that data through database and as well fabrication of naturally curved pieces through the robotic bandsaw method.

In this part we explore the possibilities of combination of the technological parts in order to achieve a new workflow for the design and fabrication of naturally curved and locally sourced timber structures.

R o b o ti ca l l y B a n d s a w n S l i c e s

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DIGITISING FORESTS Aerial Scan Valldaura

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As part of the information extracted about each tree, the location of each tree is recorded, this information helps us to find the trees for further purposes such as fabrication.

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SELECTING BRANCHES

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DIGITISING FORESTS COLLECTION OF BRANCHES SOURCED FROM VALLDAURA

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Collection of curved branches extracted from Valldaura forest.

CASE STUDY

SELECTION PROCESS

We collected around 20 curved branches from Valldaura campus site. These branches are mostly oak with the minimum length of 2.5 meters. Curved trees and branches are mostly considered as waste

in many engineered wood processes, therefore we wanted to explore the possibilites to design with and use these considered waste material.

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PREPARING BRANCHES

PREPARATION BEFORE ROBOTIC OPERATION One of the main challenges for robotic bandsaw operation is how to hold the pieces in front of the robot to make sure there is enough room for the bandsaw to operate. We built a frame to hold the pieces from two ends which enables us to cut from all sides of the branch and along the curvature. This frame has the interior length of 2000mm, therefore all the pieces had to be shortened to the size of the frame.

PROCESSING GEOMETRIES

After having the pieces prepared for the robotic setup, each piece is scanned with it’s connection detail to the frame, this helps us to place them fast and accurate on the frame with no need for further calibration.

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Axis curve of each branch is extracted for both design and fabrication purposes. as well the squares that are connection detail to the frame are extracted to calibrate the 3D model for robotic operation.

CASE STUDY Collection of selected branches

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DIGITISING FORESTS Collection of geometries of branches and the selected part of each branch that fits the design.

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D ES I G N A L T ER NA T I V E 1

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CASE STUDY In this part we explored a top-down approach where we fit out resources geometries to the designed structure, this process finds the best fit for the collection of curves in the structure based on the

process explained in matching geometries section. in the illustration below we can see the deviation of the result from the designed structure.

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CASE STUDY This design iteration shows the possibilites of using multiple slices of one branch, pieces that have the same curvature and geometry, in one design system.

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CASE STUDY This design iteration shows the possibilites of using multiple slices of one branch, pieces that have the same curvature and geometry, in one design system.

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REFERENCES 01

Advancing Wood Architecture (2017) - A. Menges, T. Schwinn, O. Krieg Chapter 10 - Hooke Park: applications for timber in its natural form - Martin Self

02 Timber Structure, Properties, Conversion and Use (1996) - H. E. DeschJ. M. Dinwoodie 03 Understanding Wood (1980) - Bruce Hoadley Wood Design (2019) - F. Bianconi, M. Filippucci 04 Digital Part 1, New Workflows for Digital Timber - Tom Svilans et al.

Propagation Characterization of Underground Sewers Towards Autonomous Inspections 05 “Wireless with Drones.” (2018) - C. Rizzo et al.

06 “Autonomous Indoor Object Tracking with the Parrot AR.Drone.” (2016) - A. Chakrabarty et al.

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UAV-Based Photogrammetric Tree Height Measurement for Intensive Forest Monitoring (2019) S.Krause et al.

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BIBLIOGRAPHY Understanding Wood (1980) - Bruce Hoadley Timber: It’s nature and behaviour (1981) - J.M. Dinwoodie Timber Structure, Properties, Conversion and Use (1996) - H. E. DeschJ. M. Dinwoodie Primary Wood Processing: Principles and Practice (2006) - Walker, J. C. Advancing Wood Architecture (2017) - A. Menges, T. Schwinn, O. Krieg Digital Wood Design (2019) - F. Bianconi, M. Filippucci Wood Urbanism (2018) - Daniel Ibanez, Jane Hutton, Kiel Moe Tree Fork Truss (2016) - Zachary Mollica, Martin Self Introduction to Thermo-hydro-mechanical (THM) Wood Processing (2007) - Dick Sandberg, Parviz Navi Timber Construction Manual (2000) - Thomas Herzog “An Integrative Design Process Utilising an Autonomous UAV and Industrial Robots for the Fabrication of Long-Span Composite Structures” - B. Felbrich et al.

“Analysis of Onboard Sensor-Based Odometry for a Quadrotor UAV in Outdoor Environment.” A. Gabdullin et al “Behavioural Production: Autonomous Swarm-Constructed Architecture.” - R. Stuart Smith

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ACKNOWLEDGEMENT This research was developed during the academic year 2018-2019 in MRAC (Master in Robotics and Advanced Construction) within STUDIO I, II and III as well as Software II and III seminars. The topics explore the impact of emerging technologies and robotics in the field of architecture and construction, implementing data and sensor feedback systems in robotic construction processes.

The authors would like to thank Alexandre Dubor and Aldo Sollazzo for their guidance and support during this project. In addtion, we thank our research colleagues and the IaaC faculty and staff.

CREDITSSTART A REUSE PROJECT

WOODWOSE Team

Andrzej Foltman, Filip Bielicki

Mohamed Owaze Ansari and Soroush Garivani MRAC Studio I

Alexandre Dubor and Raimund Krenmueller

Studio I Woodwose member: Sujay Kumarji MRAC Studio II

Aldo Sollazzo and Jose Starsk Lara MRAC Studio III

Alexandre Dubor and Aldo Sollazzo Software I

Daniel Serrano and Jose Starsk Lara

Software II

Mateusz Zwierzycki and Jose Starsk Lara

Robotic Fabrication Expert Kunaljith Chadha

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MRAC Academic Coordinator Laura Ruggeri

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