Thesis topics I coordinate, for both bachelor's students and PhD candidates. I supervise around 10 bachelor theses each year.
Topics available for undergraduate students in robotics, computer vision, AI, and embedded systems.
Two USB cameras reconstruct the object, which is approximated by a geometric primitive (superquadric); a 3D-printed robotic arm computes and executes the grasp.
Mathematics: epipolar geometry, triangulation, Levenberg–Marquardt non-linear optimization, inverse kinematics.
Equipment: 3D-printed servo arm, 2 USB cameras · Estimated budget: €60–80 · Students: 1 student
Determining the camera–end-effector transform by solving AX = XB, comparing classical methods and measuring the error on a real arm.
Mathematics: SO(3)/SE(3) groups, quaternions, SVD, least squares.
Equipment: 3D-printed arm, webcam, printed checkerboard · Estimated budget: €50–70 · Students: 1 student
The arm is driven directly from the error measured in the image (IBVS), with a stability analysis of the closed loop.
Mathematics: interaction matrix, Jacobian, Lyapunov functions.
Equipment: 3D-printed arm, webcam · Estimated budget: €50–70 · Students: 1 student
The robot learns a motion from demonstrations (SO-101 leader–follower arm) and generalizes it to new object positions; training runs on Google Colab.
Mathematics: Dynamic Movement Primitives (differential equations), regression, neural networks.
Equipment: open-source SO-101 arm or 3D-printed arm + simulation · Estimated budget: €70–230 · Students: 1 student
The dataset is generated in Blender; the network detects keypoints from which the object position and orientation are computed and validated with a webcam.
Mathematics: the PnP problem, RANSAC, rotation representations.
Equipment: webcam (optionally a 3D-printed arm) · Estimated budget: €20–70 · Students: 1 student
Deriving the Euler–Lagrange model of a 2-DOF arm, identifying its parameters from measurements and comparing it with a hybrid physics + neural network model.
Mathematics: Lagrange equations, linear-in-parameters regression, optimization.
Equipment: 3D-printed planar arm, DC motors with encoders, current sensor, ESP32/STM32 · Estimated budget: ~€50 · Students: 1 student
Estimating the trajectory without GPS by fusing camera-based motion with inertial data; evaluation on a known path.
Mathematics: extended Kalman filter, epipolar geometry, rigid-body kinematics.
Equipment: own smartphone, laptop · Estimated budget: ~€0 · Students: 1 student
Building the robot, deriving its kinematic model and implementing a trajectory-tracking controller on a microcontroller.
Mathematics: kinematic Jacobian, non-linear control, tracking-error analysis.
Equipment: 4 mecanum wheels, 4 encoder motors, ESP32/STM32, 3D-printed chassis · Estimated budget: €60–90 · Students: 1 student
Lane detection with a neural network and comparison of Pure Pursuit, Stanley and MPC controllers on a steered vehicle.
Mathematics: bicycle model, homography, model predictive control (MPC).
Equipment: 3D-printed chassis with steering servo, ESP32-CAM, laptop · Estimated budget: €50–80 · Students: 1 student
Real-time image segmentation with a quantized network, projected into a probabilistic occupancy grid.
Mathematics: Bayesian log-odds update, perspective projection, network quantization.
Equipment: Raspberry Pi + camera (or laptop processing) · Estimated budget: €20–120 · Students: 1 student
The hand skeleton detected by the camera is mapped into the robot workspace, with tremor filtering.
Mathematics: forward and inverse kinematics, coordinate transforms, Kalman filter.
Equipment: 3D-printed arm, webcam · Estimated budget: €40–60 · Students: 1 student
The lidar is rotated on two axes to obtain a point cloud of a room; scans from several positions are registered into a single model.
Mathematics: spherical coordinates, measurement error modelling, ICP, SVD.
Equipment: 1D lidar (available), 2 servos, 3D-printed mount · Estimated budget: ~€15 · Students: 1 student
The arm behaves as a mass–spring–damper system on contact with a human; torque is estimated from motor current (SimpleFOC).
Mathematics: manipulator dynamics, second-order differential equations, stability.
Equipment: BLDC gimbal motors, AS5600 magnetic encoders, FOC driver, 3D-printed parts · Estimated budget: €70–100 · Students: 1 student
Faults (imbalance, friction, backlash) are induced on a 3D-printed axis and detected from current and vibration with an autoencoder.
Mathematics: FFT, spectral analysis, Mahalanobis distance, neural networks.
Equipment: DC motor, INA219 current sensor, MPU6050 accelerometer, ESP32 · Estimated budget: €30–40 · Students: 1 student
A motor with encoder spins the lidar to obtain a 2D scan, and an occupancy map is built from one of the research institute's mini-robots.
Mathematics: polar coordinates, motion-distortion correction, scan matching, Bayesian log-odds update.
Equipment: 1D lidar and mini-robot (available), encoder motor · Estimated budget: €10–15 · Students: 1 student
A 3D-printed delta robot with a suction gripper picks parts from a moving conveyor and sorts them by category. Modules communicate through interfaces agreed from the start (ROS2/MQTT).
Modules (one student each):
Equipment: 3 servos/steppers, vacuum pump with suction cup, encoder motor, camera, microcontroller, 3D-printed parts; 1D lidar available · Estimated budget: €100–150 total
A differential-drive robot that transports parts in a lab or warehouse; each student develops a distinct module.
Modules (one student each):
Equipment: 3D-printed chassis, encoder motors, low-cost LIDAR (LD06/LD19), Raspberry Pi, ESP32 · Estimated budget: €180–250 total
Several of the research institute's mini-robots work together in an arena, localized by a ceiling-mounted camera.
Modules (one student each):
Equipment: mini-robots available at the institute, webcam · Estimated budget: ~€20 total
Doctoral research directions I coordinate.
Development of a Digital Twin architecture for real-time monitoring of an industrial robotic line; AI-based predictive maintenance models for anomaly detection and failure anticipation; operational optimization to reduce downtime; and intelligent cybersecurity/resilience mechanisms for industrial robotic lines.
Interpretable hybrid models for fixed-base robots combining classical physics models with AI-based correction; online self-modelling of kinematic/dynamic deviations; explainable residual learning for non-linearities, friction, and backlash; adaptive control based on uncertainty estimation; experimental validation on a lab robotic platform.
A framework combining AI-based cybersecurity mechanisms with functional safety strategies for autonomous drone fleets, including Trustworthy AI; real-time threat detection and mitigation on resource-constrained microcontroller platforms; and rigorous testing and validation in realistic scenarios.