Robotic systems suffer from inaccuracies stemming from a variety of sources, such as manufacturing errors, backlash, thermal expansion and compliance, limiting their absolute positioning accuracy. While a number calibration methods exist to address these issues for articulated robots, they do not extend to the more intricate case of large prismatic joints. In this project, we develop a robot model for gantry systems enabling us to perform effective end-to-end calibration.

Plasma coating is a process in which a protective layer is produced by spraying metal or ceramic powder onto a workpiece at immense temperature and velocity, typically through a robot. To achieve a high coating quality, the process puts strict requirements on the tool path, leading to very fast robot movements. The goal of this project to develop a path planning algorithm that finds an optimal toolpath, ensuring uniform coating thickness while respecting any constraints on the process parameters and robot acceleration.

This research introduces a novel robot-agnostic calibration method to enhance the positioning accuracy of articulated robots. The proposed robot model is based on the product of exponentials formula and allows to compensate for geometric and backlash errors as well as for thermal drift. It reduces the average positioning error to 0.15mm.

The success of robotic milling is dependent on the robot path accuracy. This work explores an iterative learning control approach for simple robotic milling operations, leveraging the high repeatability of the robot. A surface flatness better than +-50um and holes with a positional error below 25um were obtained during the processing of an aluminium workpiece.

This research addresses the challenge of time-consuming robot teaching methods by introducing a CAD 6D space mouse combined with an IMU as an input device. Implemented in Python, the method offers robot-agnostic, intuitive, and real-time control at the joint level, effectively managing singularities and joint limits. User studies demonstrate a twofold improvement in accuracy and speed compared to existing solutions.

In this work we examined teaching dynamic and complex time dependent movements found for example in painting or disassembling applications using the wand teaching approach and a Creaform C-Track industrial tracker. The robot-agnostic method, implemented in Python controlling the robot at the joint level, demonstrates promising performance in various benchmarks, identifying core challenges and proposing novel solutions for future exploration.

In wire arc additive manufacturing, achieving the desired final workpiece geometry relies on depositing layers of weld beads, with each bead altering the current geometry and setting the boundary for subsequent depositions. We devised Deep Learning and numerical optimization models. These models predict the evolution of the workpiece based on the current geometry and process parameters, offering valuable insights for simulation, planning, and control.

This work introduces an autonomous, robot-agnostic method for autonomous 3D part measurement. It addresses manual path planning issues, efficiently locating parts, calculating optimal view poses, solving the travelling salesman problem and performing collision-aware path planning. Experimental results confirm its reliability, making it a cost-effective solution when combined with our recently developed in-line robot calibration method which does not need an external tracking system.

This work aimed to validate the secondary kinematic system approach for robotic milling. A one-dimensional prototype was developed with an advanced feedforward controller to linearize the system, for successive modeling of the robot as a number of mutually interconnected masses. The system was identified using a custom Basin-hopping algorithm. A remarkable 200-fold increase in dynamic stiffness outside the 10-20Hz range and precise movements with accelerations up to 2G's and <0.1mm error could be achieved.

This work addresses the challenge of achieving precision and productivity in robotic milling with metals. A 3D high-performance secondary kinematic system, controlled by Beckhoff PLC with 8khz, enables the movement of a 22kW milling spindle with high accelerations of up to 4G's and high overall stiffness achieved by control. Manufacturing and assembly, as well as as first implementation of control have been completed, setting the stage for milling trials and exciting research in the coming years.

This work introduces autonomous robot arm-based workspace exploration, essential for scene reconstruction and path planning. The robot uses a hand-mounted lidar to effectively explore and reconstruct its own workspace, while avoiding collisions. The process involves engineered robot motion, clustering of unknown space, and Next-Best-View problem solving. Experiments on benchmark scenes and a hardware demonstration in an industrial setting confirm its effectiveness.

The cost of industrial foams is directly tied to density control during manufacturing, impacting production expenses. Our solution involves the development of an inline density measurement system coupled with model predictive control loops. This innovation minimizes density while ensuring critical quality characteristics, a collaborative effort with process experts and data scientists from our industry partners.

Traditional robot calibration relies on costly external tracking systems. In this study, an affordable 3D measurement device is attached to the robot's tool-flange to measure arbitrarily placed spheres, enabling precise robot model parameter identification via optimization. Numerical simulations, even with substantial measurement noise, validate the method's viability.

The heart of autonomous warehouses lies in their stacker cranes, and any unplanned downtime translates into inaccessible goods and delayed deliveries. Our approach involves a monitoring system complemented by models that predict potential failures in advance. This system enables scheduled maintenance, leading to a reduction in unplanned downtime, and increased reliability in the operations of autonomous warehouses.

Addressing the fragmentation in robot programming and integration ecosystems, this project aims to develop a universal software capable of controlling any industrial robot in real-time. It encapsulates complexity, fosters solution reusability, and democratizes robot programming, making it accessible and efficient.

Predicting the temperature field evolution during Wire Arc Additive Manufacturing (WAAM) with short computational times is essential for optimizing the process. This work introduces an innovative numerical scheme for fast thermal simulation for WAAM, combining process knowledge and high performance computing. The computational time is shorter by a factor of 10 compared to the simulated manufacturing time, allowing integration in iterative planning.

Producing new workpieces with Wire Arc Additive Manufacturing (WAAM) First-Time-Right is challenged by the uncertainty of the process behaviour. This work developed the basis for a model process planning approach for WAAM. The resulting framework is composed by the models, control approaches and system requirements needed for a successful first time right approach.

This work addresses external forces and robot model inaccuracies by utilizing a Creaform optical tracker for real-time pose tracking. A novel prediction technique, leveraging joint state information, mitigates high measurement delay. Impressive outcomes feature a 50μm mean path error at high welding speeds and compensation for external forces up to 5Hz, exceeding the robot's rated payload.