Autonomous Systems LabOpen OpportunitiesAquaculture is an important global contributor to the production of seafood for human consumption. Currently the industry is phasing several challenges which demand adaptation of novel technologies and methods to move the production from manual and experience-based to more objective approaches [1]. There is need for objective monitoring and inspection of fish conditions to contribute to better fish health and secure fish welfare. - Engineering and Technology, Information, Computing and Communication Sciences
- Master Thesis, Semester Project
| The fish farming industry has seen a rapid growth over the last decades and is today a key provider of seafood [1]. The properties of seawater put limitations on sensor systems that can be used for underwater navigation. Moreover, additional challenges related to robust localization and mapping are imposed by the fish farm environment that must be accounted for when utilizing Unmanned Underwater Vehicles (UUVs) [2]. - Engineering and Technology, Information, Computing and Communication Sciences
- Master Thesis, Semester Project
| Unmanned underwater vehicles (UUVs) have become indispensable tools for inspection, maintenance, and repair (IMR) operations in the underwater domain. Path planning and collision avoidance are fundamental concepts for enabling autonomy for mobile robots. This remains a challenge, particularly for underwater vehicles operating in complex and dynamically changing environments such as fish farms [1]. - Engineering and Technology, Information, Computing and Communication Sciences
- Master Thesis, Semester Project
| Use of robotics such as Unmanned Underwater Vehicles (UUVs) has become essential for several fish farming companies to address current challenges [1]. In particular, Remotely Operated Vehicles (ROVs) have been used for several monitoring operations such as inspection of nets and mooring lines, as well as monitoring and inspection of water quality, the cage environment and fish population. A first step towards autonomous control of robot actions under such conditions is therefore to establish more realistic models and simulation environment of the dynamic cage environment that predict and incorporate structural deformations and the impacts from the surrounding environment, and interactions between these [2]. - Engineering and Technology, Information, Computing and Communication Sciences
- Master Thesis, Semester Project
| Fish Farming industry is facing several challenges, with one of the major challenges being related to objective inspection of fish cages to detect irregularities such as holes, biofouling condition [1]. Earlier studies showed that 41% of the escapees from fish farms in Norway are caused by holes in fish cages [2] and the biofouling prevention is crucial to preserve good conditions for the fish growth [1]. - Engineering and Technology, Information, Computing and Communication Sciences
- Master Thesis, Semester Project
| Recent work on multi-robot systems with collaborative autonomy has made significant strides towards developing robotic
teams capable of performing complex tasks in real, complex settings as shown above. Right at the core of such capabilities is
the capability to collaboratively perform SLAM (Simultaneous Localization And Mapping) within such multi-agent systems that
can operate efficiently and in challenging real-world environments, which is the main goal of this project.
The aim of this project is to develop key components of a multi-robot SLAM system that is robust in challenging environments
and adaptable to different scenarios, ranging from environmental monitoring to search-and-rescue operations. The envisioned
system will research integrating complementary onboard sensor modalities (e.g., cameras, LiDAR, and IMU), machine learning
methods, and distributed communication systems to provide precise localization and mapping exhibiting resilience to sensor
failure and sufficient efficiency to be deployed onboard small platforms, such as drones. The student will be guided to work
towards a system architecture that can enable effective testing and optimization in state-of-the-art simulation engines, with the
ultimate goal of reducing the gap between simulated experiments and real tests. The outlook is to create a system that can be
employed onboard a small swarm of drones in a real setting. - Computer Vision, Intelligent Robotics
- Master Thesis
| Automating drone navigation promises to revolutionise the way we conduct a wide variety of tasks, such as agricultural monitoring, industrial inspection, and disaster relief scenarios. Equipping a drone with the capability to autonomously explore and map previously unseen environments using onboard sensors and algorithms forms the basis of autonomy. While there has been tremendous progress in this area over the past few years [1-5], existing systems still lack reliability and adaptability to the challenges and complexity of real settings, which is crucial for the deployment of this technology in actual missions. In particular, performing robust navigation and mapping in highly dynamic environments (e.g., forests) remains an open challenge.
Following promising leads from the state-of-the-art and our in-house navigation stack, the goal of this project is to develop the capability to deal with increasingly dynamic and complex scenarios. The student will be guided towards leveraging the multi-sensor capabilities of a LiDAR-Visual-Inertial payload being developed in the lab to research approaches for perception and mission planning that can fuse information from the different sensors and capture high-fidelity representations of challenging dynamic environments. Initially, the student will work within a realistic simulation environment and then deploy and test their work onboard a real drone in a real setting.
- Computer Vision, Intelligent Robotics
- Master Thesis
| Digital environments, or digital twins, allow for design, prototyping, and testing in the virtual world before moving to the real world, thus accelerating development and reducing costs. A digital twin of a farm supports crop operations such as scheduling a harvest or predicting a yield, while agritech companies can develop farm automation robots using a digital twin. The goal of this project is to develop 3D Reconstruction and localization strategies that are capable to identify temporal invariant areas and properties in crop environments during the production season. The main target is to be able to match the same plants over time. - Computer Vision, Intelligent Robotics
- Master Thesis, Semester Project
| Bundle Adjustment (BA) is a critical optimization technique used to refine a visual reconstruction by jointly estimating the 3D scene structure and the viewing parameters. Traditional BA approaches primarily focus on geometric features and might struggle in highly unstructured scenarios, such as natural environments.
This project aims to extend the Bundle Adjustment methodology by incorporating higher-level features extracted from semantic segmentation. The integration of semantic information aims to provide contextually relevant and more discriminative data to the adjustment process, thereby improving its accuracy and robustness.
- Computer Vision, Image Processing
- Master Thesis, Semester Project
| Precise calibration is a crucial part of many robotics systems. Especially, the calibration of cameras is necessary for various computer vision applications, such as Simultaneous Localization and Mapping (SLAM). Often the respective sensor setups are assembled, calibrated once and then used for several weeks or months without being recalibrated. Environmental influences, such as temperature changes, vibration or impacts, might, however, influence the sensor’s calibration over time. - Computer Vision, Image Processing, Intelligent Robotics, Mechanical Engineering
- Master Thesis, Semester Project
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