Nikolas Hohmann M.Sc.
Non-convex & multi-objective Optimization
Contact
nikolas.hohmann@tu-...
work +49 6151 16-25047
fax +49 6151 16-25058
Work
S3|10 409 ¾
Landgraf-Georg Straße 4
64283
Darmstadt
Course | Period | Function |
---|---|---|
Practical Course Control Engineering II | WiSe 2020/21 | Experiment supervision |
System Dynamics and Control Engineering II | SoSe 2021 | Course supervision |
Practical Course Control Engineering II | WiSe 2021/22 | Course & Experiment supervision |
Project Seminar Robotics & Computational Intelligence | SoSe 2022 | Planning and preparation of the task: AI for autonomous control and road sign recognition of a mobile robot (JetBot) Video |
Practical Course Control Engineering II | WiSe 2022/23 | Course & Experiment supervision |
Project Seminar Robotics & Computational Intelligence | SoSe 2023 | Planning and preparation of the task: implementation of a sorting task using a mobile robot (JetBot)GitHub Repo & Video |
Practical Course Control Engineering II | WiSe 2023/24 | Course & Experiment supervision |
Project Seminar Robotics & Computational Intelligence | SoSe 2024 | Planning and preparation of the task: localization, navigation and mapping in the labyrinth using a mobile robot (JetBot) GitHub Repo |
Some of the code of the frameworks developed during my research is available open-source on GitHub.
Robots, in particular UAVs (unmanned aerial vehicles) (e.g. quadrotors), are taking on more and more responsibility in areas where it is either too dangerous or too unpleasant for humans (dangerous & dirty). For example, they are used for rescue missions in disaster areas, to monitor industrial plants, for agricultural purposes or for important logistics tasks (e.g. transporting medicines).
In many cases, the drones fly (semi)-autonomously, meaning they are no longer controlled by a human. In such scenarios, calculating a suitable, optimal path from the quadrotor's starting point to its destination is extremely important in order to avoid dangerous flight maneuvers or even collisions in advance.
To solve the problem of optimal path planning, the following aspects are combined:
Representation
As a basis for subsequent planning and optimization algorithms, the three-dimensional space must first be suitably abstracted or mathematically modelled. There are various approaches to this. One geometric approach, for example, would be to divide the three-dimensional space into cuboid cells on which the path planning takes place.
Problem formulation
Every optimization task is preceded by a suitable formulation of the problem. A path for a quadrotor can be optimal in many respects. The shortest path is not always the best.
Examples of other optimization criteria are energy-optimal, signal-optimal and risk-optimal paths. The problem can also be formulated as a dynamic problem with regard to the optimal behavior of the robot in a network of many robots (swarm behavior).
Optimization
Depending on the problem formulation, various optimization methods and algorithms are available for optimizing the path. Approaches include the use of classical optimization methods ((non-)linear programs), evolutionary algorithms, graph search algorithms or the solution of an optimal control problem in four-dimensional space (three space components and one time component).
Simulation
To visualize and validate the optimal path found, it is essential to test the behaviour of the quadrotor in a simulation environment. This first requires a model of the quadrotor. Its behavior on the calculated path can then be simulated using the Gazebo simulator, for example, with regard to various aspects (kinematic model, kinetic model, dynamic obstacles, wind influences).
I am currently no longer offering theses.
Work Title | Type | Status |
---|---|---|
Methods and Algorithms for Location Optimization of Delivery Hubs in Logistic Scenarios | Seminar paper | canceled (February 2021) |
Facial expression recognition in the wild using static data | Master thesis | completed (March 2021) |
Multiview X-ray Object Detection with Transformers | Master thesis | completed (March 2021) |
Analysis of the Real-time Trajectory Replanning for MAVs based on Soft Constraint Method | Master thesis | completed (April 2021) |
Comparison of different methods for risk modeling of UAV | Seminar paper | completed (May 2021) |
Network optimization with the help of graph theory tools | Master thesis | completed (May 2021) |
Investigation of electrical taxi-operations for hybrid regional aircraft | Bachelor thesis | completed (July 2021) |
Heuristic Topology Optimization of Traffic Networks for Unmanned Aerial Vehicles | Master thesis | canceled (July 2021) |
Multiobjective path planning using the A* algorithm | Bachelor thesis | completed (September 2021) |
Network optimization with the help of Physarum polycephalum | Master thesis | completed (October 2021) |
Methods and Algorithms for Multi-Agent Path Finding | Seminar paper | completed (October 2021) |
Camera distortion calibration using phase-shifting structured light | Master thesis | completed (November 2021) |
Simulation of 3D multi-agent networks under consideration of congestion effects | Master thesis | completed (November 2021) |
Methods and Algorithms for Location Optimization of Delivery Hubs in Logistic Scenarios | Bachelor thesis | completed (March 2022) |
Robust Linear Parameter Varying Control of a Hybrid Unmanned Aerial Vehicle | Master thesis | completed (April 2022) |
Multi-Agent Path Finding in the Three-Dimensional Space | Bachelor thesis | completed (April 2022) |
Study on archiving strategies for multi-objective and multi-modal evolutionary optimization problems | Bachelor thesis | completed (January 2023) |
Study on Machine Learning Methods for Generation and Validation of Quadrotor Trajectories | Master thesis | completed (April 2023) |
Coverage Path Planning with Visual Line of Sight Constraint for UAVs | Bachelor thesis | completed (July 2023) |
Simulation-based Study of the Correlation Between Graph Metrics and the Quality of Traffic Networks | Bachelor thesis | completed (November 2023) |
JetBot in the Maze: Sensor and Actuator Upgrade, Integration, and Testing of a Mobile Robot | Bachelor thesis | completed (April 2024) |

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