Hans Jonas Fossum Moen

• Solend, Tor Atle; Rødningsby, Anders & Moen, Hans Jonas Fossum (2025). Impacts of infrared thermographic image blurring on UAV inspection efficiency of solar power plants. Solar Energy. ISSN 0038-092X. 299. doi: 10.1016/j.solener.2025.113673. Full text in Research Archive Show summary

  Unmanned aerial vehicles (UAVs) inspecting solar photovoltaic (PV) power plants with infrared (IR) cameras is a well-established method to identify hotspots and other defects that radiate heat. With large PV power plants, the task of inspecting the entire area can be overwhelming if the equipment and planning are inadequate. With so much information in each image, the quality of the images will determine if the inspection is useful or not. In previous work, the uncertainty in UAV navigation system parameters has been analyzed and shown to seriously deteriorate image quality and affect inspection efficiency. However, in this study, the analysis is extended to include the effect of image blurring (called motion blur), resulting from the UAV travelling too fast, obscuring vital details in the video image. The novel analysis shows that motion blur is to be regarded as a key factor limiting data quality and data acquisition efficiency. Thus, a comprehensive PV inspection simulator that analyzes the effect of motion blur combined with the UAV navigation performance, is proposed to assess the complete system performance. The simulator is used to evaluate two levels of navigation precision and three camera setups at three different power plant latitudes. To avoid unacceptable motion blurring in the IR images, the maximum UAV flight velocity is determined for all cases. Subsequently, the maximum data acquisition rate of the overall system is calculated. The simulation results show that the design of a UAV system for PV power plant inspection should include a carefully chosen platform that balances navigation performance and image resolution. The image resolution directly affects the maximum flight velocity of the UAV, caused by motion blurring, thus constraining the inspection time and data acquisition rate.

• Stensrud, Mathias; Moen, Hans Jonas Fossum & Bjerknes, Jan Dyre (2021). Information sharing in multi-agent search and task allocation problems. In Haddow, Pauline (Eds.), IEEE Symposium Series on Computational Intelligence 2021. IEEE Press. ISSN 9781728190488. doi: 10.1109/SSCI50451.2021.9660121. Full text in Research Archive
• Solend, Tor Atle; Moen, Hans Jonas Fossum & Rødningsby, Anders (2021). Modelling the impact of UAV navigation errors on infrared PV inspection data quality and efficiency. Conference record of the Photovoltaic Specialists Conference. ISSN 0160-8371. p. 991–996. doi: 10.1109/PVSC43889.2021.9518810. Full text in Research Archive
• Engebråten, Sondre Andreas; Moen, Hans Jonas Fossum; Yakimenko, Oleg A. & Glette, Kyrre (2020). A framework for automatic behavior generation in multi-function swarms. Frontiers in Robotics and AI. doi: 10.3389/frobt.2020.579403. Full text in Research Archive Show summary

  Multi-function swarms are swarms that solve multiple tasks at once. For example, a quadcopter swarm could be tasked with exploring an area of interest while simultaneously functioning as ad-hoc relays. With this type of multi-function comes the challenge of handling potentially conflicting requirements simultaneously. Using the Quality-Diversity algorithm MAP-elites in combination with a suitable controller structure, a framework for automatic behavior generation in multi-function swarms is proposed. The framework is tested on a scenario with three simultaneous tasks: exploration, communication network creation and geolocation of Radio Frequency (RF) emitters. A repertoire is evolved, consisting of a wide range of controllers, or behavior primitives, with different characteristics and trade-offs in the different tasks. This repertoire enables the swarm to online transition between behaviors featuring different trade-offs of applications depending on the situational requirements. Furthermore, the effect of noise on the behavior characteristics in MAP-elites is investigated. A moderate number of re-evaluations is found to increase the robustness while keeping the computational requirements relatively low. A few selected controllers are examined, and the dynamics of transitioning between these controllers are explored. Finally, the study investigates the importance of individual sensor or controller inputs. This is done through ablation, where individual inputs are disabled and their impact on the performance of the swarm controllers is assessed and analyzed.

• Engebråten, Sondre Andreas; Yakimenko, Oleg; Moen, Hans Jonas Fossum & Glette, Kyrre (2018). Towards a Multi-Function Swarm That Adapts to User Preferences, Swarms: From Biology to Robotics and Back. ICRA 2018 Workshop. Harvard University. Full text in Research Archive Show summary

  This paper describes the current state of our work towards a multi-function swarm of UAVs that adapts to user preferences. To achieve this, we employ software simulation and an evolutionary search method for offline generation of a repertoire of controller parameters, covering a wide range of swarm behaviors. By allowing the user to select the appropriate controller parametrization online, the swarm system can adapt to human preferences at run time. To validate our approach, we have performed real-world validation of a few select controllers, on a small fleet of modified commercial off-the-shelf UAVs. The results so far are encouraging, and we plan to extend the range of swarm behaviors and increase the swarm size in the future.

• Engebråten, Sondre Andreas; Moen, Hans Jonas Fossum; Yakimenko, Oleg & Glette, Kyrre (2018). Evolving a Repertoire of Controllers for a Multi-function Swarm. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. doi: 10.1007/978-3-319-77538-8_49. Full text in Research Archive Show summary

  Automated design of swarm behaviors with a top-down approach is a challenging research question that has not yet been fully addressed in the robotic swarm literature. This paper seeks to explore the possibility of using an evolutionary algorithm to evolve, rather than hand code, a wide repertoire of behavior primitives enabling more effective control of a large group or swarm of unmanned systems. We use the MAP-elites algorithm to generate a repertoire of controllers with varying abilities and behaviors allowing the swarm to adapt to user-defined preferences by selection of a new appropriate controller. To test the proposed method we examine two example applications: perimeter surveillance and network creation. Perimeter surveillance require agents to explore, while network creation requires them to disperse without losing connectivity. These are distinct application that have drastically different requirements on agent behavior, and are a good benchmark for our swarm controller optimization framework. We show a performance comparison between a simple weighted controller and a parametric controller. Evolving controllers allows for specifying desired behaviors top-down, in terms of objectives to solve, rather than bottom-up.

• Rauniyar, Ashish; Engelstad, Paal E. & Moen, Hans Jonas Fossum (2018). A New Distributed Localization Algorithm Using Social Learning Based Particle Swarm Optimization for Internet of Things. IEEE Vehicular Technology Conference (VTC) Proceedings. ISSN 1550-2252. doi: 10.1109/VTCSpring.2018.8417665. Full text in Research Archive Show summary

  Emerging applications in the Internet of Things (IoT) will depend on the accurate location of thousands of deployed sensors. However, accurate localization of deployed sensors nodes is a classical optimization problem which falls under NP-hard class of problems. Therefore in this work, we propose a new distributed localization algorithm using social learning based particle swarm optimization (SL-PSO) for IoT. With SL-PSO algorithm, we aim to do precise localization of deployed sensor nodes and reduce the computational complexity which will further enhance the lifetime of these resource-constrained IoT sensor nodes. Extensive simulations are carried out to show the effective performance of the SL-PSO algorithm in accurate localization. Experimental results depict that SL-PSO algorithm can not only increase convergence rate but also significantly reduce average localization error compared to traditional particle swarm optimization (PSO) and its other variants.

• Engebråten, Sondre; Moen, Hans Jonas Fossum & Glette, Kyrre (2017). Meta-heuristics for improved RF emitter localization. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. doi: 10.1007/978-3-319-55792-2_14. Full text in Research Archive Show summary

  Locating Radio Frequency (RF) emitters can be done with a number of methods, but cheap and widely available sensors make the Power Difference of Arrival (PDOA) technique a prominent choice. Predicting the location of an unknown RF emitter can be seen as a continuous optimization problem, minimizing the error w.r.t. the sensor measurements gathered. Most instances of this problem feature multi-modality, making these challenging to solve. This paper presents an analysis of the performance of evolutionary computation and other meta-heuristic methods on this real-world problem. We applied the Nelder-Mead method, Genetic Algorithm, Covariance Matrix Adaptation Evolutionary Strategies, Particle Swarm Optimization and Differential Evolution. The use of meta-heuristics solved the minimization problem more efficiently and precisely, compared to brute force search, potentially allowing for a more widespread use of the PDOA method. To compare algorithms two different metrics were proposed: average distance miss and median distance miss, giving insight into the algorithms’ performance. Finally, the use of an adaptive mutation step proved important.

• Hovland, Harald; Tørresen, Jim; Moen, Hans Jonas Fossum & Hansen, Nikolai Bjørnestøl (2013). Many Objective Optimization Using Taxi-Cab Surface Evolutionary Algorithm. In Purshouse, R.; Fleming, P.; Fonseca, C.M; Greco, S. & Shaw, J. (Ed.), Proceedings of the 7th Evolutionary Multi-Criterion Optimization (EMO 2013). Springer. ISSN 9783642371394. p. 656–668. doi: 10.1007/978-3-642-37140-0_13. Full text in Research Archive
• Moen, Hans Jonas Fossum; Kristoffersen, Stein & Sparr, Trygve (2012). Improved radar detection using evolutionary optimised filter. IET Radar, Sonar & Navigation. ISSN 1751-8784. 6(9), p. 803–812. doi: 10.1049/iet-rsn.2012.0099. Full text in Research Archive
• Nyhavn, Ragnhild; Moen, Hans Jonas Fossum; Farsund, Øystein & Rustad, Gunnar (2011). Optimal classification of standoff bioaerosol measurements using evolutionary algorithms. Proceedings of SPIE, the International Society for Optical Engineering. ISSN 0277-786X. doi: 10.1117/12.883919. Full text in Research Archive Show summary

  Early warning systems based on standoff detection of biological aerosols require real-time signal processing of a large quantity of high-dimensional data, challenging the systems efficiency in terms of both computational complexity and classification accuracy. Hence, optimal feature selection is essential in forming a stable and efficient classification system. This involves finding optimal signal processing parameters, characteristic spectral frequencies and other data transformations in large magnitude variable space, stating the need for an efficient and smart search algorithm. Evolutionary algorithms are population-based optimization methods inspired by Darwinian evolutionary theory. These methods focus on application of selection, mutation and recombination on a population of competing solutions and optimize this set by evolving the population of solutions for each generation. We have employed genetic algorithms in the search for optimal feature selection and signal processing parameters for classification of biological agents. The experimental data were achieved with a spectrally resolved lidar based on ultraviolet laser induced fluorescence, and included several releases of 5 common simulants. The genetic algorithm outperform benchmark methods involving analytic, sequential and random methods like support vector machines, Fisher's linear discriminant and principal component analysis, with significantly improved classification accuracy compared to the best classical method.

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• Solend, Tor Atle; Moen, Hans Jonas Fossum & Rødningsby, Anders (2022). Positioning a UAV for Infrared Inspection of Several Rows of Solar Panels Simultaneously. Proceedings of the European Photovoltaic Solar Energy Conference. ISSN 2196-100X. Full text in Research Archive Show summary

  When photovoltaic (PV) solar panels in a PV power plant deteriorate after some time of operation, a thermographic inspection with unmanned aerial vehicles (UAVs) can be necessary. Often, faulty panels, or cells within panels, radiate heat that is visible with infrared (IR) cameras. When performing a thermographic inspection of solar panel faults from a UAV, the correct choice of position for the UAV depends on several factors. The camera's pixel resolution and field of view (FOV) set a distance threshold between the panel and the UAV according to a minimum resolution requirement. This threshold is also dependent on the angle between the panel and the UAV. Because of the vast size of some PV power plants, inspecting several rows of panels simultaneously is often appropriate. Latitude-dependent row-to-row distances limit the number of rows which fit inside the FOV which leads to site-dependent strategies. This paper analyzes several key factors, concerning the choice of position for a thermographic inspection of a solar PV power plant with a UAV.

• Solend, Tor Atle; Moen, Hans Jonas Fossum & Rødningsby, Anders (2022). Positioning a UAV for Infrared Inspection of Several Rows of Solar Panels Simultaneously. Full text in Research Archive
• Stensrud, Mathias; Moen, Hans Jonas Fossum & Bjerknes, Jan Dyre (2021). Information sharing in multi-agent search and task allocation problems. Full text in Research Archive
• Moen, Hans Jonas Fossum; Glette, Kyrre; Nygaard, Tønnes Frostad & Johnsrud, Mette (2018). Fem felt der vi får en førerløs fremtid. [Internet]. Titan.uio.no. Full text in Research Archive
• Engebråten, Sondre Andreas; Yakimenko, Oleg; Moen, Hans Jonas Fossum & Glette, Kyrre (2018). Evolving a Repertoire of Controllers for a Multi-Function Swarm. Full text in Research Archive
• Engebråten, Sondre Andreas; Yakimenko, Oleg; Moen, Hans Jonas Fossum & Glette, Kyrre (2018). Towards a Multi-Function Swarm That Adapts to User Preferences. Full text in Research Archive
• Moen, Hans Jonas Fossum; Hansen, Nikolai Bjørnestøl; Hovland, Harald & Tørresen, Jim (2013). Many Objective Optimization Using Taxi-Cab Surface Evolutionary Algorithm. Full text in Research Archive
• Nyhavn, Ragnhild; Moen, Hans Jonas Fossum; Farsund, Øystein & Rustad, Gunnar (2011). Optimal classification of standoff bioaerosol measurements using evolutionary algorithms. Full text in Research Archive
• Holmboe, Tor; Høydal, Tor-Odd; Moen, Hans Jonas Fossum & Kristoffersen, Stein (2011). Norwegian approach and vision for new EW technologies and techniques. Full text in Research Archive
• Moen, Hans Jonas Fossum & Hovland, Harald (2011). Spanning the Pareto Front of a Counter Radar Detection Problem. Full text in Research Archive
• Moen, Hans Jonas Fossum; Ulversøy, Tore & Nyhavn, Ragnhild (2010). Evolutionary Computation at the Norwegian Defence Research Establishment. Full text in Research Archive
• Kristoffersen, Stein & Moen, Hans Jonas Fossum (2010). The Complexity of Optimal DRFM Jamming Techniques Developed Using Multiobjective Evolutionary Algorithms. Full text in Research Archive Show summary

  The EKKO II experimental radar jammer has recently been used to demonstrate denial of detection jamming techniques against experimental radars employing the constant false alarm rate detection algorithm. Effective denial of detection was then demonstrated using 10 to 30 dB less jamming power than what would be required of conventional noise jammers to achieve the same performance. EKKO II is an experimental radar jammer based on digital RF memory (DRFM) developed at the Norwegian Defence Research Establishment (FFI). In these studies it has been shown how multiobjective evolutionary algorithms are capable of finding the Pareto optimal solutions in the 2 dimensional objective space of jammer transmitting power versus detectability by manipulating the constant false alarm rate decision domain of the radar. However, in the energy domain of the radar the jamming produces structures of high complexity, potentially exposing the jamming to radar systems operating man-in-the-loop. Therefore, in this paper, an extended 3 dimensional multiobjective optimization is performed in order to analyze the trade-offs between jamming technique complexity and the two other objectives of jammer transmitting power and detectability. A suitable complexity metric characterizing how natural or noise-like the optimal jammer matrices look is proposed and compared to more traditional entropy-based metrics. It will be shown how this complexity metric is more in line with human image characterization

• Moen, Hans Jonas Fossum & Kristoffersen, Stein (2008). Multi-Resistant Radar Jamming Using Genetic Algorithms. Full text in Research Archive
• Kristoffersen, Stein & Moen, Hans Jonas Fossum (2008). Denial Jamming Technique Development Against Pulse-Doppler Radars Using Genetic Algorithms. Full text in Research Archive
• Moen, Hans Jonas Fossum (2013). Optimizing Software-Defined Radar and Radar Electronic Warfare Systems using Evolutionary Algorithms. Akademika forlag. Full text in Research Archive

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