2020 |
Manoonpong, Poramate; Xiong, Xiaofeng; Larsen, Jørgen Christian Closed-loop dynamic computations for adaptive behavior (articles based on SAB2018 conference) Miscellaneous 2020. @misc{manoonpong2020closed, title = {Closed-loop dynamic computations for adaptive behavior (articles based on SAB2018 conference)}, author = {Poramate Manoonpong and Xiaofeng Xiong and Jørgen Christian Larsen}, year = {2020}, date = {2020-01-01}, publisher = {SAGE Publications Sage UK: London, England}, keywords = {}, pubstate = {published}, tppubtype = {misc} } |
Manoonpong, Poramate; Xiong, Xiaofeng; Larsen, Jørgen Christian Closed-loop dynamic computations for adaptive behavior (articles based on SAB2018 conference) Miscellaneous 2020. @misc{manoonpong2020closedb, title = {Closed-loop dynamic computations for adaptive behavior (articles based on SAB2018 conference)}, author = {Poramate Manoonpong and Xiaofeng Xiong and Jørgen Christian Larsen}, year = {2020}, date = {2020-01-01}, publisher = {SAGE Publications Sage UK: London, England}, keywords = {}, pubstate = {published}, tppubtype = {misc} } |
Manoonpong, Poramate; Xiong, Xiaofeng; Larsen, Jørgen Christian Closed-loop dynamic computations for adaptive behavior (articles based on SAB2018 conference) Miscellaneous 2020. @misc{manoonpong2020closedc, title = {Closed-loop dynamic computations for adaptive behavior (articles based on SAB2018 conference)}, author = {Poramate Manoonpong and Xiaofeng Xiong and Jørgen Christian Larsen}, year = {2020}, date = {2020-01-01}, publisher = {SAGE Publications Sage UK: London, England}, keywords = {}, pubstate = {published}, tppubtype = {misc} } |
2018 |
Ignasov, Jevgeni The Motor Control Development of a Compliant Robotic Arm Miscellaneous Bachelor thesis, University of Southern Denmark, 2018. @misc{Jev2018, title = {The Motor Control Development of a Compliant Robotic Arm}, author = {Jevgeni Ignasov }, year = {2018}, date = {2018-01-03}, howpublished = {Bachelor thesis, University of Southern Denmark}, keywords = {}, pubstate = {published}, tppubtype = {misc} } |
Svane, Camilla; Tobiasen, Camilla Dung Beetle Inspired Robot Miscellaneous University of Southern Denmark, 2018. @misc{Camilla2018, title = {Dung Beetle Inspired Robot}, author = {Camilla Svane and Camilla Tobiasen}, year = {2018}, date = {2018-01-03}, howpublished = {University of Southern Denmark}, keywords = {}, pubstate = {published}, tppubtype = {misc} } |
2017 |
Gorjup, Gal EEG Signal Processing and Classification for Advanced Human-Machine Interfaces Masters Thesis University of Ljubljana, 2017, (Co-supervision between SDU and University of Ljubljana). @mastersthesis{Gal2017, title = {EEG Signal Processing and Classification for Advanced Human-Machine Interfaces}, author = {Gorjup, Gal}, year = {2017}, date = {2017-09-10}, school = {University of Ljubljana}, abstract = {EEG-based human-machine interfaces o er an alternative means of interaction with the environment that relies solely on interpreting brain activity. They can not only signi cantly improve the life quality of people with neuromuscular disabilities, but also present a wide range of opportunities for industrial and commercial adoption. This thesis focuses on processing and classi cation of motor imagery EEG recordings. The used data consisted of three data sets, two of which were recorded within this project. A software framework that supports EEG signal ltering, feature extraction and classi cation was developed and successfully used. Selected instances of FIR and IIR digital lters were implemented and compared, showing that the latter was more appropriate for the current application. Several feature extraction methods were implemented, including band power, autoregressive modelling, Hjorth parameters and FFT-based features. An LDA-based linear classi cation method was implemented and tests have shown that it performs best with the band power features. Additionally, an LSTM-based neural classi cation method was implemented and optimised in terms of architecture shape, learning rate and weight decay parameters. Through optimisation, it was found that this method also performs best with band power features. The implemented classi ers were compared based on the band power feature, using the available data sets recorded with wet and dry electrodes, with monopolar and bipolar montage. The two methods achieved similar performance in terms of prediction accuracy, although the linear classi er was for the given data and training approach found to be favourable due to its robustness and low complexity.}, note = {Co-supervision between SDU and University of Ljubljana}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } EEG-based human-machine interfaces o er an alternative means of interaction with the environment that relies solely on interpreting brain activity. They can not only signi cantly improve the life quality of people with neuromuscular disabilities, but also present a wide range of opportunities for industrial and commercial adoption. This thesis focuses on processing and classi cation of motor imagery EEG recordings. The used data consisted of three data sets, two of which were recorded within this project. A software framework that supports EEG signal ltering, feature extraction and classi cation was developed and successfully used. Selected instances of FIR and IIR digital lters were implemented and compared, showing that the latter was more appropriate for the current application. Several feature extraction methods were implemented, including band power, autoregressive modelling, Hjorth parameters and FFT-based features. An LDA-based linear classi cation method was implemented and tests have shown that it performs best with the band power features. Additionally, an LSTM-based neural classi cation method was implemented and optimised in terms of architecture shape, learning rate and weight decay parameters. Through optimisation, it was found that this method also performs best with band power features. The implemented classi ers were compared based on the band power feature, using the available data sets recorded with wet and dry electrodes, with monopolar and bipolar montage. The two methods achieved similar performance in terms of prediction accuracy, although the linear classi er was for the given data and training approach found to be favourable due to its robustness and low complexity. |
Kapilavai, Aditya Modeling and Control of Dung Beetle-Like Robots Masters Thesis University of Southern Denmark, 2017. @mastersthesis{Adit2017, title = {Modeling and Control of Dung Beetle-Like Robots}, author = {Kapilavai, Aditya}, year = {2017}, date = {2017-06-01}, school = {University of Southern Denmark}, abstract = {African Dung beetles that belong to the species of Scarabaeus zambesianus possess a distinctive feature in each of its leg structures. They can use their legs not only to walk but also to manipulate objects. This thesis presents the design and development of real dung beetle-like leg by conducting biological experiments on real insect front leg using microCT scans. Using these data, a robot leg kinematic model is developed and kinematic control is applied. Due to novel design with additional degrees of freedom controlling orientation of the second joint, virtual and physical prototype kinematics is able to emulate real beetle’s behavior and successfully mimics its degrees of freedom. Furthermore, simulations were conducted to analyse workspace of the leg, the performance of the kinematic control and the trajectory planning of the leg for locomotion and object manipulation based on video recordings of the African dung beetle. Experiments were conducted on the developed prototype to test the kinematic algorithm. Robotic design process based on biological experiments and modeling corrected by feedback from biologists offered novel methodology to improve the workspace of insect-like robots. This may increase both the energy efficiency for bio-inspired robots and their potential for complex manipulation tasks, such as demanding search and rescue missions in the wake of natural disasters.}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } African Dung beetles that belong to the species of Scarabaeus zambesianus possess a distinctive feature in each of its leg structures. They can use their legs not only to walk but also to manipulate objects. This thesis presents the design and development of real dung beetle-like leg by conducting biological experiments on real insect front leg using microCT scans. Using these data, a robot leg kinematic model is developed and kinematic control is applied. Due to novel design with additional degrees of freedom controlling orientation of the second joint, virtual and physical prototype kinematics is able to emulate real beetle’s behavior and successfully mimics its degrees of freedom. Furthermore, simulations were conducted to analyse workspace of the leg, the performance of the kinematic control and the trajectory planning of the leg for locomotion and object manipulation based on video recordings of the African dung beetle. Experiments were conducted on the developed prototype to test the kinematic algorithm. Robotic design process based on biological experiments and modeling corrected by feedback from biologists offered novel methodology to improve the workspace of insect-like robots. This may increase both the energy efficiency for bio-inspired robots and their potential for complex manipulation tasks, such as demanding search and rescue missions in the wake of natural disasters. |
Honore, Kristoffer A Brain-Computer Interface with Neural Networks for Device Control Masters Thesis University of Southern Denmark, 2017. @mastersthesis{Kristoffer2017, title = {A Brain-Computer Interface with Neural Networks for Device Control}, author = {Honore, Kristoffer}, year = {2017}, date = {2017-06-01}, school = {University of Southern Denmark}, abstract = {Brain-Control Interfaces (BCI) is a research subject, that has been around for a long time, but it seams that a new surge of interest into this area is under way, which is understandable as BCI has main useful applications. This thesis will use the BCI framework, to investigate the hypothesis that Artificial Neural Networks (ANN) with memory capabilities, performs better when classifying EEG signals of either right or left hand movement, then ANNs that does not have memory capabilities. The success will be seen, by how well the Radial Basis Function (RBF) network, performs compared to the Echo State Network (ESN).}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } Brain-Control Interfaces (BCI) is a research subject, that has been around for a long time, but it seams that a new surge of interest into this area is under way, which is understandable as BCI has main useful applications. This thesis will use the BCI framework, to investigate the hypothesis that Artificial Neural Networks (ANN) with memory capabilities, performs better when classifying EEG signals of either right or left hand movement, then ANNs that does not have memory capabilities. The success will be seen, by how well the Radial Basis Function (RBF) network, performs compared to the Echo State Network (ESN). |
Carstensen, Malte Implementation and Verification of an Adaptive Neural Control Oscillator for Robot Control on an FPGA Masters Thesis University of Southern Denmark, 2017. @mastersthesis{Malte2017, title = {Implementation and Verification of an Adaptive Neural Control Oscillator for Robot Control on an FPGA}, author = {Carstensen, Malte }, year = {2017}, date = {2017-06-01}, school = {University of Southern Denmark}, abstract = {This report describes the implementation of an existing and pre-trained neural network for robotic legged locomotion control on a FPGA. The used neural networks follow a modular approach consisting of relative simple elements who are capable to produce complex control behavior when connected together. The system is currently based on a microcontroller motivating this work to present the first step of investigation to convert the modules into hardware based neural network. The designed elements were first tested using the simulation environment from Xilinx Vivado Design Suite. After successful validation of the simulated signals the modular hardware neural networks were converted and tested in the analog domain using an oscilloscope. Thus, the validation and verification part consists of two different methods. The developed neural units were packaged into an IP repository that might be used for further neural FPGA implementations. All experiments are based on the same methods that were already used to validate the functionality of the software methods. All previous results could be successfully repeated by FPGA simulations and the actual chip implementation.}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } This report describes the implementation of an existing and pre-trained neural network for robotic legged locomotion control on a FPGA. The used neural networks follow a modular approach consisting of relative simple elements who are capable to produce complex control behavior when connected together. The system is currently based on a microcontroller motivating this work to present the first step of investigation to convert the modules into hardware based neural network. The designed elements were first tested using the simulation environment from Xilinx Vivado Design Suite. After successful validation of the simulated signals the modular hardware neural networks were converted and tested in the analog domain using an oscilloscope. Thus, the validation and verification part consists of two different methods. The developed neural units were packaged into an IP repository that might be used for further neural FPGA implementations. All experiments are based on the same methods that were already used to validate the functionality of the software methods. All previous results could be successfully repeated by FPGA simulations and the actual chip implementation. |
Schaldemose Reibke, Nikolaj Evolving Robot Control for Object Transportation Masters Thesis University of Southern Denmark, 2017. @mastersthesis{Nikolaj2017, title = {Evolving Robot Control for Object Transportation}, author = {Schaldemose Reibke, Nikolaj }, year = {2017}, date = {2017-06-01}, school = {University of Southern Denmark}, abstract = {This Master Thesis explores the possibilities of phonotaxis controls using behavior based controls. It focuses on the task of seeking towards a sound source while keeping another sound source in a parallel alignment as a means for ground work distributed phonotaxis individual agent formation tasks. The sound is processed in a lizard peripheral auditory system which allows the agent to subtract valuable directional information from the sources. The thesis explores the possibilities in a simulation environment which allows for precise measures of different metrics as the distance between agents and sound sources where the task explored has been the formative movement between 2 agents moving towards a goal using behaviors built from the basic building blocks from the Braitenberg vehicle models fed into the agent being a small robot with 2 wheels for moving around.}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } This Master Thesis explores the possibilities of phonotaxis controls using behavior based controls. It focuses on the task of seeking towards a sound source while keeping another sound source in a parallel alignment as a means for ground work distributed phonotaxis individual agent formation tasks. The sound is processed in a lizard peripheral auditory system which allows the agent to subtract valuable directional information from the sources. The thesis explores the possibilities in a simulation environment which allows for precise measures of different metrics as the distance between agents and sound sources where the task explored has been the formative movement between 2 agents moving towards a goal using behaviors built from the basic building blocks from the Braitenberg vehicle models fed into the agent being a small robot with 2 wheels for moving around. |
Blanco, Aitor Miguel Locomotion control for complex behaviour of bio-inspired multi-legged robotic systems Masters Thesis University of Southern Denmark, 2017. @mastersthesis{Aitor2017, title = {Locomotion control for complex behaviour of bio-inspired multi-legged robotic systems}, author = {Aitor Miguel Blanco}, year = {2017}, date = {2017-05-17}, school = {University of Southern Denmark}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } |
Setaro, Michelangelo Affordance Learning Applied on Bio-inspired Artificial Agents Masters Thesis University of Southern Denmark, 2017. @mastersthesis{Setaro2017, title = {Affordance Learning Applied on Bio-inspired Artificial Agents}, author = {Setaro, Michelangelo}, year = {2017}, date = {2017-01-20}, school = {University of Southern Denmark}, abstract = {The field of robotics is in continuous evolution. handling objects affordances represents a further step towards the creation of intelligent agents. This Master Thesis focuses on implementing a representation of affordance making use of limited set of sensory-motor skills and Neural Networks. The design of a controller takes inspiration from biology and ecological psychology. A simulated Dung-Beetle hexapod robot is employed, it is capable of insect-like locomotion and dung beetle-like object manipulation. This thesis has resulted in the successful implementation of an affordance neural controller, that is eventually combined with the dung-beetle object transportation capabilities.}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } The field of robotics is in continuous evolution. handling objects affordances represents a further step towards the creation of intelligent agents. This Master Thesis focuses on implementing a representation of affordance making use of limited set of sensory-motor skills and Neural Networks. The design of a controller takes inspiration from biology and ecological psychology. A simulated Dung-Beetle hexapod robot is employed, it is capable of insect-like locomotion and dung beetle-like object manipulation. This thesis has resulted in the successful implementation of an affordance neural controller, that is eventually combined with the dung-beetle object transportation capabilities. |
Mehanovic, Almir Evolving Robot Morphology with Genetic Algorithms Miscellaneous University of Southern Denmark, 2017. @misc{Mehanovic2017, title = {Evolving Robot Morphology with Genetic Algorithms}, author = {Mehanovic, Almir}, year = {2017}, date = {2017-01-20}, institution = {University of Southern Denmark}, howpublished = {University of Southern Denmark}, keywords = {}, pubstate = {published}, tppubtype = {misc} } |
2016 |
Strøm-Hansen Theis; Thor, Mathias Embodied Control of a Dung Beetle Inspired Hexapod Miscellaneous Bachelor thesis, University of Southern Denmark, 2016. @misc{Hansen2016, title = {Embodied Control of a Dung Beetle Inspired Hexapod}, author = {Strøm-Hansen, Theis; Thor, Mathias}, year = {2016}, date = {2016-06-15}, abstract = {This study presents a different approach to modelling biologically-inspired hexapods. Many projects concerning biologically-inspired hexapods are greatly influenced by approximations, which is commonly shown in oversimplified leg kinematics. The focus of these projects is usually on the locomotive aspect of the model and thus many of these hexapods are unable to perform manipulative related tasks. This study will try to accurately model the dung beetle and its complex kinematics, based on actual measurements of the dung beetle species Geotrupes stercorarius. The intention is to exploit its locomotive and manipulative behavior by doing as few approximations as possible. A position based version of the walknet controller is implemented on the dung beetle model, in order to test the performance and stability of the its locomotive behavior. The manipulative behavior of the model is however tested through static positions that the dung beetle is commonly seen in, where the actual controller implementation of this behavior is left for future study.}, howpublished = {Bachelor thesis, University of Southern Denmark}, keywords = {}, pubstate = {published}, tppubtype = {misc} } This study presents a different approach to modelling biologically-inspired hexapods. Many projects concerning biologically-inspired hexapods are greatly influenced by approximations, which is commonly shown in oversimplified leg kinematics. The focus of these projects is usually on the locomotive aspect of the model and thus many of these hexapods are unable to perform manipulative related tasks. This study will try to accurately model the dung beetle and its complex kinematics, based on actual measurements of the dung beetle species Geotrupes stercorarius. The intention is to exploit its locomotive and manipulative behavior by doing as few approximations as possible. A position based version of the walknet controller is implemented on the dung beetle model, in order to test the performance and stability of the its locomotive behavior. The manipulative behavior of the model is however tested through static positions that the dung beetle is commonly seen in, where the actual controller implementation of this behavior is left for future study. |
Dyre Skjold; Bagge Jensen, Martin Receptive Fields-based Approach for Generation of Movement Patterns Miscellaneous Bachelor thesis, University of Southern Denmark, 2016. @misc{Dyre2016, title = {Receptive Fields-based Approach for Generation of Movement Patterns}, author = {Dyre, Skjold; Bagge Jensen, Martin}, year = {2016}, date = {2016-06-15}, abstract = {This report contains information regarding movement pattern generation using a Radial Basis Function Network. The report will focus on development of the network and optimisation of the networks run time. Optimisation is done in the CPU as well as CUDA is used to parallelise computations on the GPU. The learning used in the project will at this point be supervised as it is not the main objective.}, howpublished = {Bachelor thesis, University of Southern Denmark}, keywords = {}, pubstate = {published}, tppubtype = {misc} } This report contains information regarding movement pattern generation using a Radial Basis Function Network. The report will focus on development of the network and optimisation of the networks run time. Optimisation is done in the CPU as well as CUDA is used to parallelise computations on the GPU. The learning used in the project will at this point be supervised as it is not the main objective. |
Weickgenannt, Dominik Steven Action-Sequence Learning in Mobile Robotics Masters Thesis University of Southern Denmark, 2016. @mastersthesis{Dominik2016, title = {Action-Sequence Learning in Mobile Robotics}, author = {Weickgenannt, Dominik Steven}, year = {2016}, date = {2016-06-15}, school = {University of Southern Denmark}, abstract = {This master thesis explores a number of di fferent deep and shallow Arti ficial Neural Network architectures to solve the problem of Action-Sequence-Learning on a mobile robot platform. The architectures are divided into two archetypes: direct Sensor-to-Behaviour mapping and a Trigger Network combined with a Neural Memory Module. The tested architectures include Feed-Forward, Recurrent, Echo-State and Long-Short-Term-Memory networks as well as Dynamic Neural Fields. The performances are compared against each other with and without noise. Findings indicate that a number of structures are capable of learning the predefi ned sequence in a robot simulation environment.}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } This master thesis explores a number of di fferent deep and shallow Arti ficial Neural Network architectures to solve the problem of Action-Sequence-Learning on a mobile robot platform. The architectures are divided into two archetypes: direct Sensor-to-Behaviour mapping and a Trigger Network combined with a Neural Memory Module. The tested architectures include Feed-Forward, Recurrent, Echo-State and Long-Short-Term-Memory networks as well as Dynamic Neural Fields. The performances are compared against each other with and without noise. Findings indicate that a number of structures are capable of learning the predefi ned sequence in a robot simulation environment. |
Moro García, Carlos Multiple Time Scales of Learning for Adaptive Behavior of Mobile Robots Masters Thesis University of Southern Denmark, 2016. @mastersthesis{carlos2016, title = {Multiple Time Scales of Learning for Adaptive Behavior of Mobile Robots}, author = {Moro García, Carlos}, year = {2016}, date = {2016-06-15}, school = {University of Southern Denmark}, abstract = {Different time scales of learning can be found within the artificial intelligence algorithms. These time scales have been traditionally used separately except for some recent papers that started combining two of them. In fact, each of them has advantages and disadvantages that could be easily compensated by following the approach of using them at the same time as it is seen in nature, where different species evolve while organisms of each generation learn new behaviours. Therefore, this thesis tries to research the viability of using a short time scale of learning such as input correlation learning (ICO) with a long time scale of learning in the shape of a genetic algorithm (GA). These two systems together were tested with a mobile robot in a simulated environment relatively complex to navigate. The idea behind the simulation was to see how the evolutionary algorithms are able to obtain a specific morphology of the robot that is adapted to the environment while the correlation-based learning allows the system to navigate it. Experiments were done combining the algorithms in a sequential way, and a proof that this lead to an improvement was gotten. Moreover, there is evidence in the results obtained which suggests that a parallel combination of the algorithms would be able to perform even better.}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } Different time scales of learning can be found within the artificial intelligence algorithms. These time scales have been traditionally used separately except for some recent papers that started combining two of them. In fact, each of them has advantages and disadvantages that could be easily compensated by following the approach of using them at the same time as it is seen in nature, where different species evolve while organisms of each generation learn new behaviours. Therefore, this thesis tries to research the viability of using a short time scale of learning such as input correlation learning (ICO) with a long time scale of learning in the shape of a genetic algorithm (GA). These two systems together were tested with a mobile robot in a simulated environment relatively complex to navigate. The idea behind the simulation was to see how the evolutionary algorithms are able to obtain a specific morphology of the robot that is adapted to the environment while the correlation-based learning allows the system to navigate it. Experiments were done combining the algorithms in a sequential way, and a proof that this lead to an improvement was gotten. Moreover, there is evidence in the results obtained which suggests that a parallel combination of the algorithms would be able to perform even better. |
van Dinten, Imara EEG Pattern Recognition Masters Thesis University of Southern Denmark, 2016. @mastersthesis{Dinten2016, title = {EEG Pattern Recognition}, author = {Dinten, Imara van}, year = {2016}, date = {2016-06-15}, school = {University of Southern Denmark}, abstract = {During this master thesis, research regarding recurring patterns in EEG was done. Different methods on to how to detect these recurring patterns were compared. The recurring pattern chosen to detect in the EEG signal were 2D images that were shown to a test subject. It was then concluded that it is possible to see a change as to where in the moment the image was shown. After this, research was conducted to see which method could best automatically detect the diff erence in the EEG signal. Even make a distinction between the di fferent images shown. The final method chosen for this application was an Arti ficial Neural Network.}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } During this master thesis, research regarding recurring patterns in EEG was done. Different methods on to how to detect these recurring patterns were compared. The recurring pattern chosen to detect in the EEG signal were 2D images that were shown to a test subject. It was then concluded that it is possible to see a change as to where in the moment the image was shown. After this, research was conducted to see which method could best automatically detect the diff erence in the EEG signal. Even make a distinction between the di fferent images shown. The final method chosen for this application was an Arti ficial Neural Network. |
Tomás, Timon Artificial Neural Control of a Prosthetic Masters Thesis University of Southern Denmark, 2016. @mastersthesis{Timon2016, title = {Artificial Neural Control of a Prosthetic}, author = {Tomás, Timon}, year = {2016}, date = {2016-06-15}, school = {University of Southern Denmark}, abstract = {Artificial limb prosthetics have come a long way from being crude, unsophisticated devices they once were. In the past couple of years, advanced actuation systems have been developed which now allow even relatively precise movements to be performed under laboratory conditions. Although these systems would require continuous and stable control signals to operate, in real-life scenarios these conditions are rarely met, which poses a significant engineering problem needing to be solved. This is especially true in the case of myoelectric prosthetic hands, since they possess many degrees of freedom, and the body produced signals — mainly do to their biological nature — are inherently unstable. One possible solution to this problem is to translate the incoming electromyographic (EMG) signals to stable control signals with the help of an intermediating artificial neural network (ANN), that has been trained to overcome the aforementioned errors present in the system. In this thesis work, I will investigate the potential usefulness of the application of ANNbased filtering and conditioning techniques for myoelectric signals in order to provide cleaner inputs to systems used to control assistive devices in the medical domain. Moreover, the thesis also aims to verify that ANN with memory capabilities are better suited for this given task than those without one. First a method will be developed to synthesize EMG signals to help in their analysis, then the capabilities of a radial basis function (RBF) network will be compared to that of an echo state network (ESN) using the synthetic data. In the next phase, real EMG signals will be measured and errors will be introduced to them. Finally, the contaminated signals will be cleaned, and the process tested through the control of a prosthetic hand. It will be shown, that neural networks are suitable to mitigate — and in certain cases eliminate — the artifacts present in these signals, and that the ESN — relying on its reservoir of interconnected neurons providing it with memory — can outperform the — memoryless — RBF network in multiple scenarios.}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } Artificial limb prosthetics have come a long way from being crude, unsophisticated devices they once were. In the past couple of years, advanced actuation systems have been developed which now allow even relatively precise movements to be performed under laboratory conditions. Although these systems would require continuous and stable control signals to operate, in real-life scenarios these conditions are rarely met, which poses a significant engineering problem needing to be solved. This is especially true in the case of myoelectric prosthetic hands, since they possess many degrees of freedom, and the body produced signals — mainly do to their biological nature — are inherently unstable. One possible solution to this problem is to translate the incoming electromyographic (EMG) signals to stable control signals with the help of an intermediating artificial neural network (ANN), that has been trained to overcome the aforementioned errors present in the system. In this thesis work, I will investigate the potential usefulness of the application of ANNbased filtering and conditioning techniques for myoelectric signals in order to provide cleaner inputs to systems used to control assistive devices in the medical domain. Moreover, the thesis also aims to verify that ANN with memory capabilities are better suited for this given task than those without one. First a method will be developed to synthesize EMG signals to help in their analysis, then the capabilities of a radial basis function (RBF) network will be compared to that of an echo state network (ESN) using the synthetic data. In the next phase, real EMG signals will be measured and errors will be introduced to them. Finally, the contaminated signals will be cleaned, and the process tested through the control of a prosthetic hand. It will be shown, that neural networks are suitable to mitigate — and in certain cases eliminate — the artifacts present in these signals, and that the ESN — relying on its reservoir of interconnected neurons providing it with memory — can outperform the — memoryless — RBF network in multiple scenarios. |
Bulin, Martin Classification of Terrain based on Proprioception and Tactile Sensing for Multi-legged Walking Robot Masters Thesis University of Southern Denmark, 2016. @mastersthesis{Martin2016, title = {Classification of Terrain based on Proprioception and Tactile Sensing for Multi-legged Walking Robot}, author = {Bulin, Martin}, year = {2016}, date = {2016-06-15}, school = {University of Southern Denmark}, abstract = {Proprioception and tactile sensing in insect-like legged robots is a fast, illumination insensitive and biologically inspired way of ground perception. In this thesis, 14 virtually generated terrains are classified based on the mentioned sensor types for a simulated version of hexapod robot AMOS II. A feedforward neural network framework equipped with a novel network pruning algorithm has been developed for classification. We observe over 92% classification accuracy on deterministic terrain data and 72% on manually noised data. The pruning algorithm removes unimportant synapses (generally more than 90%) from a fully-connected network, while the classification accuracy does not drop significantly. The number of input neurons is reduced by 65%, resulting in the minimal network structure for the classification problem. A theory of using minimal structures for feature selection is proposed. The thesis outcome consists of a minimal neural network capable of terrain classification based on selected features of proprioceptive and tactile sensory signals.}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } Proprioception and tactile sensing in insect-like legged robots is a fast, illumination insensitive and biologically inspired way of ground perception. In this thesis, 14 virtually generated terrains are classified based on the mentioned sensor types for a simulated version of hexapod robot AMOS II. A feedforward neural network framework equipped with a novel network pruning algorithm has been developed for classification. We observe over 92% classification accuracy on deterministic terrain data and 72% on manually noised data. The pruning algorithm removes unimportant synapses (generally more than 90%) from a fully-connected network, while the classification accuracy does not drop significantly. The number of input neurons is reduced by 65%, resulting in the minimal network structure for the classification problem. A theory of using minimal structures for feature selection is proposed. The thesis outcome consists of a minimal neural network capable of terrain classification based on selected features of proprioceptive and tactile sensory signals. |
Lund Sørensen, Chris Tryk Object Manipulation in a Hexapod Robot Masters Thesis University of Southern Denmark, 2016. @mastersthesis{Chris2016b, title = {Object Manipulation in a Hexapod Robot}, author = {Lund Sørensen, Chris Tryk}, year = {2016}, date = {2016-06-13}, school = {University of Southern Denmark}, abstract = {Insects, like dung beetles, can perform versatile motor behaviors including walking, climbing an object (i.e., dung ball), as well as manipulating and transporting it. To achieve such complex behaviors for arti ficial legged systems, this work presents modular neural controllers of a bio- inspired hexapod robot. The controllers utilize discrete-time neurodynamics and consists of seven modules based on three generic neural networks. One is a neural oscillator network serving as a central pattern generator (CPG) which generates basic rhythmic patterns. The other two networks are so-called velocity regulating and phase switching networks. They are used for regulating the rhythmic patterns and changing their phase. As a result, the modular neural controllers enable the hexapod robot to walk and climb a cylindrical object with a diameter of 18 cm (i.e., 2.8 times the robot's body height) and a spherical object with a diameter of 30 cm (i.e., 4.6 times the robot's body height). Additionally, they can also generate di fferent hind leg movements for di fferent object manipulation modes, like soft push, boxing-like motion and hard push. The manipulation behaviors makes it possible for the robot to transport the objects, and for the spherical object, across rough terrain. Combining these pushing modes, the robot can quickly transport the cylindrical object across an obstacle with a height up to 14 cm (i.e., 2.2 times the robot's body height) and the spherical object up to 16 cm (i.e., 2.2 times the robot's body height). The controllers was developed and evaluated using a physical simulation environment.}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } Insects, like dung beetles, can perform versatile motor behaviors including walking, climbing an object (i.e., dung ball), as well as manipulating and transporting it. To achieve such complex behaviors for arti ficial legged systems, this work presents modular neural controllers of a bio- inspired hexapod robot. The controllers utilize discrete-time neurodynamics and consists of seven modules based on three generic neural networks. One is a neural oscillator network serving as a central pattern generator (CPG) which generates basic rhythmic patterns. The other two networks are so-called velocity regulating and phase switching networks. They are used for regulating the rhythmic patterns and changing their phase. As a result, the modular neural controllers enable the hexapod robot to walk and climb a cylindrical object with a diameter of 18 cm (i.e., 2.8 times the robot's body height) and a spherical object with a diameter of 30 cm (i.e., 4.6 times the robot's body height). Additionally, they can also generate di fferent hind leg movements for di fferent object manipulation modes, like soft push, boxing-like motion and hard push. The manipulation behaviors makes it possible for the robot to transport the objects, and for the spherical object, across rough terrain. Combining these pushing modes, the robot can quickly transport the cylindrical object across an obstacle with a height up to 14 cm (i.e., 2.2 times the robot's body height) and the spherical object up to 16 cm (i.e., 2.2 times the robot's body height). The controllers was developed and evaluated using a physical simulation environment. |
Torroba Balmori Ignacio; Rodriguez Marin, Jorge Design, Simulation and Development of a Bipedal Locomotion Platform for Human-like Gaits Studies Masters Thesis University of Southern Denmark, 2016. @mastersthesis{Torroba2016, title = {Design, Simulation and Development of a Bipedal Locomotion Platform for Human-like Gaits Studies}, author = {Torroba Balmori, Ignacio; Rodriguez Marin, Jorge}, year = {2016}, date = {2016-06-13}, school = {University of Southern Denmark}, abstract = {The modern field of robotics, in its attempt to understand and mimic the underlying principles of legged locomotion, has in the past decades given birth to some of the most advanced existing bipedal creatures, finding inspiration in nature and the human being. This thesis contributes the biped robot RuBi and its development environment to the advancement of this area of the engineering. RuBi is a human-inspired, compliance-reconfigurable and low-cost bipedal platform for research in human-like walking and running gaits control. The framework presented consists of the lower body frame of the robot and ROS enabled control architecture, a simulation model and environment and a test bench for 2D motion. The controller interface with the robot and the simulation environment in Gazebo has been successfully implemented tested. A set of experiments for the robot prototype is presented here although they have not been conducted due to time constraints and eventualities. Furthermore, everything has been set so that the utilization of the framework can be easily learned and mastered by new users.}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } The modern field of robotics, in its attempt to understand and mimic the underlying principles of legged locomotion, has in the past decades given birth to some of the most advanced existing bipedal creatures, finding inspiration in nature and the human being. This thesis contributes the biped robot RuBi and its development environment to the advancement of this area of the engineering. RuBi is a human-inspired, compliance-reconfigurable and low-cost bipedal platform for research in human-like walking and running gaits control. The framework presented consists of the lower body frame of the robot and ROS enabled control architecture, a simulation model and environment and a test bench for 2D motion. The controller interface with the robot and the simulation environment in Gazebo has been successfully implemented tested. A set of experiments for the robot prototype is presented here although they have not been conducted due to time constraints and eventualities. Furthermore, everything has been set so that the utilization of the framework can be easily learned and mastered by new users. |
Goldschmidt, Dennis ; Manoonpong, Poramate ; Dasgupta, Sakyasingha Neural mechanisms for reward-modulated vector learning and navigation: from social insects to embodied agents Online 2016. @online{goldschmidt2016neural, title = {Neural mechanisms for reward-modulated vector learning and navigation: from social insects to embodied agents}, author = {Goldschmidt, Dennis and Manoonpong, Poramate and Dasgupta, Sakyasingha}, year = {2016}, date = {2016-01-01}, journal = {bioRxiv}, pages = {045559}, publisher = {Cold Spring Harbor Labs Journals}, keywords = {}, pubstate = {published}, tppubtype = {online} } |
Fischer, J"orn ; Manoonpong, Poramate ; Lackner, S Reconstructing Neural Parameters and Synapses of arbitrary interconnected Neurons from their Simulated Spiking Activity Online 2016. @online{fischer2016reconstructing, title = {Reconstructing Neural Parameters and Synapses of arbitrary interconnected Neurons from their Simulated Spiking Activity}, author = {Fischer, J"orn and Manoonpong, Poramate and Lackner, S}, year = {2016}, date = {2016-01-01}, journal = {arXiv preprint arXiv:1608.06132}, keywords = {}, pubstate = {published}, tppubtype = {online} } |
2015 |
Børresen, Lasse Simmelsgaard Separation of Multiple Sound Sources Using Directional Stereo Masters Thesis University of Southern Denmark, 2015. @mastersthesis{Lasse_B rresen, title = {Separation of Multiple Sound Sources Using Directional Stereo}, author = {Børresen, Lasse Simmelsgaard}, year = {2015}, date = {2015-12-03}, address = {CBR Embodied AI and Neurorobotics Lab, The Mærsk Mc-Kinney Møller Institute}, school = {University of Southern Denmark}, abstract = {Humans primarily interpret sound as combinations of frequencies, this is the way useful information in sound is encoded, not in waveform. By converting sound to spectrograms, the change of frequency spectrum over timer can be represented as an image, e effectively representing sound information over time. Using machine learning, otherwise unobtainable knowledge can be extracted from complex data. ML algorithms create models directly based on the data unlike expert systems. Performance of machine learning algorithms and Neural Networks applied to sound and sound direction are explored in this thesis. A data set of spectrograms with direction labels was recorded using two directional microphones placed at the centre of a semicircle of 37 speakers (5 deg interval). Both single sinusoids and complex speech is in the data set. Two experiments have been executed: (1) Estimating direction of a single source sound using a neural network. (2) Filtering a multi source sound by target direction. For both purposes, the input is the right and left spectrograms of the sound.For estimating source direction, a mean error of 2.01 deg was achieved. However, precision is direction dependent, with the best resolution around 0.0 deg and worst at the extremities. This is an attribute of the specifi c microphone setup. For filtering sounds by direction a convolutional neural network was trained on 10000 samples of mixed source sounds. A mean MSE of 0:093 on fi ltering standardized spectrograms was achieved, resulting in remarkable filtering of off -axis frequencies.}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } Humans primarily interpret sound as combinations of frequencies, this is the way useful information in sound is encoded, not in waveform. By converting sound to spectrograms, the change of frequency spectrum over timer can be represented as an image, e effectively representing sound information over time. Using machine learning, otherwise unobtainable knowledge can be extracted from complex data. ML algorithms create models directly based on the data unlike expert systems. Performance of machine learning algorithms and Neural Networks applied to sound and sound direction are explored in this thesis. A data set of spectrograms with direction labels was recorded using two directional microphones placed at the centre of a semicircle of 37 speakers (5 deg interval). Both single sinusoids and complex speech is in the data set. Two experiments have been executed: (1) Estimating direction of a single source sound using a neural network. (2) Filtering a multi source sound by target direction. For both purposes, the input is the right and left spectrograms of the sound.For estimating source direction, a mean error of 2.01 deg was achieved. However, precision is direction dependent, with the best resolution around 0.0 deg and worst at the extremities. This is an attribute of the specifi c microphone setup. For filtering sounds by direction a convolutional neural network was trained on 10000 samples of mixed source sounds. A mean MSE of 0:093 on fi ltering standardized spectrograms was achieved, resulting in remarkable filtering of off -axis frequencies. |
Jankovics, Vince Artificial Neural Network based Adaptive Complaint Control for Robotic Arms Miscellaneous Bachelor thesis, University of Southern Denmark, 2015. @misc{Vince2015, title = {Artificial Neural Network based Adaptive Complaint Control for Robotic Arms}, author = {Jankovics, Vince}, year = {2015}, date = {2015-06-10}, howpublished = {Bachelor thesis, University of Southern Denmark}, keywords = {}, pubstate = {published}, tppubtype = {misc} } |
Horvatic, Deniel Efficient Reconstruction of Neural Interconnections in Simulated Spiking Neurons Masters Thesis University of Southern Denmark, 2015. @mastersthesis{Deniel2015, title = {Efficient Reconstruction of Neural Interconnections in Simulated Spiking Neurons}, author = {Horvatic, Deniel}, year = {2015}, date = {2015-06-10}, school = {University of Southern Denmark}, abstract = {Insects, like dung beetles, can perform versatile motor behaviors including walking, climbing an object (i.e., dung ball), as well as manipulating and transporting it. To achieve such complex behaviors for arti cial legged systems, this work presents modular neural controllers of a bio- inspired hexapod robot. The controllers utilize discrete-time neurodynamics and consists of seven modules based on three generic neural networks. One is a neural oscillator network serving as a central pattern generator (CPG) which generates basic rhythmic patterns. The other two networks are so-called velocity regulating and phase switching networks. They are used for regulating the rhythmic patterns and changing their phase. As a result, the modular neural controllers enable the hexapod robot to walk and climb a cylindrical object with a diameter of 18 cm (i.e., 2.8 times the robot's body height) and a spherical object with a diameter of 30 cm (i.e., 4.6 times the robot's body height). Additionally, they can also generate di erent hind leg movements for di erent object manipulation modes, like soft push, boxing-like motion and hard push. The manipulation behaviors makes it possible for the robot to transport the objects, and for the spherical object, across rough terrain. Combining these pushing modes, the robot can quickly transport the cylindrical object across an obstacle with a height up to 14 cm (i.e., 2.2 times the robot's body height) and the spherical object up to 16 cm (i.e., 2.2 times the robot's body height). The controllers was developed and evaluated using a physical simulation environment.}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } Insects, like dung beetles, can perform versatile motor behaviors including walking, climbing an object (i.e., dung ball), as well as manipulating and transporting it. To achieve such complex behaviors for arti cial legged systems, this work presents modular neural controllers of a bio- inspired hexapod robot. The controllers utilize discrete-time neurodynamics and consists of seven modules based on three generic neural networks. One is a neural oscillator network serving as a central pattern generator (CPG) which generates basic rhythmic patterns. The other two networks are so-called velocity regulating and phase switching networks. They are used for regulating the rhythmic patterns and changing their phase. As a result, the modular neural controllers enable the hexapod robot to walk and climb a cylindrical object with a diameter of 18 cm (i.e., 2.8 times the robot's body height) and a spherical object with a diameter of 30 cm (i.e., 4.6 times the robot's body height). Additionally, they can also generate di erent hind leg movements for di erent object manipulation modes, like soft push, boxing-like motion and hard push. The manipulation behaviors makes it possible for the robot to transport the objects, and for the spherical object, across rough terrain. Combining these pushing modes, the robot can quickly transport the cylindrical object across an obstacle with a height up to 14 cm (i.e., 2.2 times the robot's body height) and the spherical object up to 16 cm (i.e., 2.2 times the robot's body height). The controllers was developed and evaluated using a physical simulation environment. |
Krauch, Hans-Joachim Exploration and Object Retrieval with Robot Swarms Masters Thesis University of Southern Denmark, 2015. @mastersthesis{SwarmKrauch, title = {Exploration and Object Retrieval with Robot Swarms}, author = {Hans-Joachim Krauch}, year = {2015}, date = {2015-06-01}, address = {CBR Embodied AI and Neurorobotics Lab, The Mærsk Mc-Kinney Møller Institute}, school = {University of Southern Denmark}, abstract = {Swarm robotics is a new approach to the coordination of large robot populations. This thesis focuses on coordination strategies for exploration and foraging tasks. Di erent agent strategies are implemented and tested in a simulation environment. Key parts of the implemented design include the modular agent design and the probabilistic way in which agents decide where to go next. Each agent strategy is simulated on exploration and foraging tasks and rated based on its performance, scalability, robustness against agent failure and computational complexity. The experiments show that the most sophisticated strategies have the best performance for small swarms. For larger swarms however, the strategies that perform the best, are the most simple ones.}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } Swarm robotics is a new approach to the coordination of large robot populations. This thesis focuses on coordination strategies for exploration and foraging tasks. Di erent agent strategies are implemented and tested in a simulation environment. Key parts of the implemented design include the modular agent design and the probabilistic way in which agents decide where to go next. Each agent strategy is simulated on exploration and foraging tasks and rated based on its performance, scalability, robustness against agent failure and computational complexity. The experiments show that the most sophisticated strategies have the best performance for small swarms. For larger swarms however, the strategies that perform the best, are the most simple ones. |
Canio, Giuliano Di; Stolc, Patrick Adaptive Locomotion Control of Embodied Legged Systems Masters Thesis University of Southern Denmark, 2015. @mastersthesis{Patrick_Giuliano, title = {Adaptive Locomotion Control of Embodied Legged Systems}, author = {Giuliano Di Canio and Patrick Stolc}, year = {2015}, date = {2015-06-01}, address = {CBR Embodied AI and Neurorobotics Lab, The Mærsk Mc-Kinney Møller Institute}, school = {University of Southern Denmark}, abstract = {The eld of robotics is evolving very fast. In the eld of biped robotics research is focused on making robots walk and adapt to the environment. This master thesis focuses on locomotion and balance control of the DACBot, a small planar robot. Both locomotion and balance control of the DACBot is inspired by biology. By using biology as an inspiration for the development of the controllers, this thesis has shown that these principles can be used for controlling a biped robot. This thesis has resulted in the successful implementation of an adaptive locomotion controller for the DACBot and a two stage balance controller.}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } The eld of robotics is evolving very fast. In the eld of biped robotics research is focused on making robots walk and adapt to the environment. This master thesis focuses on locomotion and balance control of the DACBot, a small planar robot. Both locomotion and balance control of the DACBot is inspired by biology. By using biology as an inspiration for the development of the controllers, this thesis has shown that these principles can be used for controlling a biped robot. This thesis has resulted in the successful implementation of an adaptive locomotion controller for the DACBot and a two stage balance controller. |
Stoyanov, Stoyan Brain - Computer Interface for Robot Control Masters Thesis University of Southern Denmark, 2015. @mastersthesis{Stoyan, title = {Brain - Computer Interface for Robot Control}, author = {Stoyanov, Stoyan}, year = {2015}, date = {2015-06-01}, address = {CBR Embodied AI and Neurorobotics Lab, The Mærsk Mc-Kinney Møller Institute}, school = {University of Southern Denmark}, abstract = {This thesis presents a framework for brain - computer interface for robot control. The list of functionalities of the framework includes data acquisition, data processing, feature extraction and classi cation. The list is further extended with options for robot control and simulation due to the framework integration with the lpzrobots and gorobots software. The framework also can save, visualise and stream the acquired data because of its integration with ROS. As a proof of concept several experiments were performed with a single channel EEG device and a real hexapod robot. The experiments showed that the framework is able to demonstrate real robot control based on real EEG recordings as well as recording of neuron synchronisation and de synchronisation over the motor cortex area.}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } This thesis presents a framework for brain - computer interface for robot control. The list of functionalities of the framework includes data acquisition, data processing, feature extraction and classi cation. The list is further extended with options for robot control and simulation due to the framework integration with the lpzrobots and gorobots software. The framework also can save, visualise and stream the acquired data because of its integration with ROS. As a proof of concept several experiments were performed with a single channel EEG device and a real hexapod robot. The experiments showed that the framework is able to demonstrate real robot control based on real EEG recordings as well as recording of neuron synchronisation and de synchronisation over the motor cortex area. |
Andersen, Ditlev; Lindvig, Anders Prier Visual Segmentation of Potatoes in Cluttered Environments Masters Thesis University of Southern Denmark, 2015. @mastersthesis{Ditlev, title = {Visual Segmentation of Potatoes in Cluttered Environments}, author = {Ditlev Andersen and Anders Prier Lindvig}, year = {2015}, date = {2015-06-01}, address = {The Mærsk Mc-Kinney Møller Institute}, school = {University of Southern Denmark}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } |
2014 |
Stenvang Thor Stærk; Nielsen, Mathias Control and Regulation of the Starkick Football Table Miscellaneous Bachelor thesis, University of Southern Denmark, 2014. @misc{Stenvang2014, title = {Control and Regulation of the Starkick Football Table}, author = {Stenvang, Thor Stærk; Nielsen, Mathias}, year = {2014}, date = {2014-06-10}, howpublished = {Bachelor thesis, University of Southern Denmark}, keywords = {}, pubstate = {published}, tppubtype = {misc} } |
Lund, Jon Control and Motion Planning of a Five Axis Robot Arm Miscellaneous Bachelor thesis, University of Southern Denmark, 2014. @misc{Lund2014b, title = {Control and Motion Planning of a Five Axis Robot Arm}, author = {Lund, Jon}, year = {2014}, date = {2014-06-10}, howpublished = {Bachelor thesis, University of Southern Denmark}, keywords = {}, pubstate = {published}, tppubtype = {misc} } |
Theses / Others
2020 |
Closed-loop dynamic computations for adaptive behavior (articles based on SAB2018 conference) Miscellaneous 2020. |
Closed-loop dynamic computations for adaptive behavior (articles based on SAB2018 conference) Miscellaneous 2020. |
Closed-loop dynamic computations for adaptive behavior (articles based on SAB2018 conference) Miscellaneous 2020. |
2018 |
The Motor Control Development of a Compliant Robotic Arm Miscellaneous Bachelor thesis, University of Southern Denmark, 2018. |
Dung Beetle Inspired Robot Miscellaneous University of Southern Denmark, 2018. |
2017 |
EEG Signal Processing and Classification for Advanced Human-Machine Interfaces Masters Thesis University of Ljubljana, 2017, (Co-supervision between SDU and University of Ljubljana). |
Modeling and Control of Dung Beetle-Like Robots Masters Thesis University of Southern Denmark, 2017. |
A Brain-Computer Interface with Neural Networks for Device Control Masters Thesis University of Southern Denmark, 2017. |
Implementation and Verification of an Adaptive Neural Control Oscillator for Robot Control on an FPGA Masters Thesis University of Southern Denmark, 2017. |
Evolving Robot Control for Object Transportation Masters Thesis University of Southern Denmark, 2017. |
Locomotion control for complex behaviour of bio-inspired multi-legged robotic systems Masters Thesis University of Southern Denmark, 2017. |
Affordance Learning Applied on Bio-inspired Artificial Agents Masters Thesis University of Southern Denmark, 2017. |
Evolving Robot Morphology with Genetic Algorithms Miscellaneous University of Southern Denmark, 2017. |
2016 |
Embodied Control of a Dung Beetle Inspired Hexapod Miscellaneous Bachelor thesis, University of Southern Denmark, 2016. |
Receptive Fields-based Approach for Generation of Movement Patterns Miscellaneous Bachelor thesis, University of Southern Denmark, 2016. |
Action-Sequence Learning in Mobile Robotics Masters Thesis University of Southern Denmark, 2016. |
Multiple Time Scales of Learning for Adaptive Behavior of Mobile Robots Masters Thesis University of Southern Denmark, 2016. |
EEG Pattern Recognition Masters Thesis University of Southern Denmark, 2016. |
Artificial Neural Control of a Prosthetic Masters Thesis University of Southern Denmark, 2016. |
Classification of Terrain based on Proprioception and Tactile Sensing for Multi-legged Walking Robot Masters Thesis University of Southern Denmark, 2016. |
Object Manipulation in a Hexapod Robot Masters Thesis University of Southern Denmark, 2016. |
Design, Simulation and Development of a Bipedal Locomotion Platform for Human-like Gaits Studies Masters Thesis University of Southern Denmark, 2016. |
Neural mechanisms for reward-modulated vector learning and navigation: from social insects to embodied agents Online 2016. |
Reconstructing Neural Parameters and Synapses of arbitrary interconnected Neurons from their Simulated Spiking Activity Online 2016. |
2015 |
Separation of Multiple Sound Sources Using Directional Stereo Masters Thesis University of Southern Denmark, 2015. |
Artificial Neural Network based Adaptive Complaint Control for Robotic Arms Miscellaneous Bachelor thesis, University of Southern Denmark, 2015. |
Efficient Reconstruction of Neural Interconnections in Simulated Spiking Neurons Masters Thesis University of Southern Denmark, 2015. |
Exploration and Object Retrieval with Robot Swarms Masters Thesis University of Southern Denmark, 2015. |
Adaptive Locomotion Control of Embodied Legged Systems Masters Thesis University of Southern Denmark, 2015. |
Brain - Computer Interface for Robot Control Masters Thesis University of Southern Denmark, 2015. |
Visual Segmentation of Potatoes in Cluttered Environments Masters Thesis University of Southern Denmark, 2015. |
2014 |
Control and Regulation of the Starkick Football Table Miscellaneous Bachelor thesis, University of Southern Denmark, 2014. |
Control and Motion Planning of a Five Axis Robot Arm Miscellaneous Bachelor thesis, University of Southern Denmark, 2014. |