+publisher:"Delft University of Technology" +contributor:("Jonker, Pieter")

Delft University of Technology

1. Guljelmović, Nikol (author). Task Parameter Inference in Human-Robot Interaction.

Degree: 2017, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:059dc816-2edb-4d44-a841-cc4d58ecf802

►

Task-parameterized movement representation, as an approach for the generalization of demonstrations, is used to represent data from multiple local perspectives within the global reference frame,… (more)

Subjects/Keywords: Task-parameterized movement representation; Task parameter inference; human-robot interaction; Procrustes analysis

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Guljelmović, N. (. (2017). Task Parameter Inference in Human-Robot Interaction. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:059dc816-2edb-4d44-a841-cc4d58ecf802

Chicago Manual of Style (16^th Edition):

Guljelmović, Nikol (author). “Task Parameter Inference in Human-Robot Interaction.” 2017. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:059dc816-2edb-4d44-a841-cc4d58ecf802.

MLA Handbook (7^th Edition):

Guljelmović, Nikol (author). “Task Parameter Inference in Human-Robot Interaction.” 2017. Web. 26 Jan 2021.

Vancouver:

Guljelmović N(. Task Parameter Inference in Human-Robot Interaction. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:059dc816-2edb-4d44-a841-cc4d58ecf802.

Council of Science Editors:

Guljelmović N(. Task Parameter Inference in Human-Robot Interaction. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:059dc816-2edb-4d44-a841-cc4d58ecf802

Delft University of Technology

2. Bollini, Valentina (author). Automatic data collection for facial expression recognition.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:1335643e-6202-4a8c-bd51-9f7657a3611e

► Facial expression recognition today is a widely-researched topic with some pertinent applications in human-computer interaction (HCI), surveillance, etc. The diverse range of expressions makes data… (more)

Subjects/Keywords: data collection; facial expression

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Bollini, V. (. (2018). Automatic data collection for facial expression recognition. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:1335643e-6202-4a8c-bd51-9f7657a3611e

Chicago Manual of Style (16^th Edition):

Bollini, Valentina (author). “Automatic data collection for facial expression recognition.” 2018. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:1335643e-6202-4a8c-bd51-9f7657a3611e.

MLA Handbook (7^th Edition):

Bollini, Valentina (author). “Automatic data collection for facial expression recognition.” 2018. Web. 26 Jan 2021.

Vancouver:

Bollini V(. Automatic data collection for facial expression recognition. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:1335643e-6202-4a8c-bd51-9f7657a3611e.

Council of Science Editors:

Bollini V(. Automatic data collection for facial expression recognition. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:1335643e-6202-4a8c-bd51-9f7657a3611e

Delft University of Technology

3. Cherici, Teo (author). Robotic Auxiliary Losses for continuous reinforcement learning.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:82fb85b9-9b68-4f05-a89b-53e82c593194

►

Recent advancements in computation power and artificial intelligence have allowed the creation of advanced reinforcement learning models which could revolutionize, between others, the field of… (more)

Subjects/Keywords: Reinforcement Learning; Loss Functions; Robotics

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Cherici, T. (. (2018). Robotic Auxiliary Losses for continuous reinforcement learning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:82fb85b9-9b68-4f05-a89b-53e82c593194

Chicago Manual of Style (16^th Edition):

Cherici, Teo (author). “Robotic Auxiliary Losses for continuous reinforcement learning.” 2018. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:82fb85b9-9b68-4f05-a89b-53e82c593194.

MLA Handbook (7^th Edition):

Cherici, Teo (author). “Robotic Auxiliary Losses for continuous reinforcement learning.” 2018. Web. 26 Jan 2021.

Vancouver:

Cherici T(. Robotic Auxiliary Losses for continuous reinforcement learning. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:82fb85b9-9b68-4f05-a89b-53e82c593194.

Council of Science Editors:

Cherici T(. Robotic Auxiliary Losses for continuous reinforcement learning. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:82fb85b9-9b68-4f05-a89b-53e82c593194

Delft University of Technology

4. van Dijk, Tom (author). Low-memory Visual Route Following for Micro Aerial Vehicles in Indoor Environments.

Degree: 2017, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:82c91d74-6c01-4718-a574-221df210f01a

►

This thesis presents a visual route following method that minimizes memory consumption to the point that even Micro Aerial Vehicles (MAV) equipped with only a… (more)

Subjects/Keywords: navigation; route following; visual homing; odometry; indoor; micro aerial vehicle; MAV; unmanned aerial vehicle; UAV; quadrotor; vision; ARDrone; MAVLAB

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

van Dijk, T. (. (2017). Low-memory Visual Route Following for Micro Aerial Vehicles in Indoor Environments. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:82c91d74-6c01-4718-a574-221df210f01a

Chicago Manual of Style (16^th Edition):

van Dijk, Tom (author). “Low-memory Visual Route Following for Micro Aerial Vehicles in Indoor Environments.” 2017. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:82c91d74-6c01-4718-a574-221df210f01a.

MLA Handbook (7^th Edition):

van Dijk, Tom (author). “Low-memory Visual Route Following for Micro Aerial Vehicles in Indoor Environments.” 2017. Web. 26 Jan 2021.

Vancouver:

van Dijk T(. Low-memory Visual Route Following for Micro Aerial Vehicles in Indoor Environments. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:82c91d74-6c01-4718-a574-221df210f01a.

Council of Science Editors:

van Dijk T(. Low-memory Visual Route Following for Micro Aerial Vehicles in Indoor Environments. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:82c91d74-6c01-4718-a574-221df210f01a

Delft University of Technology

5. Resink, Tim (author). Vehicle motion prediction for autonomous driving: A deep learning model based on vehicle interaction and road geometry using a semantic map.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:bb469461-b879-40fd-abbc-1a6e0233bf1b

► To be able to understand the dynamic driving environment, an autonomous vehicle needs to predict the mo- tion of other traffic participants in the driving… (more)

▼ To be able to understand the dynamic driving environment, an autonomous vehicle needs to predict the mo- tion of other traffic participants in the driving scene. Motion prediction can be done based on experience and recently observed series of past events, and entails reasoning about probable outcomes with these past ob- servations. Aspects that influence driving behavior comprise many factors, such as general driving physics, infrastructure geometry, traffic rules, weather and so on. Models that are used today are incapable of including many of these aspects. These deep learning models often rely heavily on the past driven trajectory as an information source for future prediction. Sparsely, some work has been done to include interaction between vehicles or some infrastructural features to the model. The lack of information regarding the driving scene can be seen as a missed opportunity, because it is a use- ful feature source to predict the motion of a vehicle. Especially when a map of the driving scene is available, reliable information regarding the road layout can be extracted. In this thesis, a model architecture is sought that is aware of the interaction between vehicles, and that un- derstands the geometry of the roads in the scene. Importantly, map information regarding the driving scene should be used as the primary source of information regarding the road geometry. First, a baseline deep learning model is constructed, that can generate predictions based on the past observed trajectory. To add interaction features to the model, a social pooling module is introduced. The social pooling method allows to efficiently include the driving behavior of other vehicles in the scene. To introduce reliable, map-based road information to the model, two novel methods are proposed. In the first, the predictions from a model are used to extract features in a map. These features describe the road scene around the predicted location, and are used to update these predictions. In the second method, the semantic map is only used to extract a road segment ahead of the vehicle. A road-RNN is introduced to con- struct features regarding the road segment, and an attention mechanism to determine what part of the road segment is relevant for the predictions. These modules are referred to as road-refinement and road-attention respectively. The importance of including both road geometry and interaction methods in the model is shown by con- structing 5 different models that vary in their road-geometric and interaction awareness. A baseline deep learning model is used and extended with a road-geometry module, an interaction module, a combination of the two, or none. To test the prediction capabilities of the models, they are trained on two different datasets. The first dataset, called i80, consists of trajectory recordings from a straight highway with dense traffic. The other dataset is a curved version of the i80, called i80c, where the trajectories and road are transformed to introduce road-geometric variations in the data. The… Advisors/Committee Members: Jonker, Pieter (mentor), Gaisser, F. (mentor), Kooij, Julian (graduation committee), Alonso Mora, Javier (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Autonomous driving; Motion prediction; Deep learning

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Resink, T. (. (2019). Vehicle motion prediction for autonomous driving: A deep learning model based on vehicle interaction and road geometry using a semantic map. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:bb469461-b879-40fd-abbc-1a6e0233bf1b

Chicago Manual of Style (16^th Edition):

Resink, Tim (author). “Vehicle motion prediction for autonomous driving: A deep learning model based on vehicle interaction and road geometry using a semantic map.” 2019. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:bb469461-b879-40fd-abbc-1a6e0233bf1b.

MLA Handbook (7^th Edition):

Resink, Tim (author). “Vehicle motion prediction for autonomous driving: A deep learning model based on vehicle interaction and road geometry using a semantic map.” 2019. Web. 26 Jan 2021.

Vancouver:

Resink T(. Vehicle motion prediction for autonomous driving: A deep learning model based on vehicle interaction and road geometry using a semantic map. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:bb469461-b879-40fd-abbc-1a6e0233bf1b.

Council of Science Editors:

Resink T(. Vehicle motion prediction for autonomous driving: A deep learning model based on vehicle interaction and road geometry using a semantic map. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:bb469461-b879-40fd-abbc-1a6e0233bf1b

Delft University of Technology

6. van Laar, Patrick (author). Acoustic recognition of motorized vehicles with a moving listener.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:3e74c3a7-9099-48c0-ab26-afb0fa785629

►

New measures have to be taken to combat fatalities caused by traffic accidents. Intelligent vehicles have the potential to increase safety, but depend heavily on… (more)

Subjects/Keywords: Acoustic perception; Intelligent Vehicles; Machine Learning

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

van Laar, P. (. (2019). Acoustic recognition of motorized vehicles with a moving listener. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:3e74c3a7-9099-48c0-ab26-afb0fa785629

Chicago Manual of Style (16^th Edition):

van Laar, Patrick (author). “Acoustic recognition of motorized vehicles with a moving listener.” 2019. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:3e74c3a7-9099-48c0-ab26-afb0fa785629.

MLA Handbook (7^th Edition):

van Laar, Patrick (author). “Acoustic recognition of motorized vehicles with a moving listener.” 2019. Web. 26 Jan 2021.

Vancouver:

van Laar P(. Acoustic recognition of motorized vehicles with a moving listener. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:3e74c3a7-9099-48c0-ab26-afb0fa785629.

Council of Science Editors:

van Laar P(. Acoustic recognition of motorized vehicles with a moving listener. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:3e74c3a7-9099-48c0-ab26-afb0fa785629

Delft University of Technology

7. Zijlmans, Jeroen (author). Improving Monocular SLAM: using Depth Estimating CNN.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:af8aad54-e594-4cfe-a2ef-a3b3f302a4d5

► To bring down the number of traffic accidents and increase people’s mobility companies, such as Robot Engineering Systems (RES) try to put automated vehicles on… (more)

▼ To bring down the number of traffic accidents and increase people’s mobility companies, such as Robot Engineering Systems (RES) try to put automated vehicles on the road. RES is developing the WEpod, a shuttle capable of autonomously navigating through mixed traffic. This research has been done in cooperation with RES to improve the localization capabilities of the WEpod. The WEpod currently localizes using its GPS and lidar sensors. These have proven to be not accurate and reliable enough to safely navigate through traffic. Therefore, other methods of localization and mapping have been investigated. The primary method investigated in this research is monocular Simultaneous Localization and Mapping (SLAM). Based on literature and practical studies, ORB-SLAM has been chosen as the implementation of SLAM. Unfortunately, ORB-SLAM is unable to initialize the setup when applied on WEpod images. Literature has shown that this problem can be solved by adding depth information to the inputs of ORB-SLAM. Obtaining depth information for the WEpod images is not an arbitrary task. The sensors on the WEpod are not capable of creating the required dense depth-maps. A Convolutional Neural Network (CNN) could be used to create the depth-maps. This research investigates whether adding a depth-estimating CNN solves this initialization problem and increases the tracking accuracy of monocular ORB-SLAM. A well performing CNN is chosen and combined with ORB-SLAM. Images pass through the depth estimating CNN to obtain depth-maps. These depth-maps together with the original images are used in ORB-SLAM, keeping the whole setup monocular. ORB-SLAM with the CNN is first tested on the Kitti dataset. The Kitti dataset is used since monocular ORB- SLAM initializes on Kitti images and ground-truth depth-maps can be obtained for Kitti images. Monocular ORB-SLAM’s tracking accuracy has been compared to ORB-SLAM with ground-truth depth-maps and to ORB-SLAM with estimated depth-maps. This comparison shows that adding estimated depth-maps in- creases the tracking accuracy of ORB-SLAM, but not as much as the ground-truth depth images. The same setup is tested on WEpod images. The CNN is fine-tuned on 7481 Kitti images as well as on 642 WEpod images. The performance on WEpod images of both CNN versions are compared, and used in combination with ORB-SLAM. The CNN fine-tuned on the WEpod images does not perform well, missing details in the estimated depth-maps. However, this is enough to solve the initialization problem of ORB-SLAM. The combination of ORB-SLAM and the Kitti fine-tuned CNN has a better tracking accuracy than ORB-SLAM with the WEpod fine-tuned CNN. It has been shown that the initialization problem on WEpod images is solved as well as the tracking accuracy is increased. These results show that the initialization problem of monocular ORB-SLAM on WEpod images is solved by adding the CNN. This makes it applicable to improve the current localization methods on the WEpod. Using only this setup for localization on the WEpod is not possible yet,… Advisors/Committee Members: Gaisser, Floris (mentor), Kooij, Julian (mentor), Jonker, Pieter (graduation committee), Tejada Ruiz, Arturo (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: monocular SLAM; Depth-estimating CNN; ORB-SLAM

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Zijlmans, J. (. (2018). Improving Monocular SLAM: using Depth Estimating CNN. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:af8aad54-e594-4cfe-a2ef-a3b3f302a4d5

Chicago Manual of Style (16^th Edition):

Zijlmans, Jeroen (author). “Improving Monocular SLAM: using Depth Estimating CNN.” 2018. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:af8aad54-e594-4cfe-a2ef-a3b3f302a4d5.

MLA Handbook (7^th Edition):

Zijlmans, Jeroen (author). “Improving Monocular SLAM: using Depth Estimating CNN.” 2018. Web. 26 Jan 2021.

Vancouver:

Zijlmans J(. Improving Monocular SLAM: using Depth Estimating CNN. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:af8aad54-e594-4cfe-a2ef-a3b3f302a4d5.

Council of Science Editors:

Zijlmans J(. Improving Monocular SLAM: using Depth Estimating CNN. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:af8aad54-e594-4cfe-a2ef-a3b3f302a4d5

Delft University of Technology

8. van Apeldoorn, Naphur (author). 3D Reconstruction of Niniveh: A casestudy on the Bas-reliefs of the Southwest Palace of Sennacherib with monocular images.

Degree: 2017, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:54b5c5c4-1a36-4571-b0d4-4ef0450c311e

►

Parts of the Southwest Palace of Sennacherib (700 BC), which were located in Nineveh (Iraq), are now destroyed by the ongoing conflict in Iraq and… (more)

Subjects/Keywords: 3D Reconstruction; Niniveh; image reconstruction; Monocular; 3D printing; Cultural Heritage

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

van Apeldoorn, N. (. (2017). 3D Reconstruction of Niniveh: A casestudy on the Bas-reliefs of the Southwest Palace of Sennacherib with monocular images. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:54b5c5c4-1a36-4571-b0d4-4ef0450c311e

Chicago Manual of Style (16^th Edition):

van Apeldoorn, Naphur (author). “3D Reconstruction of Niniveh: A casestudy on the Bas-reliefs of the Southwest Palace of Sennacherib with monocular images.” 2017. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:54b5c5c4-1a36-4571-b0d4-4ef0450c311e.

MLA Handbook (7^th Edition):

van Apeldoorn, Naphur (author). “3D Reconstruction of Niniveh: A casestudy on the Bas-reliefs of the Southwest Palace of Sennacherib with monocular images.” 2017. Web. 26 Jan 2021.

Vancouver:

van Apeldoorn N(. 3D Reconstruction of Niniveh: A casestudy on the Bas-reliefs of the Southwest Palace of Sennacherib with monocular images. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:54b5c5c4-1a36-4571-b0d4-4ef0450c311e.

Council of Science Editors:

van Apeldoorn N(. 3D Reconstruction of Niniveh: A casestudy on the Bas-reliefs of the Southwest Palace of Sennacherib with monocular images. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:54b5c5c4-1a36-4571-b0d4-4ef0450c311e

Delft University of Technology

9. Raipuria, Geetank (author). Vehicle Trajectory Prediction Using Road Structure.

Degree: 2017, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:6cae1b47-f44e-4b74-8bfd-9098ce843e68

► An autonomous vehicle should be able to operate amidst numerous other human-driven vehicles, each driving on its own trajectory. To safely navigate in such a… (more)

Subjects/Keywords: Trajectory Prediction; Road Structure; Context; Long-term; Recurrent Neural Network; Interactive Multiple Model Filter; Path Planning; Automated driving

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Raipuria, G. (. (2017). Vehicle Trajectory Prediction Using Road Structure. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:6cae1b47-f44e-4b74-8bfd-9098ce843e68

Chicago Manual of Style (16^th Edition):

Raipuria, Geetank (author). “Vehicle Trajectory Prediction Using Road Structure.” 2017. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:6cae1b47-f44e-4b74-8bfd-9098ce843e68.

MLA Handbook (7^th Edition):

Raipuria, Geetank (author). “Vehicle Trajectory Prediction Using Road Structure.” 2017. Web. 26 Jan 2021.

Vancouver:

Raipuria G(. Vehicle Trajectory Prediction Using Road Structure. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:6cae1b47-f44e-4b74-8bfd-9098ce843e68.

Council of Science Editors:

Raipuria G(. Vehicle Trajectory Prediction Using Road Structure. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:6cae1b47-f44e-4b74-8bfd-9098ce843e68

Delft University of Technology

10. van Duijneveldt, Joris (author). On correction prediction in man and robot using the cerebellar model articulation controller.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:5b99031c-ab3e-4b01-a151-3b3e40ba2676

► The subject of this thesis is to investigate whether the Cerebellar Model Articulation Controller (CMAC) can be used to anticipate controller corrections and increase performance… (more)

Subjects/Keywords: (Artificial) Cerebellum; Delay reduction; Cerebellar Model Articulation Controller (CMAC); Purkinje Cell

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

van Duijneveldt, J. (. (2019). On correction prediction in man and robot using the cerebellar model articulation controller. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:5b99031c-ab3e-4b01-a151-3b3e40ba2676

Chicago Manual of Style (16^th Edition):

van Duijneveldt, Joris (author). “On correction prediction in man and robot using the cerebellar model articulation controller.” 2019. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:5b99031c-ab3e-4b01-a151-3b3e40ba2676.

MLA Handbook (7^th Edition):

van Duijneveldt, Joris (author). “On correction prediction in man and robot using the cerebellar model articulation controller.” 2019. Web. 26 Jan 2021.

Vancouver:

van Duijneveldt J(. On correction prediction in man and robot using the cerebellar model articulation controller. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:5b99031c-ab3e-4b01-a151-3b3e40ba2676.

Council of Science Editors:

van Duijneveldt J(. On correction prediction in man and robot using the cerebellar model articulation controller. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:5b99031c-ab3e-4b01-a151-3b3e40ba2676

Delft University of Technology

11. Vehmeijer, Leonore (author). Measures for the reduction of CO2 emissions, by the inland shipping fleet, on the Rotterdam-Antwerp corridor.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:1abd88e0-9ab6-47fd-a503-2f19ba13bbff

► The main aim of this research was to establish which measures are significant in reducing the CO2 emission levels on the waterways between Rotterdam and… (more)

Subjects/Keywords: emission reduction; Emission; carbon dioxide recudtion; Inland shipping; Rotterdam; Antwerp; python; simulation models; measures; Rijkswaterstaat; Scheldt-Rhine; Zuid-Beveland; Krammer locks; Kreekrak locks; Hansweert locks; Volkerak locks

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Vehmeijer, L. (. (2019). Measures for the reduction of CO2 emissions, by the inland shipping fleet, on the Rotterdam-Antwerp corridor. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:1abd88e0-9ab6-47fd-a503-2f19ba13bbff

Chicago Manual of Style (16^th Edition):

Vehmeijer, Leonore (author). “Measures for the reduction of CO2 emissions, by the inland shipping fleet, on the Rotterdam-Antwerp corridor.” 2019. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:1abd88e0-9ab6-47fd-a503-2f19ba13bbff.

MLA Handbook (7^th Edition):

Vehmeijer, Leonore (author). “Measures for the reduction of CO2 emissions, by the inland shipping fleet, on the Rotterdam-Antwerp corridor.” 2019. Web. 26 Jan 2021.

Vancouver:

Vehmeijer L(. Measures for the reduction of CO2 emissions, by the inland shipping fleet, on the Rotterdam-Antwerp corridor. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:1abd88e0-9ab6-47fd-a503-2f19ba13bbff.

Council of Science Editors:

Vehmeijer L(. Measures for the reduction of CO2 emissions, by the inland shipping fleet, on the Rotterdam-Antwerp corridor. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:1abd88e0-9ab6-47fd-a503-2f19ba13bbff

Delft University of Technology

12. Willems, Rick (author). Longitudinal Behavior Planning with Maneuver Prediction for Urban Driving.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:57f9a5f4-0608-42ca-b956-346afe5e4b7d

► One of the remaining challenges in the development of intelligent vehicles is the topic of behavior planning in urban scenarios. Based on perception of the… (more)

Subjects/Keywords: Behavior Planning; Decision Making; Model Predictive Control; Intelligent Vehicles; Autonomous driving

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Willems, R. (. (2018). Longitudinal Behavior Planning with Maneuver Prediction for Urban Driving. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:57f9a5f4-0608-42ca-b956-346afe5e4b7d

Chicago Manual of Style (16^th Edition):

Willems, Rick (author). “Longitudinal Behavior Planning with Maneuver Prediction for Urban Driving.” 2018. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:57f9a5f4-0608-42ca-b956-346afe5e4b7d.

MLA Handbook (7^th Edition):

Willems, Rick (author). “Longitudinal Behavior Planning with Maneuver Prediction for Urban Driving.” 2018. Web. 26 Jan 2021.

Vancouver:

Willems R(. Longitudinal Behavior Planning with Maneuver Prediction for Urban Driving. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:57f9a5f4-0608-42ca-b956-346afe5e4b7d.

Council of Science Editors:

Willems R(. Longitudinal Behavior Planning with Maneuver Prediction for Urban Driving. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:57f9a5f4-0608-42ca-b956-346afe5e4b7d

Delft University of Technology

13. van der Molen, Hessel (author). Development of a Low-Cost, Solid-State, Line-Laser Distance Sensor: An Integrated Approach.

Degree: 2017, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:509d12ba-7224-4e24-adff-d15059422319

► Many robotic systems rely on laser ranging sensors to navigate and map their environment. As robots are getting more advanced, smaller and cheaper, their sensors… (more)

Subjects/Keywords: Triangulation; Distance Measurement; Solid-State Sensor; Line-Laser; Modelling; Optimisation; Low-Cost; LiDAR

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

van der Molen, H. (. (2017). Development of a Low-Cost, Solid-State, Line-Laser Distance Sensor: An Integrated Approach. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:509d12ba-7224-4e24-adff-d15059422319

Chicago Manual of Style (16^th Edition):

van der Molen, Hessel (author). “Development of a Low-Cost, Solid-State, Line-Laser Distance Sensor: An Integrated Approach.” 2017. Masters Thesis, Delft University of Technology. Accessed January 26, 2021. http://resolver.tudelft.nl/uuid:509d12ba-7224-4e24-adff-d15059422319.

MLA Handbook (7^th Edition):

van der Molen, Hessel (author). “Development of a Low-Cost, Solid-State, Line-Laser Distance Sensor: An Integrated Approach.” 2017. Web. 26 Jan 2021.

Vancouver:

van der Molen H(. Development of a Low-Cost, Solid-State, Line-Laser Distance Sensor: An Integrated Approach. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Jan 26]. Available from: http://resolver.tudelft.nl/uuid:509d12ba-7224-4e24-adff-d15059422319.

Council of Science Editors:

van der Molen H(. Development of a Low-Cost, Solid-State, Line-Laser Distance Sensor: An Integrated Approach. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:509d12ba-7224-4e24-adff-d15059422319

		in
And / Or
		in
And / Or
		in
And / Or
		in

Open Access Theses and Dissertations