arXiv subjects

Kyrre Glette

Publications and source records attributed to Kyrre Glette.

41 records · Page 3Linked to original sources

arXiv2018

Multi-Objective Convolutional Neural Networks for Robot Localisation and 3D Position Estimation in 2D Camera Images

The field of collaborative robotics and human-robot interaction often focuses on the prediction of human behaviour, while assuming the information about the robot setup and configuration being known. This is often the case with fixed setups, which have all the sensors fixed and calibrated in relation to the rest of the system. However, it becomes a limiting factor when the system needs to be reconfigured or moved. We present a deep learning approach, which aims to solve this issue. Our method learns to identify and precisely localise the robot in 2D camera images, so having a fixed setup is no longer a requirement and a camera can be moved. In addition, our approach identifies the robot type and estimates the 3D position of the robot base in the camera image as well as 3D positions of each of the robot joints. Learning is done by using a multi-objective convolutional neural network with four previously mentioned objectives simultaneously using a combined loss function. The multi-objective approach makes the system more flexible and efficient by reusing some of the same features and diversifying for each objective in lower layers. A fully trained system shows promising results in providing an accurate mask of where the robot is located and an estimate of its base and joint positions in 3D. We compare the results to our previous approach of using cascaded convolutional neural networks.

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arXiv2018

Real-World Evolution Adapts Robot Morphology and Control to Hardware Limitations

For robots to handle the numerous factors that can affect them in the real world, they must adapt to changes and unexpected events. Evolutionary robotics tries to solve some of these issues by automatically optimizing a robot for a specific environment. Most of the research in this field, however, uses simplified representations of the robotic system in software simulations. The large gap between performance in simulation and the real world makes it challenging to transfer the resulting robots to the real world. In this paper, we apply real world multi-objective evolutionary optimization to optimize both control and morphology of a four-legged mammal-inspired robot. We change the supply voltage of the system, reducing the available torque and speed of all joints, and study how this affects both the fitness, as well as the morphology and control of the solutions. In addition to demonstrating that this real-world evolutionary scheme for morphology and control is indeed feasible with relatively few evaluations, we show that evolution under the different hardware limitations results in comparable performance for low and moderate speeds, and that the search achieves this by adapting both the control and the morphology of the robot.

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arXiv2018

Exploring Mechanically Self-Reconfiguring Robots for Autonomous Design

Evolutionary robotics has aimed to optimize robot control and morphology to produce better and more robust robots. Most previous research only addresses optimization of control, and does this only in simulation. We have developed a four-legged mammal-inspired robot that features a self-reconfiguring morphology. In this paper, we discuss the possibilities opened up by being able to efficiently do experiments on a changing morphology in the real world. We discuss present challenges for such a platform and potential experimental designs that could unlock new discoveries. Finally, we place our robot in its context within general developments in the field of evolutionary robotics, and consider what advances the future might hold.

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arXiv2016

Automatic Calibration of a Robot Manipulator and Multi 3D Camera System

With 3D sensing becoming cheaper, environment-aware and visually-guided robot arms capable of safely working in collaboration with humans will become common. However, a reliable calibration is needed, both for camera internal calibration, as well as Eye-to-Hand calibration, to make sure the whole system functions correctly. We present a framework, using a novel combination of well proven methods, allowing a quick automatic calibration for the integration of systems consisting of the robot and a varying number of 3D cameras by using a standard checkerboard calibration grid. Our approach allows a quick camera-to-robot recalibration after any changes to the setup, for example when cameras or robot have been repositioned. Modular design of the system ensures flexibility regarding a number of sensors used as well as different hardware choices. The framework has been proven to work by practical experiments to analyze the quality of the calibration versus the number of positions of the checkerboard used for each of the calibration procedures.

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arXiv2016

Multi 3D Camera Mapping for Predictive and Reflexive Robot Manipulator Trajectory Estimation

With advancing technologies, robotic manipulators and visual environment sensors are becoming cheaper and more widespread. However, robot control can be still a limiting factor for better adaptation of these technologies. Robotic manipulators are performing very well in structured workspaces, but do not adapt well to unexpected changes, like people entering the workspace. We present a method combining 3D Camera based workspace mapping, and a predictive and reflexive robot manipulator trajectory estimation to allow more efficient and safer operation in dynamic workspaces. In experiments on a real UR5 robot our method has proven to provide shorter and smoother trajectories compared to a reactive trajectory planner in the same conditions. Furthermore, the robot has successfully avoided any contact by initialising the reflexive movement even when an obstacle got unexpectedly close to the robot. The main goal of our work is to make the operation more flexible in unstructured dynamic workspaces and not just avoid obstacles, but also adapt when performing collaborative tasks with humans in the near future.

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