Teleoperated semi-autonomous care robots aim to alleviate work pressure from care workers. Unlike many traditional stiff position-controlled robots, the care robot is operating in a shared environment with humans that is often unpredictable and unknown. Especially when dealing with tasks that involve contact with the environment, modulation of compliance is a key component to successfully execute autonomous manipulation tasks. Using Learning from Demonstrating (LfD) techniques, the teleoperator must have a system that allows intuitive demonstration of compliance to the robot. Current state-of-the-art systems are either not teleoperated, only allow limited modulation of the stiffness matrix or, are too complex and cumbersome for practical applications. This research tries to overcome these limitations and proposes a teleoperated stiffness commanding method that allows complete modulation of stiffness matrix in 3 Degrees of Freedom (DoF). The system uses the same haptic device (Geomagic Touch) as used for controlling robot manipulator, hence does not require specialized equipment. Through wiggling the stylus of the haptic device, stiffness is commanded to the robot and directly fed back to the operator through haptic and visual feedback. The system is illustrated in a simulated task where a task appropriate stiffness profile is demonstrated along a kinematic trajectory. Additionally, the performance and acceptance of the system is evaluated through a simulated user study. It shows how varying the commanded DoF, orientation, and size of the stiffness commands significantly influences the performance through the eigenvectors and eigenvalues of the stiffness matrix.

Quadrupedal robots possess the ability to move freely in the world and perform a variety of actions that would be unsafe or impractical for humans to perform. In the SNOW project, a quadrupedal robot is tasked with aiding firefighters in rescue missions during house fires by locating humans and assessing their health. Pushing away obstacles that cannot be circumvented otherwise is one of the many capabilities a quadrupedal robot should possess to be of most use in such missions. We develop a proof of concept by solving two problems sequentially: which stance to take on prior to the push and how to perform the push. The process of stance selection starts with generating a certain amount of stances. Stances are generated starting from a preselected stance appropriate to the goal location and deviating from the 12 joint angles with a normal distribution. All generated stances are ran through a number of filters, which rely on solving for the forward and inverse kinematics of the robot. These filters check if the initial position is sensible and balanced and if the projected final position is close to the goal and balanced. The inverse kinematics are solved using Adaptive-Network-Based Fuzzy Inference Systems (ANFIS), which results in accurate estimations within a time frame that can be used in real-time applications. The final stance is selected by comparing the total displacement of all joint angles per stance, where the lowest total displacement is considered optimal. The push is controlled by a nonlinear model predictive controller. We strive for a stable push, where the contact between the pusher and the object sticks, by keeping the movement of the end-effector within the motion cone. The motion cone denotes all twists the object can have without slipping at the contact with the pusher and is constructed using the limit surface to model the interaction between the object and the support surface and the generalized friction cone to model the interaction between the pusher and the object. We find that the motion cone predicts stick and slip with an accuracy slightly higher than 80%. Our controller steers accurately to all goals that lie within the motion cone and moves the object with a twist on the edge of the motion cone if the goal location lies outside of the motion cone. The robot remains balanced throughout the pushing motion in the vast majority of cases, but is more at risk of tipping over when pushing heavier objects.

Robots that use legged locomotion have the ability to overcome obstacles and can negotiate a wide range of difficult terrains, such as encountered in outer-space missions. In many practical scenarios however, their applicability is still limited, mainly due to insufficient speed and efficiency. On the other hand, robots that use wheeled locomotion are fast and efficient, but are generally confined to flat or prepared surfaces. A relatively new approach, to use the advantages of both forms, is the combination of walking and driving technology into Hybrid Walker-Wheeler technology. In this research we will explore the feasibility of applying Hybrid Walker-Wheeler technology to the Zesbenige Robot (ZebRo). ZebRo is a small walking robot with six One-Degree-of-Freedom (DoF) legs and walks with an insect-inspired gait. It is being developed with the intention to go on a mission to the moon. The objective in this thesis is to increase the speed and energy efficiency of the ZebRo on flat surfaces, while maintaining its robustness and walking capability on rough terrain. We will set up the criteria for a new design, explore various options and parameters and choose a concept. We will then do a number of simulations to analyse the properties of a wheel and a leg and to apply these in the design. The final prototype consists of a single module with a wheel, a one-DoF leg and a custom-design coupling which switches torque, from the motor, between the wheel-axle and the leg-axle. This prototype was tested and evaluated on its electrical power consumption and the torque and speed transmitted to the leg-axle and wheel-axle. From these results, we were able to draw a number of conclusions and make recommendations for a ZebRo equipped with Hybrid Walker-Wheeler technology.

The generation of a 3D map of an unseen environment, obtained through solving the SLAM problem, is a popular topic currently in the robotics domain. The Lunar Rover Mini (LRM) at the German Aerospace Center solves this problem using a RGB-D camera system, which is favourable in space applications due to its lightweight characteristics and energy-efficiency. Performing SLAM based on camera images is based on visual odometry: the science of estimating the rover’s trajectory trough a sequence of images. However, the dependency on a single sensor to perform mapping and navigation poses a threat to the reliability of the system. To increase the reliability and robustness of the SLAM algorithm, an inertial measurement unit (IMU) is incorporated in the robot hardware. This thesis describes the design for a visual-inertial SLAM algorithm that incorporates both visual and inertial measurements to solve the SLAM problem through performing tightly coupled sensor fusion, which estimates and corrects for IMU biases. The solution is based on a non-linear factor graph, which is a graphical model to represent the relationships between the rover’s measurements and the unknown variables which are optimised for. This is done using the open-source GTSAM framework. Using experimental data, the robustness of the novel visual-inertial SLAM algorithm is demonstrated by simulating specific sensor failures. Moreover, the novel algorithm shows its capability to incorporate a degree of certainty regarding specific areas of the generated map, closely resembling how a human being would generate a map of an unknown area. An additional use case for tightly coupled sensor fusion is the increased accuracy of the estimated trajectory. Assuming Gaussian noise models for both measurement models, averaging the two can yield a higher accuracy than either of the two sensors could have obtained by itself. This hypothesis was tested in another experiment. As the main mechanism behind bias estimation is reducing the error between visual and inertial measurements, bias estimation is quickly affected by this drifting visual odometry, which in its turn deteriorates the accuracy of the visual-inertial odometry module. This observation proves that the bias estimation is not correlated to the underlying physical process, but is rather just a numerical value in the optimisation reducing the residual error. It raises the question whether this strategy of tightly coupled sensor fusion can actually be used to increase the accuracy of a visual odometry algorithm.

This thesis is motivated by evolutionary robot systems where robot bodies and brains evolve simultaneously. In such a robot system, `birth' must be followed by `infant learning' by a learning method that works for various morphologies evolution may produce. Here we address the task of directed locomotion in modular robots with controllers based on Central Pattern Generators. We present a bio-inspired adaptive feedback mechanism that uses a forward model and an inverse model that can be learned on-the-fly. We compare two versions (a simple and a sophisticated one) of this concept to a traditional (open-loop) controller using Bayesian Optimization as a learning algorithm. The experimental results show that the sophisticated version outperforms the simple one and the traditional controller. It leads to improvement in performance and more robust controllers that cope better with noise.

Active inference is a novel brain theory based on the free energy principle, stating that every organism, in order to stay alive, minimizes a certain free energy. This theory is being translated into robot control, hoping to mimic the capabilities of the brain. Research in this field of robotics is still quite young, and active inference has yet to mature into a reliable control theory. Implementations of active inference are scarce, and hierarchical implementations, containing multiple layers of active inference, are almost non-existent. This work presents the first implementation of hierarchical active inference performing online control on a robot in continuous state-space. The robot in question is a skid-steering mobile robot, on which two different active inference controllers are implemented and compared. The first controller is a non-hierarchical active inference controller giving wheel speed commands to the default PID controller of the robot manufacturer based on desired body velocities. The second controller is a novel implementation of a hierarchical active inference controller, that besides the function of the first controller, also replaces the low-level PID control with active inference, resulting in two-layers of active inference ultimately taking desired body velocities as inputs and translating it into open loop voltage commands for the motors. Both controllers are tuned to give roughly the same step responses in order to get a clearer view on the differences between non-hierarchical and hierarchical active inference. During the tuning process of the hierarchical controller, it became clear that no amount of tuning could get the (causal) states to convergence sufficiently fast. This problem was identified to be caused by the message passing in between the layers of active inference, where the prediction error from below negatively impacts convergence. This impact scales together with the tuning parameters, and therefore is not easily fixed by tuning alone. A potential solution is given in the form a an extra parameter in front of the upwards prediction error, giving the ability the independently tune the impact of this error on the behaviour of the system. This solution is used to fully tune the hierarchical controller, after which the performance is compared to the non-hierarchical controller, using a series of experiments. A total of 8 experiments are done where both controllers are tested performing two driving actions in two situations, namely cornering and pivoting on the ground and with the wheels suspended. The non-hierachical controller, as a baseline, performed satisfactory, but the hierarchical controller does not run smoothly on the ground, potentially struggling with the stick-slip of the wheel-ground interactions. From this it seems that using active inference for the low-level, as compared to something like PID, is less suitable. However, that conclusion is too premature for this work as the available encoder feedback frequency for the active inference is way lower than what the MCU-level PID has access to, making the comparison less fair. The main contribution of this work lies in the insight in the implementation structure of hierachical active inference for continuous state-space control, which can be further built upon, using the lessons learned during implementation.

An intracranial aneurysm is a bulge in the cerebral vasculature. The rupture of an aneurysm results in a brain bleed. As a consequence, most patients become severely handicapped or may even die. Preventive treatment with endovascular coiling is controversial due to the high risk of complications. These complications are partly caused by the current uncontrolled delivery of coils to the aneurysm. While recent developments in microcatheter design allow for improved positioning, no method has been devised to model and control the tension applied by the coil on the aneurysm wall throughout a coiling procedure. This thesis aims to come up with a method to improve the safety of aneurysm treatment. This thesis presents a new way to dynamically model an endovascular coil and its interaction with a microcatheter and the aneurysm wall throughout a coil deployment procedure. The main advantage of this model is its low computational complexity allowing real-time control computation. A control architecture is presented that enables regulation of the contact force between the coil and the aneurysm wall while obeying the constraints imposed by the equipment and the environment. The presented architecture comprises an augmented energy-shaping controller working in parallel with a constraint preservation controller. This thesis shows that this control architecture asymptotically stabilizes the aneurysm wall tension at the desired value throughout the coil deployment procedure. This work provides a basis for modelling and control in future experimental validations. Therefore, this work is a promising first step in the modelling and control of robotic systems for neurovascular interventions and a step forward in the preventive treatment of intracranial aneurysms.

During the transfer of calli in plant regeneration, the repetitive work of pick and placement of calli in new agar is still done by human operators. This process can be automated to eliminate labour-intensive work. The thesis focuses on the development of a robotic gripper to automate the transfer of calli. To be able to grip calli, first its physical properties have to be investigated. With different weights, the maximum allowable force before calli damage was determined. Subsequently a methodological design process was performed, which consisted of formulating system requirements and conceptual design analysis with the use of the Kesselring method. In order to finalize the design, the two highest scoring gripper concepts were chosen to be further explored. Both concepts were transformed in a 3D design and printed to validate its effectiveness for gripping calli. Of those two concepts, the best performing gripper was further optimized and used as prototype for additional validation. To validate this new gripper prototype, calli of different shapes and sizes were transferred to a new petri dish. To correctly grip the calli, a script was created to adequately detect and determine the size of the calli. The plastic prototype had high slack and inaccuracies due its the material. Therefore, the prototype was made of aluminium, with the use of Computer Numerical Control (CNC). Experiments revealed that the aluminium prototype had a success rate of 98% when gripping calli. In conclusion, the designed aluminium gripper was able to grip and release the calli without damage and has therefore great potential to automate calli transfer and thereby improve the quality and quantity of plant regeneration.