Steering control for haptic feedback and active safety functions

Steering feedback is an important element that defines driver–vehicle interaction. It strongly affects driving performance and is primarily dependent on the steering actuator\u27s control strategy. Typically, the control method is open loop, that is without any reference tracking; and its drawbacks are hardware dependent steering feedback response and attenuated driver–environment transparency. This thesis investigates a closed-loop control method for electric power assisted steering and steer-by-wire systems. The advantages of this method, compared to open loop, are better hardware impedance compensation, system independent response, explicit transparency control and direct interface to active safety functions.The closed-loop architecture, outlined in this thesis, includes a reference model, a feedback controller and a disturbance observer. The feedback controller forms the inner loop and it ensures: reference tracking, hardware impedance compensation and robustness against the coupling uncertainties. Two different causalities are studied: torque and position control. The two are objectively compared from the perspective of (uncoupled and coupled) stability, tracking performance, robustness, and transparency.The reference model forms the outer loop and defines a torque or position reference variable, depending on the causality. Different haptic feedback functions are implemented to control the following parameters: inertia, damping, Coulomb friction and transparency. Transparency control in this application is particularly novel, which is sequentially achieved. For non-transparent steering feedback, an environment model is developed such that the reference variable is a function of virtual dynamics. Consequently, the driver–steering interaction is independent from the actual environment. Whereas, for the driver–environment transparency, the environment interaction is estimated using an observer; and then the estimated signal is fed back to the reference model. Furthermore, an optimization-based transparency algorithm is proposed. This renders the closed-loop system transparent in case of environmental uncertainty, even if the initial condition is non-transparent.The steering related active safety functions can be directly realized using the closed-loop steering feedback controller. This implies, but is not limited to, an angle overlay from the vehicle motion control functions and a torque overlay from the haptic support functions.Throughout the thesis, both experimental and the theoretical findings are corroborated. This includes a real-time implementation of the torque and position control strategies. In general, it can be concluded that position control lacks performance and robustness due to high and/or varying system inertia. Though the problem is somewhat mitigated by a robust H-infinity controller, the high frequency haptic performance remains compromised. Whereas, the required objectives are simultaneously achieved using a torque controller

Using High-Level Processing of Low-Level Signals to Actively Assist Surgeons with Intelligent Surgical Robots

Robotic surgical systems are increasingly used for minimally-invasive surgeries. As such, there is opportunity for these systems to fundamentally change the way surgeries are performed by becoming intelligent assistants rather than simply acting as the extension of surgeons' arms. As a step towards intelligent assistance, this thesis looks at ways to represent different aspects of robot-assisted surgery (RAS). We identify three main components: the robot, the surgeon actions, and the patient scene dynamics. Traditional learning algorithms in these domains are predominantly supervised methods. This has several drawbacks. First many of these domains are non-categorical, like how soft-tissue deforms. This makes labeling difficult. Second, surgeries vary greatly. Estimation of the robot state may be affected by how the robot is docked and cable tensions in the instruments. Estimation of the patient anatomy and its dynamics are often inaccurate, and in any case, may change throughout a surgery. To obtain the most accurate information, these aspects must be learned during the procedure. This limits the amount of labeling that could be done. On the surgeon side, different surgeons may perform the same procedure differently and the algorithm should provide personalized estimations for surgeons. All of these considerations motivated the use of self-supervised learning throughout this thesis. We first build a representation of the robot system. In particular, we looked at learning the dynamics model of the robot. We evaluate the model by using it to estimate forces. Once we can estimate forces in free space, we extend the algorithm to take into account patient-specific interactions, namely with the trocar and the cannula seal. Accounting for surgery-specific interactions is possible because our method does not require additional sensors and can be trained in less than five minutes, including time for data collection. Next, we use cross-modal training to understand surgeon actions by looking at the bottleneck layer when mapping video to kinematics. This should contain information about the latent space of surgeon-actions, while discarding some medium-specific information about either the video or the kinematics. Lastly, to understand the patient scene, we start with modeling interactions between a robot instrument and a soft-tissue phantom. Models are often inaccurate due to imprecise material parameters and boundary conditions, particularly in clinical scenarios. Therefore, we add a depth camera to observe deformations to correct the results of simulations. We also introduce a network that learns to simulate soft-tissue deformation from physics simulators in order to speed up the estimation. We demonstrate that self-supervised learning can be used for understanding each part of RAS. The representations it learns contain information about signals that are not directly measurable. The self-supervised nature of the methods presented in this thesis lends itself well to learning throughout a surgery. With such frameworks, we can overcome some of the main barriers to adopting learning methods in the operating room: the variety in surgery and the difficulty in labeling enough training data for each case