Ioannis Georgilas

Electro-hydraulic servoactuators have great potential in mobile robotics due to their robustness, high bandwidth and power density, but compared with electromechanical actuators, they can be inefficient and more difficult to integrate into systems. The Integrated Smart Actuator (ISA) developed by Moog Controls Ltd. is a hydromechatronic device that aims to address these issues by combining a novel efficient servovalve, cylinder, sensors and control electronics into a single component. The aim of this work was to develop a robust motion control algorithm that can make integration of the ISA into a robotic system straightforward by requiring minimal controller set-up despite variations in the load characteristics. The proposed controller is a sliding mode controller with a varying boundary layer that contains two robustness parameters and a single bandwidth parameter that defines the response. The controller outperforms a conventional high-performance linear controller in terms of tracking performance and its robustness to variations in the load mass and fluid bulk modulus. The response when the system was subject to some unachievable demand trajectories, such as large step demands, was found to be poor, and an online velocity, acceleration and jerk limited trajectory filter was demonstrated to rectify this issue. The successful implementation of a robust motion controller enables this highly novel integrated actuator to live up to its ‘smart’ epithet.

Introduction: Compliant mechanisms, especially continuum robots, are becoming integral to advancements in minimally invasive surgery due to their ability to autonomously navigate natural pathways, significantly reducing collision severity. A major challenge lies in developing an effective control strategy to accurately reflect their behavior for enhanced operational precision.Methods: This study examines the trajectory tracking capabilities of a tendon-driven continuum robot at its tip. We introduce a novel feedforward control methodology that leverages a mathematical model based on Cosserat rod theory. To mitigate the computational challenges inherent in such models, we implement an implicit time discretization strategy. This approach simplifies the governing equations into space-domain ordinary differential equations, facilitating real-time computational efficiency. The control strategy is devised to enable the robot tip to follow a dynamically prescribed trajectory in two dimensions.Results: The efficacy of the proposed control method was validated through experimental tests on six different demand trajectories, with a motion capture system employed to assess positional accuracy. The findings indicate that the robot can track trajectories with an accuracy within 9.5%, showcasing consistent repeatability across different runs.Discussion: The results from this study mark a significant step towards establishing an efficient and precise control methodology for compliant continuum robots. The demonstrated accuracy and repeatability of the control approach significantly enhance the potential of these robots in minimally invasive surgical applications, paving the way for further research and development in this field.

Conventional trajectory planning for lower limb assistive devices usually relies on a finite-state strategy, which pre-defines fixed trajectory types for specific gait events and activities. The advancement of deep learning enables walking assistive devices to better adapt to varied terrains for diverse users by learning movement patterns from gait data. Using a self-attention mechanism, a temporal deep learning model is developed in this study to continuously generate lower limb joint angle trajectories for an ankle and knee across various activities. Additional analyses, including using Fast Fourier Transform and paired t-tests, are conducted to demonstrate the benefits of the proposed attention model architecture over the existing methods. Transfer learning has also been performed to prove the importance of data diversity. Under a 10-fold leave-one-out testing scheme, the observed attention model errors are 11.50% (±2.37%) and 9.31% (±1.56%) NRMSE for ankle and knee angle estimation, respectively, which are small in comparison to other studies. Statistical analysis using the paired t-test reveals that the proposed attention model appears superior to the baseline model in terms of reduced prediction error. The attention model also produces smoother outputs, which is crucial for safety and comfort. Transfer learning has been shown to effectively reduce model errors and noise, showing the importance of including diverse datasets. The suggested joint angle trajectory generator has the potential to seamlessly switch between different locomotion tasks, thereby mitigating the problem of detecting activity transitions encountered by the traditional finite-state strategy. This data-driven trajectory generation method can also reduce the burden on personalization, as traditional devices rely on prosthetists to experimentally tune many parameters for individuals with diverse gait patterns.

The formation of porosity is a major challenge in any composite manufacturing process, particularly in the absence of vacuum assistance. Highly localized injection of polymer matrix into regions of interest in a dry preform is a route to manufacturing multi-matrix fiber-reinforced composites with high filler concentrations, which are otherwise difficult to achieve. Unlike traditional composites, such multi-matrix fiber-reinforced composite systems, which combine multiple resins in continuous form, offer improved structural performance around stress concentrators and multifunctional capabilities. As the process lacks vacuum assistance, porosity becomes a primary issue to be addressed. This paper presents a rheo-kinetic coupled rapid consolidation procedure for optimizing the quality of localized matrix patches. The procedure involves manufacturing trials and analytical consolidation models to determine the best processing program for minimal voidage in the patch. The results provide a step toward an efficient manufacturing process for the optimal design of multi-matrix composites without the need for complex vacuum bag arrangements, thus reducing cost and time while opening avenues to improve overall composite performance.

OctoMap is an efficient probabilistic mapping framework to build occupancy maps from point clouds, representing 3D environments with cubic nodes in the octree. However, the map update policy in OctoMap has limitations. All the nodes containing points will be assigned with the same probability regardless of the points being noise, and the probability of one such node can only be increased with a single measurement. In addition, potentially occupied nodes with points inside but traversed by rays cast from the sensor to endpoints will be marked as free. To overcome these limitations in OctoMap, the current work presents a mapping method using the context of neighbouring points to update nodes containing points, with occupancy information of a point represented by the average distance from a point to its k-Nearest Neighbours. A relationship between the distance and the change in probability is defined with the Cumulative Density Function of average distances, potentially decreasing the probability of a node despite points being present inside. Experiments are conducted on 20 data sets to compare the proposed method with OctoMap. Results show that our method can achieve up to 10% improvement over the optimal performance of OctoMap.

Occupancy mapping is widely used to generate volumetric 3D environment models from point clouds, informing a robotic platform which parts of the environment are free and which are not. The selection of the parameters that govern the point cloud generation algorithms and mapping algorithms affects the process and the quality of the final map. Although previous studies have been reported in the literature on optimising major parameter configurations, research in the process to identify optimal parameter sets to achieve best occupancy mapping performance remains limited. The current work aims to fill this gap with a two-step principled methodology that first identifies the most significant parameters by conducting Neighbourhood Component Analysis on all parameters and then optimise those using grid search with the area under the Receiver Operating Characteristic curve. This study is conducted on 20 data sets with specially designed targets, providing precise ground truths for evaluation purposes. The methodology is tested on OctoMap with point clouds created by applying StereoSGBM on the images from a stereo camera. A clear indication can be seen that mapping parameters are more important than point cloud generation parameters. Moreover, up to 15% improvement in mapping performance can be achieved over default parameters.

Reduction of fractures in the minimally invasive (MI) manner can avoid risks associated with open fracture surgery. The MI approach requires specialised tools called percutaneous fragment manipulation devices (PFMD) to enable surgeons to safely grasp and manipulate fragments. PFMDs developed for long-bone manipulation are not suitable for intra-articular fractures where small bone fragments are involved. With this study we offer a solution to potentially move the current fracture management practice closer to the use of a MI approach. We investigate the design and testing of a new PFMD design for manual as well as robot-assisted manipulation of small bone fragments. This new PFMD design is simulated using FEA in three loading scenarios (force/torque: 0N/2.6Nm, 75.7N/3.5N, 147N/6.8Nm) assessing structural properties, breaking points, and maximum bending deformations. The PFMD is tested in a laboratory setting on Sawbones models (0N/2.6Nm), and on ex-vivo swine samples (F=80N±8N, F=150±15N). A commercial optical tracking system was used for measuring PFMD deformations under external loading and the results were verified with an electromagnetic tracking system. The average error difference between the tracking systems was 0.5mm, being within their accuracy limits. Final results from reduction manoeuvres performed both manually and with the robot assistance are obtained from 7 human cadavers with reduction forces in the range of (F=80N±8N, F=150±15N, respectively). The results show that structurally, the system performs as predicted by the simulation results. The PFMD did not break during ex-vivo and cadaveric trials. Simulation, laboratory, and cadaveric tests produced similar results regarding the PFMD bending. Specifically, for forces applied perpendicularly to the axis of the PFMD of 80±8N deformations of 2.8mm, 2.97mm, and 3.06mm are measured on the PFMD, while forces of 150±15N produced deformations of 5.8mm, 4.44mm, and 5.19mm. This study has demonstrated that the proposed PFMD undergoes predictable deformations under typical bone manipulation loads. Testing of the device on human cadavers proved that these deformations do not affect the anatomic reduction quality. The PFMD is, therefore, suitable to reliably achieve and maintain fracture reductions, and to, consequently, allow external fracture fixation.

A wide range of applications for which Unmanned Aerial Vehicles (UAVs) are ideally suited rely on the development of manipulators capable of exchanging forces with the environment. One such application is the installation and retrieval of intelligent sensors for monitoring wide-spread areas and locations that are difficult to access by any other means. Within this paper we report on both indoor and outdoor flights tests of a novel force controlled lightweight compliant manipulator which allows a UAV to carry out this type of task. Installation and retrieval are both demonstrated with different scenarios, indoors and outdoors. Key results include interaction forces up to 22 N exerted by a small-sized multirotor; placement and retrieval operations carried out on flat as well as cylindrical surfaces; and an analysis of the overall system. The results of multiple flight experiments clearly demonstrate the potential of this approach for the deployment of sensors and other force-related tasks.

The design of medical devices is a complex and crucial process to ensure patient safety. It has been shown that improperly designed devices lead to errors and associated accidents and costs. A key element for a successful design is incorporating the views of the primary and secondary stakeholders early in the development process. They provide insights into current practice and point out specific issues with the current processes and equipment in use. This work presents how information from a user-study conducted in the early stages of the RAFS (Robot Assisted Fracture Surgery) project informed the subsequent development and testing of the system. The user needs were captured using qualitative methods and converted to operational, functional, and non-functional requirements based on the methods derived from product design and development. This work presents how the requirements inform a new workflow for intra-articular joint fracture reduction using a robotic system. It is also shown how the various elements of the system are developed to explicitly address one or more of the requirements identified, and how intermediate verification tests are conducted to ensure conformity. Finally, a validation test in the form of a cadaveric trial confirms the ability of the designed system to satisfy the aims set by the original research question and the needs of the users.