In robot-assisted orthopaedic surgery, registration is a key step, which defines the position of the patient in the robot frame so that the preoperative plan can be performed. Current registration methods have their limitations, such as the requirement of immobilisation of the limbs or the line of sight (LOS) issues. These issues cause inconvenience for the surgeons and interrupt the surgical workflow in the operating room.

Targetting these issues of current registration methods, we propose a camera-robot registration system for joint replacement. The bone geometry, which is measured directly by a depth camera, is aligned to a preoperatively obtained bone model to calculate the pose of the target. Simultaneously, in order to avoid registration failure caused by LOS interruptions, the depth camera tracks objects that may occlude the target bone, and a robot manipulator is used to move the camera away from the nearest obstacle. The optimal camera motion is calculated based on the position and velocity of the obstacle, which avoids the occlusion efficiently without changing the target position in the camera frame. Inverse kinematics of the robot is used to project the Cartesian velocity of the end-effector into the joint space, with kinematic singularities considered for stable robotic control. An admittance controller is designed as the human-robot interface so that the surgeon can directly set the robot configuration by hand according to the actual environment.

Simulations and experiments were conducted to test the performance. The results show that the proposed obstacle avoidance method can effectively increase the distance between the obstacle and the LOS, which lowers the risk of registration failure due to obstacle occlusion. This pilot study is promising in reducing distractions to the surgeon and can help achieve a fluent and surgeon-centred workflow.

Joint assessment through manual physical examination is a fundamental skill that must be acquired by orthopaedic surgeons. These joint assessments allow surgeons to identify soft tissue injuries (e.g. ligament tears) which are critical in identifying appropriate treatment options.

The difficulty in communicating the feeling of different joint conditions and the limited opportunities for practice can make these skills challenging to learn, resulting in reduced treatment effectiveness and increased costs. This research seeks to improve the training of joint assessment with the creation of a haptic joint simulator that can train surgeons with increased effectiveness.

A first of its kind haptic simulator is presented, which incorporates: a newly defined kinetic knee simulation, a haptic device for user interaction, and a haptic control algorithm. The knee model has been specifically created for this application and allows six degree-of-freedom manipulation of the tibia while considering the effects of ten knee ligament bundles. The model has been mathematically formulated to allow for the high update rates necessary for smooth and stable haptic simulation.

Two quantitative assessments were made of the model to confirm its clinical validity. The first was against the widely used OpenSim biomechanical simulation software. Simulations of the model's performance for both anterior-posterior draw tests and varus-valgus rotation tests showed less than 0.7%RMSE for force and 5.5%RMSE for moments. Crucially, the proposed model could generate updated forces in less than 1ms, compared to 188ms for OpenSim. The second validation of the model was against a cadaveric knee that was tested using a validated robotic testing platform. This comparison showed that the model could generate similar force- motion pathways to the cadaveric knee after the model's parameters were scaled to match.

Having demonstrated that it is possible to create a computational knee model that has good conformance to gold-standard knee simulations and cadaveric recordings, while updating at less than 1ms, this research has overcome a major hurdle. The next stage of this research will be to incorporate the knee model into a full haptic simulator and perform skill acquisition trials. Given the effectiveness of past haptic training systems in aiding clinical skills acquisition, this research offers a promising way to improve surgeon training, and therefore also patient diagnosis and treatment.