Computer vision is an important cornerstone for the foundation of many modern technologies. The development of modern computer-aided-surgery, especially in the context of surgical navigation for minimally invasive surgery, is one example. Surgical navigation provides the necessary spatial information in computer-aided-surgery. Amongst the various forms of perception, vision-based sensing has been proposed as a promising candidate for tracking and localisation application largely due to its ability to provide timely intra-operative feedback and contactless sensing. The motivation for vision-based sensing in surgical navigation stems from many factors, including the challenges faced by other forms of navigation systems. A common surgical navigation system performs tracking of surgical tools with external tracking systems, which may suffer from both technical and usability issues. Vision-based tracking offers a relatively streamlined framework compared to those approaches implemented with external tracking systems. This review study aims to discuss contemporary research and development in vision-based sensing for surgical navigation. The selected review materials are expected to provide a comprehensive appreciation of state-of-the-art technology and technical issues enabling holistic discussions of the challenges and knowledge gaps in contemporary development. Original views on the significance and development prospect of vision-based sensing in surgical navigation are presented.

A key challenge associated with myoelectric prosthesis limbs is the acquisition of a good quality gesture intent signal from the residual anatomy of an amputee. In this study, the authors aim to overcome this limitation by observing the classification accuracy of the fusion of wearable electromyography (EMG) and near-infrared (NIR) to classify eight hand gesture motions across 12 able-bodied participants. As part of the study, they investigate the classification accuracy across a multi-layer perceptron neural network, linear discriminant analysis and quadratic discriminant analysis for different sensing configurations, i.e. EMG-only, NIR-only and EMG-NIR. A separate offline ultrasound scan was conducted as part of the study and served as a ground truth and contrastive basis for the results picked up from the wearable sensors, and allowed for a closer study of the anatomy along the humerus during gesture motion. Results and findings from the work suggest that it could be possible to further develop transhumeral prosthesis using affordable, ergonomic and wearable EMG and NIR sensing, without the need for invasive neuromuscular sensors or further hardware complexity.

In this study, a robust controller is proposed for rollover risk suppression in automatically-driven vehicles by reducing the lateral acceleration through a steer-by-wire system equipped on the vehicles. First, since the slip angle is difficult to measure directly, a sliding mode observer combining an add-on switching term with a linear observer is developed to provide more robust estimation of slip angle in the presence of system uncertainties. Second, an adaptive sliding mode control method is proposed, in which the control gain is adaptively tuned to compensate for the system uncertainties. Lastly, simulation is conducted and the results verify that the proposed estimator and controller can reduce the vehicle rollover risk efficiently and robustly.

To solve the problem of uncertain parameters in dynamic modelling of upper-limb rehabilitation robots, a dynamic parameter identification method based on variable parameters particle swarm optimisation (PSO) is developed. Based on the dynamic model of the system, the algorithm changes the inertia parameter and learning law of the basic PSO algorithm from the fixed-parameter to the function that changes with the number of iterations. It solves the problems of small search space in the early stage and slow convergence speed in the later stage of the basic PSO algorithm, which greatly improves its identification accuracy. Finally, through the comparison and analysis of the simulation results, compared with those of the least square (LS) and unmodified PSO identification algorithms, a great improvement in the identification accuracy of the algorithm is achieved. The control effect in the actual control system is also much better than those of the LS and PSO algorithms.

In this study, the authors address the problem of optimal routing and relative motion control in a network of robots. The path planning scheme has been designed using a fuzzy-based potential function employing optimal routing parameters. The optimal routing variables, such as routing probability and the transmission rate are obtained using a discrete optimisation problem. To deal with the disturbances and uncertainties in the physical system, an adaptive second-order sliding mode control(SMC) scheme has been proposed for the relative motion control of the networks of robots, where the disturbances are estimated using a novel disturbance observer and the controller parameters are updated online using an adaptive tuning algorithm derived based on Lyapunov theory. The robustness of the proposed path planner and the control scheme are validated through simulation as well as through real-time experimentation based on Pioneer P3-DX robots. The comparison results based on conventional SMC and adaptive SMC are also drawn.