Neural Networks for Fast Optimisation in Model Predictive Control: A Review

Model Predictive Control (MPC) is an optimal control algorithm with strong stability and robustness guarantees. Despite its popularity in robotics and industrial applications, the main challenge in deploying MPC is its high computation cost, stemming from the need to solve an optimisation problem at each control interval. There are several methods to reduce this cost. This survey focusses on approaches where a neural network is used to approximate an existing controller. Herein, relevant and unique neural approximation methods for linear, nonlinear, and robust MPC are presented and compared. Comparisons are based on the theoretical guarantees that are preserved, the factor by which the original controller is sped up, and the size of problem that a framework is applicable to. Research contributions include: a taxonomy that organises existing knowledge, a summary of literary gaps, discussion on promising research directions, and simple guidelines for choosing an approximation framework. The main conclusions are that (1) new benchmarking tools are needed to help prove the generalisability and scalability of approximation frameworks, (2) future breakthroughs most likely lie in the development of ties between control and learning, and (3) the potential and applicability of recently developed neural architectures and tools remains unexplored in this field.Comment: 34 pages, 6 figures 3 tables. Submitted to ACM Computing Survey

Sphingomyelinase D Activity in Model Membranes: Structural Effects of in situ Generation of Ceramide-1-Phosphate

The toxicity of Loxosceles spider venom has been attributed to a rare enzyme, sphingomyelinase D, which transforms sphingomyelin to ceramide-1-phosphate. The bases of its inflammatory and dermonecrotic activity, however, remain unclear. In this work the effects of ceramide-1-phosphate on model membranes were studied both by in situ generation of this lipid using a recombinant sphingomyelinase D from the spider Loxosceles laeta and by pre-mixing it with sphingomyelin and cholesterol. The systems of choice were large unilamellar vesicles for bulk studies (enzyme kinetics, fluorescence spectroscopy and dynamic light scattering) and giant unilamellar vesicles for fluorescence microscopy examination using a variety of fluorescent probes. The influence of membrane lateral structure on the kinetics of enzyme activity and the consequences of enzyme activity on the structure of target membranes containing sphingomyelin were examined. The findings indicate that: 1) ceramide-1-phosphate (particularly lauroyl ceramide-1-phosphate) can be incorporated into sphingomyelin bilayers in a concentration-dependent manner and generates coexistence of liquid disordered/solid ordered domains, 2) the activity of sphingomyelinase D is clearly influenced by the supramolecular organization of its substrate in membranes and, 3) in situ ceramide-1-phosphate generation by enzymatic activity profoundly alters the lateral structure and morphology of the target membranes

The Detection of Pedestrian Crossing Behaviour

The pedestrian is regarded to be one of the most vulnerable road users. Non-verbal communication between drivers and pedestrians seems to play an important role in the mitigation of collisions. The emergence of autonomous vehicles in traffic in the near future presses the need to investigate objective measures related to pedestrian crossing behaviour and the efficacy of communication devices on autonomous vehicles that might replace the nonverbal signals of the human driver. In order to objectively investigate the efficacy of communication devices on autonomous vehicles, 24 participants in this study were immersed in a virtual reality environment, via the use of an Oculus Rift and an Xsens Link motion tracking device. In this virtual reality environment, participants were presented with 18 series of autonomous vehicles. Each series represented one unique combination of independent variables and contained a total of five vehicles. The vehicles were either equipped with a Text display or Frontal Braking Lights that indicated the yielding intentions of the vehicle, or were without any external interface. Furthermore, the inter-vehicular distance between the second and the third vehicle in the series varied between 20, 30 or 40 meters. The participants were instructed to cross the road onto the zebra crossing in the virtual environment when they deemed it was safe to do so. The experiment was designed in such a way that the only crossing opportunity for the participants was between the second and third vehicle when the third vehicles yielded. The road crossing decision of the participants, operationalized by the objective measure of their forward gait velocity, was earlier in time when there was either a Text display or Frontal Braking Lights present on the third vehicle in the series, when the inter-vehicular distance between the second and third vehicle was 20 meters and the third and subsequent vehicles yielded. Congruently, the self-reported ability of participants to predict the behaviour of the oncoming vehicles was significantly better when the third vehicle had a Text display compared to when there was no external interface. However, no significant difference in self-reported ability to predict the behaviour of the oncoming vehicles was found for the Frontal Braking Lights. Furthermore, the forward gait velocity was significantly greater in the presence of a Text display compared to when there was no external interface present for the condition in which the inter-vehicular distance between the second and third vehicle was 30 meters and the third and subsequent vehicles yielded. This work shows that besides the current standard of subjective validation by pedestrians of external human-machine interfaces on autonomous vehicles these interfaces can objectively be validated through the recording and differentiation of body motions.Biomedical Engineerin

Data for meta-analysis and systematic review from tactile-mediated vection research

Data that has been generated and analysed for the publication &#34;A Systematic Review and Meta-Analysis on The Use of Tactile Stimulation in Vection Research&#34;. Data from the moment the manuscript was submitted for publication and its subsequent revisions has been included.</p

A virtual reality study investigating the train illusion

The feeling of self-movement that occurs in the absence of physical motion is often referred to as vection, which is commonly exemplified using the train illusion analogy (TIA). Limited research exists on whether the TIA accurately exemplifies the experience of vection in virtual environments (VEs). Few studies complemented their vection research with participants' qualitative feedback or by recording physiological responses, and most studies used stimuli that contextually differed from the TIA. We investigated whether vection is experienced differently in a VE replicating the TIA compared to a VE depicting optic flow by recording subjective and physiological responses. Additionally, we explored participants' experience through an open question survey. We expected the TIA environment to induce enhanced vection compared to the optic flow environment. Twenty-nine participants were visually and audibly immersed in VEs that either depicted optic flow or replicated the TIA. Results showed optic flow elicited more compelling vection than the TIA environment and no consistent physiological correlates to vection were identified. The post-experiment survey revealed discrepancies between participants' quantitative and qualitative feedback. Although the dynamic content may outweigh the ecological relevance of the stimuli, it was concluded that more qualitative research is needed to understand participants' vection experience in VEs

How should external human-machine interfaces behave? Examining the effects of colour, position, message, activation distance, vehicle yielding, and visual distraction among 1,434 participants

External human-machine interfaces (eHMIs) may be useful for communicating the intention of an automated vehicle (AV) to a pedestrian, but it is unclear which eHMI design is most effective. In a crowdsourced experiment, we examined the effects of (1) colour (red, green, cyan), (2) position (roof, bumper, windshield), (3) message (WALK, DON'T WALK, WILL STOP, WON'T STOP, light bar), (4) activation distance (35 or 50 m from the pedestrian), and (5) the presence of visual distraction in the environment, on pedestrians' perceived safety of crossing the road in front of yielding and non-yielding AVs. Participants (N = 1434) had to press a key when they felt safe to cross while watching a random 40 out of 276 videos of an approaching AV with eHMI. Results showed that (1) green and cyan eHMIs led to higher perceived safety of crossing than red eHMIs; no significant difference was found between green and cyan, (2) eHMIs on the bumper and roof were more effective than eHMIs on the windshield, (3) for yielding AVs, perceived safety was higher for WALK compared to WILL STOP, followed by the light bar; for non-yielding AVs, a red bar yielded similar results to red text, (4) for yielding AVs, a red bar caused lower perceived safety when activated early compared to late, whereas green/cyan WALK led to higher perceived safety when activated late compared to early, and (5) distraction had no significant effect. We conclude that people adopt an egocentric perspective, that the windshield is an ineffective position, that the often-recommended colour cyan may have to be avoided, and that eHMI activation distance has intricate effects related to onset saliency.Human-Robot InteractionMedical Instruments & Bio-Inspired Technolog