Adaptive sliding mode control for uncertain wheel mobile robot

In this paper a simple adaptive sliding mode controller is proposed for tracking control of the wheel mobile robot (WMR) systems. The WMR are complicated systems with kinematic and dynamic model so the error dynamic model is built to simplify the mathematical model. The sliding mode control then is designed for this error model with the adaptive law to compensate for the mismatched. The proposed control scheme in this work contains only one control loop so it is simple in both implementation and mathematical calculation. Moreover, the requirement of upper bounds of disturbance that is popular in the sliding mode control is cancelled, so it is convenient for real world applications. Finally, the effectiveness of the presented algorithm is verified through mathematical proof and simulations. The comparison with the existing work is also executed to evaluate the correction of the introduced adaptive sliding mode controller. Thoroughly, the settling time, the peak value, the integral square error of the proposed control scheme reduced about 50% in comparison with the compared disturbance observer based sliding mode control

Vision-based pavement marking detection and condition assessment : a case study

Pavement markings constitute an effective way of conveying regulations and guidance to drivers. They constitute the most fundamental way to communicate with road users, thus, greatly contributing to ensuring safety and order on roads. However, due to the increasingly extensive traffic demand, pavement markings are subject to a series of deterioration issues (e.g., wear and tear). Markings in poor condition typically manifest as being blurred or even missing in certain places. The need for proper maintenance strategies on roadway markings, such as repainting, can only be determined based on a comprehensive understanding of their as-is worn condition. Given the fact that an efficient, automated and accurate approach to collect such condition information is lacking in practice, this study proposes a vision-based framework for pavement marking detection and condition assessment. A hybrid feature detector and a threshold-based method were used for line marking identification and classification. For each identified line marking, its worn/blurred severity level was then quantified in terms of worn percentage at a pixel level. The damage estimation results were compared to manual measurements for evaluation, indicating that the proposed method is capable of providing indicative knowledge about the as-is condition of pavement markings. This paper demonstrates the promising potential of computer vision in the infrastructure sector, in terms of implementing a wider range of managerial operations for roadway management