Navigating an auto guided vehicle using rotary encoders and proportional controller

Auto Guided Vehicle (AGV) is commonly used in industry to reduce labour cost and to improve the productivity. A few programmable devices are combined in an AGV to optimize the usage of time and energy. AGV is widely used to transport goods and materials from one place to another place. For the first generation of AGV was used the track to guide the AGV but it was not flexible enough. This study investigates an alternative to control an AGV using two rotary encoders and proportional controller. Arduino Mega 2560 was used as a microcontroller to receive and process the signals from the rotary encoders. Logic controller and proportional controller were implemented to control the AGV, respectively. The coefficient of proportional controller was optimized to improve the performance of the AGV during navigation process. Findings show that AGV with the proportional controller with coefficient 1.5 achieved the best performance during the navigation process

Real Hypersurfaces of Type A in Complex Two-Plane Grassmannians Related to The Reeb Vector Field

Y. J. Suh and H. Lee (Bull. Korean. Math. Soc. 47, 551-561 (2010)) characterized real hypersurfaces $M$ of type $B$ by the invariance of vector bundle $JTM^\perp$ under the shape operator and the orthogonality of $JTM^\perp$ and $\mathcal {J}TM^\perp$, where $TM^\perp$, $J$ and $\mathcal J$ are the normal bundle of $M$, K\"ahler structure and Quaternionic K\"ahler structure of $G_2({\mathbb{C}}^{m+2})$ respectively. In this paper, we characterize real hypersurfaces $M$ of type A by the invariance of the vector bundle $JTM^\perp$ under the shape operator with the Reeb vector field in $\mathcal {J}TM^\perp$.Comment: 8 page

Acute effects of inspiratory pressure threshold loading upon airway resistance in people with asthma

This is the post-print version of the final paper published in Respiratory Physiology & Neurobiology. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2009 Elsevier B.V.Large inspiratory pressures may impart stretch to airway smooth muscle and modify the response to deep inspiration (DI) in asthmatics. Respiratory system resistance (Rrs) was assessed in response to 5 inspiratory manoeuvres using the forced oscillation technique: (a) single unloaded DI; (b) single DI at 25 cmH2O; (c) single DI at 50% maximum inspiratory mouth pressure [MIP]; (d) 30 DIs at 50% MIP; and (e) 30 DIs at 50% MIP with maintenance of normocapnia. Rrs increased after the unloaded DI and the DI at 25 cmH2O but not after a DI at 50% MIP (3.6 ± 1.6 hPa L s−1 vs. 3.6 ± 1.5 hPa L s−1; p = 0.95), 30 DIs at 50% MIP (3.9 ± 1.5 hPa L s−1 vs. 4.2 ± 2.0 hPa L s−1; p = 0.16) or 30 DIs at 50% MIP under normocapnic conditions (3.9 ± 1.5 hPa L s−1 vs. 3.9 ± 1.5 hPa L s−1; p = 0.55). Increases in Rrs in response to DI were attenuated after single and multiple loaded breaths at 50% MIP

Predictive positioning and quality of service ridesharing for campus mobility on demand systems

Autonomous Mobility On Demand (MOD) systems can utilize fleet management strategies in order to provide a high customer quality of service (QoS). Previous works on autonomous MOD systems have developed methods for rebalancing single capacity vehicles, where QoS is maintained through large fleet sizing. This work focuses on MOD systems utilizing a small number of vehicles, such as those found on a campus, where additional vehicles cannot be introduced as demand for rides increases. A predictive positioning method is presented for improving customer QoS by identifying key locations to position the fleet in order to minimize expected customer wait time. Ridesharing is introduced as a means for improving customer QoS as arrival rates increase. However, with ridesharing perceived QoS is dependent on an often unknown customer preference. To address this challenge, a customer ratings model, which learns customer preference from a 5-star rating, is developed and incorporated directly into a ridesharing algorithm. The predictive positioning and ridesharing methods are applied to simulation of a real-world campus MOD system. A combined predictive positioning and ridesharing approach is shown to reduce customer service times by up to 29%. and the customer ratings model is shown to provide the best overall MOD fleet management performance over a range of customer preferences.Ford Motor CompanyFord-MIT Allianc

Demand estimation and chance-constrained fleet management for ride hailing

In autonomous Mobility on Demand (MOD) systems, customers request rides from a fleet of shared vehicles that can be automatically positioned in response to customer demand. Recent approaches to MOD systems have focused on environments where customers can only request rides through an app or by waiting at a station. This paper develops MOD fleet management approaches for ride hailing, where customers may instead request rides simply by hailing a passing vehicle, an approach of particular importance for campus MOD systems. The challenge for ride hailing is that customer demand is not explicitly provided as it would be with an app, but rather customers are only served if a vehicle happens to be located at the arrival location. This work focuses on maximizing the number of served hailing customers in an MOD system by learning and utilizing customer demand. A Bayesian framework is used to define a novel customer demand model which incorporates observed pedestrian traffic to estimate customer arrival locations with a quantification of uncertainty. An exploration planner is proposed which routes MOD vehicles in order to reduce arrival rate uncertainty. A robust ride hailing fleet management planner is proposed which routes vehicles under the presence of uncertainty using a chance-constrained formulation. Simulation of a real-world MOD system on MIT's campus demonstrates the effectiveness of the planners. The customer demand model and exploration planner are demonstrated to reduce estimation error over time and the ride hailing planner is shown to improve the fraction of served customers in the system by 73% over a baseline exploration approach.Ford-MIT AllianceFord Motor Compan

Kidney and Kidney Tumour Segmentation in CT Images

Automatic segmentation of kidney and kidney tumour in Computed Tomography (CT) images is essential, as it uses less time as compared to the current gold standard of manual segmentation. However, many hospitals are still reliant on manual study and segmentation of CT images by medical practitioners because of its higher accuracy. Thus, this study focuses on the development of an approach for automatic kidney and kidney tumour segmentation in contrast-enhanced CT images. A method based on Convolutional Neural Network (CNN) was proposed, where a 3D U-Net segmentation model was developed and trained to delineate the kidney and kidney tumour from CT scans. Each CT image was pre-processed before inputting to the CNN, and the effect of down-sampled and patch-wise input images on the model performance was analysed. The proposed method was evaluated on the publicly available 2021 Kidney and Kidney Tumour Segmentation Challenge (KiTS21) dataset. The method with the best performing model recorded an average training Dice score of 0.6129, with the kidney and kidney tumour Dice scores of 0.7923 and 0.4344, respectively. For testing, the model obtained a kidney Dice score of 0.8034, and a kidney tumour Dice score of 0.4713, with an average Dice score of 0.6374