General Motors (Canada)

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios.

A nonlinear model predictive control (NMPC) method has been presented as the energy management strategy of a battery-supercapacitor (SC) hybrid energy storage system (H-ESS) in a Toyota Rav4EV. For the first time, the NMPC has been shown to be real-time implementable for these fast systems. The performance of the proposed controller has been demonstrated against a linear model predictive control (LMPC) and a rule-based control (RBC) strategy. The NMPC shows to outperform the RBC even with no prior knowledge of the future trip available. The NMPC also shows performance improvement over the LMPC by compensating for the error accompanied by linearization in LMPCs. Hardware-in-the-loop (HiL) testing has been performed to demonstrate the NMPC capability for real-time implementation in a battery-SC H-ESS. Upon carefully choosing the prediction horizon and control horizon size, as well as the maximum number of iterations, the turn-around time for the control update is shown to fall far below the necessary sampling time of 10 ms in vehicle control.

This paper summarizes the results of theoretical and experimental studies of tool temperatures in interrupted cutting. In the theoretical study, the temperature in a semi-infinite rectangular corner heated by a time-varying heat flux with various spatial distributions is used to investigate the general nature of the tool temperature distribution. The results of this analysis are compared with infrared and tool-chip thermocouple cutting temperature measurements from interrupted end turning tests on 2024 aluminum and gray cast iron at speeds up to 18 m/s. The results show that temperatures are generally lower in interrupted cutting than in continuous cutting under the same conditions. Temperatures depend primarily on the length of cutting cycles and secondarily on the length of cooling intervals between cycles. For short cutting cycles the peak and average surface temperatures are relatively low, but they increase rapidly as the cutting cycle is lengthened and approach steady-state values for long cycles. Temperatures increase for very short cooling intervals, since in this case heat does not disperse between heating cycles, but for moderate and large values varying the cooling interval has little effect on temperatures. The theoretical analysis reproduces the qualitative trends but underestimates temperatures for short cutting cycles. The accuracy of the analysis could be improved by using a transient model to calculate the amount of heat entering the tool from the tool-chip contact.

The outbreak of the novel coronavirus and its disease COVID-19 presents an unprecedented challenge for humanity. Intelligent systems and robotics particularly are helping the fight against COVID-19 several ways. Potential technology-driven solutions in this accelerating pandemic include, but are not limited to, early detection and diagnosis, assistive robots, indoor and outdoor disinfection robots, public awareness and patrolling, contactless last-mile delivery services, micro-and nano-robotics and laboratory automation. This article sheds light on the roles robotics and automation can play in fighting this disastrous pandemic and highlights a number of potential applications to transform this challenge into opportunities. The article also highlights the ethical implications of robotics and intelligent systems during the emergency side and in the post-pandemic world.

The variable solid-state diffusivity (VSSD) and the resistive-reactant (RR) models that focus on different physical phenomena are used to investigate the solid-state transport (bulk effects) and electronic conductivity (surface effects) of LiFePO4 (LFP). For the first time, the models are effectively validated against experimental galvanostatic discharge data over a full range of applied currents. To achieve a reasonable degree of accuracy, particle-level parameters are estimated by fitting to experimental data obtained under low-rate discharge conditions, whereas electrode-level properties are derived based on high-rate conditions. Particle size distribution turns out to play a pivotal role in determining the rate capability of the electrode determined by the VSSD and a revised version of the RR model. Based on the full-range comparative study, both the resistive-reactant effect and bulk-related rate limitations prove to contribute significantly to the electrode polarization, especially at high C-rate. The resistive-reactant effect is expected to increase in an electrode made of smaller LFP particles.

This pilot study compared a Nintendo Wii intervention to single-joint resistance training for the upper limb in children ages 7 to 12 with spastic hemiplegic cerebral palsy (CP). Children were randomized to Wii training (n= 3), or resistance training (n= 3) and trained at home for 6 weeks. Pre, post and 4-week follow-up measures were collected. Outcome measures were the Melbourne Assessment (MA2), and ABILHAND-Kids, and grip strength. Compliance, motivation and feasibility of each intervention was explored using daily logbook responses and questionnaires. Descriptive statistics were used. Three children improved in the MA2, two of which were in the Wii training group. Improvements in the ABILHAND-Kids were minimal for all participants. Grip strength improvements were observed in 3 participants, two of which were in the resistance training group. The Wii training group reported higher compliance and more consistently positive responses to motivation and feasibility questions. Therefore, Wii training may be an effective home-based rehabilitation strategy, and is worth exploring in a larger trial. Implications of Wii training in the context of motivation theory are discussed.

Accurate and reliable mathematical modeling is essential for the optimal control and performance analysis of polymer electrolyte membrane fuel cell (PEMFC) systems, which are mainly implemented based on accurate parameter estimation. In this paper, a multi-strategy tuna swarm optimization (MS-TSO) is proposed to estimate the parameters of PEMFC voltage models and compare them with other optimizers such as differential evolution, the whale optimization approach, the salp swarm algorithm, particle swarm optimization, Harris hawk optimization and the slime mould algorithm. In the optimizing routine, the unidentified factors of the PEMFCs are used as the decision variables, which are optimized to minimize the sum of square errors between the estimated and measured data. The optimizers are examined based on three PEMFC datasets including BCS500W, NedStackPS6 and harizon500W as well as a set of experimental data which are measured using the Greenlight G20 platform with a 25 cm2 single cell at 353 K. It is confirmed that MS-TSO gives better performance in terms of convergence speed and accuracy than the competing algorithms. Furthermore, the results achieved by MS-TSO are compared with other reported approaches in the literature. The advantages of MS-TSO in ascertaining the optimum factors of various PEMFCs have been comprehensively demonstrated.

The paper introduces a new physical approach to account for the effect of reduced air density on the flashover voltage and critical leakage current of polluted high voltage insulators. The analysis starts by updating the mathematical model, previously established, of power frequency flashover of polluted insulators at normal atmospheric pressure. It then proceeds to introduce the effect of ambient pressure on the physical parameters of the dielectric recovery equation. The effect of reduced pressure on the arc boundary radius is investigated. The combined effect of humidity and reduced air density on the dielectric strength at ambient temperature is also accounted for. The above analysis results in a new expression for the reignition voltage which includes ambient pressure effects. The analytical findings are then used to investigate the effect of reduced air density on the critical leakage current and flashover voltage of simple-shaped polluted insulators. The effect of more complex profiles is subsequently introduced. The model results are compared with experiments and the agreement established is quite satisfactory. Finally simple practical altitude correction factors for polluted insulators are proposed.

Robotics has the power to help our society in managing many current and foreseeable challenges, and contribute to a responsible future, as formally structured in the United Nations Sustainable Development Goals (SDGs) initiative. Prior work has already investigated the impact of Artificial Intelligence (AI) on the SDGs, using a systematic consensus-based expert elicitation process. However, the existing literature has not focused on the intricacies of robotics and the unique dynamics this domain has regarding the SDGs. In this vein, this work adapts an established approach, to focus on and dive deeper, into the field of robotics and social responsibility. We present a multidisciplinary analysis of both the enabling and disabling roles of robotics, in achieving the SDG-presented, major economic, social and environmental priorities. The United Nation's 17 SDG and the 169 Targets, were individually examined within the context of state-of-the-art robotics already documented in scientific literature. The significance and the quality-of-evidence of enabling/inhibiting impacts, were assessed by an international panel of experts, to quantify the positive or negative effect of the applied robotic systems. Results from this study indicate that robotics has the potential to enable 46 % of the Targets, particularly for the industry and environment-related SDGs, forecasting a huge impact on our production systems and thus on our entire society. Inversely, robotics could inhibit 19 % of the SDG Targets, mainly through exacerbation of inequalities and tensions in the SDGs. The objective of this paper is to assess and grade the current impact of the robotics megatrend on the SDGs, provide comparable data, and encourage the robotics community, to work on these targets, in a unified way and eventually improve the quality of the related outcomes.

Multiple mobile robots in formation are often required to dock to each other to overcome the limitations, such as battery failure, transportation capacity, and maneuverability on rough terrains; however, it is challenging to design a single controller that navigates the robots to dock to each other, maintains the other robots in formation, and is applicable to both docked and nondocked robots, while it is also robust to uncertainties and disturbances. This article proposes a novel robust subsumption architecture for nonholonomic mobile robots in formation with docking capability. In addition to docking, the robots, i.e., all the nondocked robots and the front-docked robots, maintain a formation that can also be switched automatically to other configurations when necessary and avoid collisions with other robots and dynamic obstacles. The proposed subsumption control architecture takes into account each follower's desired goal as well as its docking condition to synthesize a control law as a velocity control signal that is then used to determine the robust input torque for each follower using the robots' dynamics. The Lyapunov stability of the controller is also proved. We also develop strategies for efficient centralized motion planning of the followers to achieve various goals, e.g., formation keeping/switching, docking, and collision avoidance. The effectiveness of our proposed methodology was verified in simulations as well as implementations on a virtual robot environment. Note to Practitioners - Multiple mobile robots, especially when operating as a formation, are able to perform tasks that are beyond the capabilities of individual robots. Existing formation control approaches neglect some realistic limitations of mobile robots, such as battery failure, limited transportation capacity, and maneuverability, to name a few. This article was motivated by these realistic limitations of mobile robots when operating in formation, and it suggests a new approach for navigation of such robots by docking some (or all) of these robots to each other and pursue a variety of goals. The goal includes autonomous docking, formation keeping/switching, and collision avoidance in dynamic environments. We include robot dynamics and system uncertainties in our algorithm and provide a robust control methodology. Therefore, the developed methodologies in this article can be adopted in real applications that require robots to be supplied with sufficient battery or having a large payload capacity, e.g., agricultural robotics.

Seven estimators for the scale (δ) and shape (ß) parameters and percentiles of the Weibull distribution were compared by Monte Carlo methods. The evaluated estimators include the maximum likelihood estimator (MLE), linear estimators, least squares estimators, and a moment estimator. The performance of these estimators with respect to mean square error was studied in complete and Type II censored samples of sizes 10 and 25. No estimator outperformed all the others in all situations. One estimator, however, consistently performed worse than one of the others. The following summarizes the results. 1. The MLEs performed very well in the simulation study for all parameters when estimating from complete samples of size 25. For smaller samples and/or censored samples, they still performed very well as estimators of 1/ß and the upper percentiles of the distribution. 2. The best linear unbiased estimator (BLUE) was generally better than the best linear invariant estimator (BLIE) for estimating ß and the 10-th percentile. The BLIE was generally better than the BLUE for estimating 1/ß, δ, and the 90-th percentile. The overall performance of both of these linear estimators was similar to that of the MLEs. A choice between the linear estimators and the MLEs for a specific application can be based on such considerations as the availability of tables and ease of computation. No overriding superiority of the linear estimators over the maximum likelihood estimator was demonstrated, and vice versa. 3.

In many life tests, the initial censoring of items results in withdrawing a portion of the survivors while some remain on test until failure or until a subsequent stage of censoring. If the censoring is progressive through several stages, the resulting sample consists of censored items intermingled with failed ones. The maximum likelihood estimator (MLE) and a least squares median ranks estimator (LSMRE) apply in this situation. Using Monte Carlo methods, the statistical properties of these estimators for the parameters and percentiles of the 2-parameter Weibull distribution are determined. The results are: 1. The MLE performs well in estimating the parameters and percentiles for complete samples of moderate to large size (25 and 100). For small sample size (10) and/or censored samples it performs relatively well in estimating the scale parameter and the upper percentiles of this distribution. 2. The LSMRE was generally less reliable than the MLE in estimating the scale parameter and the upper percentiles of the distribution. It performed relatively well when estimating the shape parameter and the lower percentiles.

Esta pesquisa teve como objetivo a apreensão e interpretação de sentidos e significados que alunos obesos atribuem às práticas corporais de saúde realizadas no Projeto de Exercício Físico Adaptado para Obesos da Universidade do Estado do Rio de Janeiro, no sentido de compreender os motivos que levam esses alunos a procurarem e a permanecerem no Projeto de Extensão. Trata-se de um estudo de caso de natureza socioantropológica situado no campo da Saúde Coletiva, cuja estratégia metodológica foi a articulação entre observação etnográfica (participante), entrevistas informais e formais (gravadas). Concluímos que atividades coletivas de indivíduos que partilham um mesmo estigma podem resultar em efeitos positivos para o grupo, na medida em que o encontro com os pares permite que vivenciem experiências construtoras de valores coletivos e cordiais, produtores de sentidos para além do desejo de emagrecimento.

Recent advances in transportation have enabled lane-specific measurements and lane-specific control. This paper makes use of such data to promote energy efficiency of vehicles. In particular, a Multi-Lane Adaptive Cruise Controller (MLACC) is designed which determines the optimal velocity and lane-to-drive in real-time. This cruise controller solves lane-specific optimization problems to compute an instantaneous trip cost for each lane and selects the lane that poses the lowest cost. The optimization tasks incorporate future route data and encompass multiple objectives including safety, energy efficiency and desired velocity tracking. Therefore, they can be treated as distinct Nonlinear Model Predictive Control (NMPC) problems that have to be solved altogether in each sampling time. To handle the computational load of solving multiple NMPCs in real-time, an integration of Newton and Generalized Minimal Residual numerical methods is employed. The proposed MLACC is implemented for a 2013 Toyota Prius and a wide range of simulation studies are performed to examine the controller. Specifically, hardware-in-the-loop experiments are utilized to evaluate the real-time implementability of the controller. In addition, extensive model-in-the-loop simulations are carried out and the results are compared with driver-in-the-loop experiments. Simulation results indicated that speed profiles and lane changes suggested by MLACC yield up to 27% improvement in energy consumption compared to human drivers.

A two-dimensional numerical solution has been obtained of the effect of a venetian blind on the conjugate heat transfer at an indoor window glazing. A solution has been obtained to the coupled laminar free convection and radiation heat transfer problem, including conduction along the blind slats. The local convective Nusselt number distributions were found to compare well with published experimental data for an aluminum blind. Also, there was good qualitative agreement with temperature and flow field visualization photographs. The results show that, over a wide range of Rayleigh numbers, an aluminum venetian blind can have a strong effect on the heat transfer rate from the indoor window glazing. Depending upon the specific conditions, the average convective heat transfer rate can either increase or decrease. However, for all cases studied, the blind was found to substantially reduce the radiative heat transfer rate from the window, even when the slats were fully open.

Sliding-mode control is applied to ABS wheel-slip control due to the nonlinearity of vehicle traction system. Vehicle traction dynamics are reviewed. Sliding-mode application to ABS wheel-slip control is discussed. An experimental setup in a brake test cell with an electric dynamometer is used to emulate the ABS braking of a half car. Test results for the sliding-mode ABS wheel-slip control indicate that sliding-mode ABS control provides tight wheel-slip control on both dry and ice-like surfaces.

<div class="htmlview paragraph">This new approach for improving fuel economy uses computer programs to optimize and tailor an engine's fuel, EGR and spark control in the laboratory. New forms of engine and vehicle test data are used as inputs. This includes a simple simulation of the catalytic converter. The emission engineer is in control of the process via a special interactive program at a computer terminal. He combines his know-how with the computer programs to create a feasible engine control calibration for vehicle evaluation. The programs can also be used to study tradeoffs of optimized fuel economy vs emissions for a vehicle. Although presently limited to warmed up operation, the optimization procedure has proved to be valid and useful.</div>

In this paper, a new approach for lane change and double-lane change planning and following for autonomous driving is proposed. Herein, we introduce a novel technique, based on exponential functions, to generate online and feasible lane change maneuvers; these maneuvers satisfy the constraints on the maximum allowable curvature a given vehicle can handle. In addition, a simultaneous local path planning and path-following control framework is adapted. The framework utilizes a multi-threading architecture to run the local planner module concurrently with the control module. The planning module generates parametric reference paths based on the proposed lane change and double-lane change maneuvers. The control module is based on a Model Predictive Path-following Control (MPFC) scheme, which achieves the path following objective while satisfying vehicle’s state and control limits. To validate the proposed framework, several real-time simulation scenarios are designed and tested on CARLA soft real-time vehicle simulator. The results show the effectiveness of the proposed framework in generating and smoothly following lane change maneuvers.

Diamond-like carbon (DLC) coatings are promising candidates as tool coatings for dry machining of aluminum alloys as aluminum has a lower tendency to adhere to the DLC surface when compared to other hard coatings in ambient conditions. This study investigated the tribological behavior of non-hydrogenated DLC coatings against a 319 Al alloy in various environments including ambient air (47% RH), vacuum (6.65 × 10 − 4 Pa), inert gases (Ar, He, and N 2 ) and a mixture of 60% He-40% H 2 . Unlike the results from the other test conditions, significantly low coefficient of friction (COF) values, ranging between 0.01–0.02, were observed in the 60% He-40% H 2 mixture after a brief period with a high COF of 0.70. The formation of a carbonaceous tribolayer on the counterface and the passivation of the sliding surfaces by the chemisorption of hydrogen were suggested as mechanisms that might have been responsible for the very low COF behavior of the non-hydrogenated DLC coatings in the 60% He-40% H 2 mixture.

Convolution of a discrete Walsh function with a rectangular pulse simplifies the derivation of an expression for the Fourier transform of a Walsh function. The nonrecursive transform equation that is developed is a function of the bits of the Gray code number for the order of the Walsh function.