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.

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 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 LiFePO 4 (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.

The paper presents the theory of performance sensitivity distribution and a novel robust parameter design technique. In the theory, a Jacobian matrix describes the effect of the component tolerance to the system performance, and the performance distribution is characterized in the variation space by a set of eigenvalues and eigenvectors. Thus, the feasible performance space is depicted as an ellipsoid. The size, shape, and orientation of the ellipsoid describe the quantity as well as quality of the feasible space and, therefore, the performance sensitivity distribution against the tolerance variation. The robustness of a design is evaluated by comparing the fitness between the ellipsoid feasible space and the tolerance space, which is a block, through a set of quantitative and qualitative indexes. The robust design can then be determined. The design approach is demonstrated in a mechanism design problem. Because of the generality of the analysis theory, the method can be used in any design situation as long as the relationship between the performance and design variables can be expressed analytically.

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.

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.

We present reflectance measurements collected from virgin and ocean-harvested plastics. Virgin plastics included high and low density polyethylene (HDPE, LDPE), polypropylene (PP) as well as polystyrene (PS). Ocean-harvested plastics were ropes, sheets, foam, pellets and fragmented items previously trawled from the North Pacific Garbage Patch. Nadir viewing angles and plastic pixel coverage were varied to advance our understanding of how reflectance shape and magnitude can be influenced by these parameters. We also investigated the effect of apparent colour of plastics on the measured reflectance from the ultraviolet (UV - 350 nm), visible, near to shortwave infrared (NIR, SWIR - 2500 nm). Statistical analyses indicated that the spectral reflectance of the plastics was significantly correlated to the percentage pixel coverage. There was no clear relationship between the reflectance observed and the viewing nadir angle but dampened materials seemed to be more isotropic (near-Lambertian) than their dry counterparts. A loss in reflectance was also determined between dry and wet plastics. Location of absorption features was not affected by the apparent colour of objects. In general, ocean-harvested plastics shared more identical absorption features (~960, 1215, 1440, 1732, 1920 nm) and had lower reflectance intensity compared to the virgin plastics (~980 nm). Prospects for satellite retrieval of plastic type and pixel plastic coverage are discussed based on Top-of-Atmosphere (TOA) signal simulated through radiative transfer computation using the documented plastic reflectances. Non-linear relationships between TOA reflectance and plastic coverage were observed depending on wavelength and plastic type. Most of the plastics analysed impact significantly the TOA signal but two plastic types did not produce strong signal at TOA (hard fragments, LDPE). Nevertheless, all plastic types produced detectable signals when observations were simulated within the sunglint direction. The measurements collected in this study are an extension to available high quality spectral reference libraries and can support further research in developing remote sensing algorithms for marine litter.

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.

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.

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.

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.

The development and deployment of robotic technologies can have an important role in efforts to achieve the United Nations’ (UN) Sustainable Development Goals (SDGs)—with both enabling and inhibiting impacts. During a workshop at the 2021 IEEE/Robotics Society of Japan International Conference on Intelligent Robots and Systems (IROS 2021), experts from various disciplines analyzed the role of robotics in achieving the SDGs. This article provides a summary of the most important outcomes of the workshop. During the workshop panels, the variety of roles that robots can play in enabling the SDGs was underlined. The panelists discussed the challenges to the adoption of robots and to their deployment at their full potential. The probable undesirable effects of robots were also considered, and the panelists suggested approaches to correctly design SDG-relevant robotic solutions. Governance frameworks were also discussed, with respect to their contents as well as the challenges to build them. The role of military funding was briefly analyzed. Finally, several proposals for actions and policies were made. The contents of the workshop, including contributing papers and videos from the panelists, as well as additional information about future initiatives regarding robotics and the SDGs, are available atwww.sustainablerobotics.org.

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.

This study considers push and pull strategies to control multistage production systems with random processing times. Such systems are important as they mirror the level of complexity often encountered in practice. We start with definitions of push and pull systems, and develop a framework to compare multistage production systems based upon work-in-process (WIP) and throughput (TP) tradeoff. Surprisingly, we find that often push out performs pull, i.e. push systems accumulate less WIP than pull systems, while maintaining higher PT Concerning pull systems we find that WIP linearly increases in the number of stages and that WIP is not affected by variation in processing time. Concerning push systems we find that the release of material into the system in deterministic time intervals greatly improves performance.

This paper investigates efficiency gains achieved using an 800 V DC bus and wideband gap silicon carbide (SiC) semiconductors for a light-duty electric vehicle (EV), rather than an insulated-gate bipolar transistor (IGBT) inverter with a 400 V bus as is commonly used for EVs. Analytical inverter loss models with 600 V and 1200 V IGBTs, and 1200 V hybrid SiC and 1200 V All-SiC semiconductors are incorporated into a Chevrolet Bolt EV model and simulated over standard drive cycles. Battery pack voltage variations throughout the drive cycles, as well as variations in junction temperature, resulted in 16 to 27 % increased loss compared to fixed voltage and temperature assumptions. To validate the models, experimental testing was performed on a 1200 V IGBT inverter and a 1200 V SiC inverter both powering 160+ kW rated traction machines. Experimentally measured loss was typically within 100 W of the model, demonstrating its accuracy. Going from a 400 V to an 800 V DC bus with IGBTs, EV range was modeled to increase 1.2 %, while an 800 V bus and all SiC inverter results in a range increase of 5.0%. An empirical loss model fitted to measured inverter data shows the analytical model estimates range within 6 km.

In this paper we consider the minimum time population transfer problem for a two level quantum system driven by two external fields with bounded amplitude. The controls are modeled as real functions and we do not use the Rotating Wave Approximation. After projection on the Bloch sphere, we treat the time-optimal control problem with techniques of optimal synthesis on 2D manifolds. Based on the Pontryagin Maximum Principle, we characterize a restricted set of candidate optimal trajectories. Properties on this set, crucial for complete optimal synthesis, are illustrated by numerical simulations. Furthermore, when the two controls have the same bound and this bound is small with respect to the difference of the two energy levels, we get a complete optimal synthesis up to a small neighborhood of the antipodal point of the initial condition.