A novel robot co-worker system for paint factories without the need of existing robotic infrastructure

This paper presents a human–robot co-working system to be applied to industrial tasks such as the production line of a paint factory. The aim is to optimize the picking task with respect to manual operation in a paint factory. The use of an agile autonomous robot co-worker reduces the time in the picking process of materials, and the reduction of the exposure time to raw materials of the worker improves the human safety. Moreover, the process supervision is also improved thanks to a better traceability of the whole process. The whole system consists of a manufacturing process management system, an autonomous navigation system, and a people detection and tracking system. The localization module does not require the installation of reflectors or visual markers for robot operation, significantly simplifying the system deployment in a factory. The robot is able to respond to changing environmental conditions such as people, moving forklifts or unmapped static obstacles like pallets or boxes. The system is not tied to specific manufacturing orders. It is fully integrated with the manufacturing process management system and it can process all possible orders as long as their components are placed into the warehouse. Real experiments to validate the system have been performed in a paint factory by a real holonomic platform and a worker. The results are promising from the evaluation of performance indicators such as exposure time of the worker to raw materials, automation of the process, robust and safe navigation, and the assessment of the end-user.     

A fuel cell powered autonomous surface vehicle: The Eco-SWAMP project

Autonomous surface vehicles are becoming consolidated robotic tools for marine, coastal and inland surveys. Autonomous surface vehicles are usually equipped with electronic instruments to perform remotely controlled or autonomous geo-morphological, biological, chemical, physical analyses and data collection. Actually, well-established solutions provide battery power but the research focuses on introducing a fuel cell to decrease the environmental impact meanwhile increasing the cruising range. In this paper, the design of the Eco-SWAMP, a fuel cell powered autonomous surface vehicle, is presented starting from its battery-powered version, the SWAMP prototype. The experimental power consumption profile of the SWAMP during four missions is analysed to define the primary energy sources ratings of the Eco-SWAMP. After a commercial choice of primary sources, power management algorithms are designed and compared in MATLAB/Simulink environment by simulation results. The proposed procedure can be easily applied to any autonomous marine vehicle.     

Predictive economic efficiency of combining nuclear power plants with autonomous hydrogen power complex

Currently, the United Energy System (UEC) of Russia is trending in the deficit of peak and half-peak capacity with a simultaneous increase in the number of nuclear power plants (NPPs), which will require the participation of the NPPs in the variable part of the schedule of electrical loads.In addition to the economic need to maintain the high-level utilization rate, there are technological limitations of maneuverability for NPPs.The authors developed an approach to solving this problem by combining with an environmentally friendly energy source – an autonomous hydrogen power complex, which includes thermal batteries and an additional multifunctional low-power steam turbine installation.The developed energy complex can also provide reliable reservation of electricity supply to consumers of their own needs of the nuclear power plant in case of complete blackout of the plant.The feasibility study of the main equipment of the autonomous hydrogen power complex, which is necessary for combining with a two-unit nuclear power plant with WWER-1000, has been evaluated.On the basis of the assessment of the inflation indicators of the Russian economy over the past 11 years, three variants of fuel cost dynamics and tariff rates for electricity (capacity) as well as the size of operating costs, including depreciation deductions to the main equipment, are defined, taking into account the current principles of price formation.The result is a value for accumulated net present value, depending on the ratio of the cost of the half-peak and off-peak electricity at different inflation rates.The positive economic effect of reducing the risk of the core damage accident, replacing the construction of the gas turbine unit as a maneuverable source of electricity in the power grid and increasing the income of the Russian federal budget from the savings of natural gas has been taken into account.The greatest economic efficiency is achieved with maximum projected inflation, which is associated with the maximum rate of discounting and the high rate of growth of electricity tariffs.Reducing the risk of the core damage accident ensures that the proposed approach is competitive in all the inflation options under consideration and the ratio of electricity tariffs.     

自动驾驶4WS车辆路径跟踪最优控制算法仿真

针对自动驾驶车辆高速主动转向工况下传统的控制算法的控制效果容易出现较多的超调量和较长调节时间的问题,提出了基于车辆动力学模型的轨迹预测跟踪主动转向控制算法,并基于轮胎侧偏刚度非线性的特性设计了权系数线性最优二次型(LQR)后轮转角控制算法,通过联合仿真对控制算法效果进行了验证。仿真结果表明:自动驾驶四轮转向车辆在低、高速工况下进行自主换道行驶时,算法控制效果满足汽车操纵稳定性要求,且权系数LQR后轮转向算法比定侧偏刚度的LQR线性控制算法有更优越的操控性能。     

Vulnerable objects detection for autonomous driving: A review

Object detection performed by Autonomous Vehicles (AV)s is a crucial operation that comes ahead of various autonomous driving tasks, such as object tracking, trajectories estimation, and collision avoidance. Dynamic road elements (pedestrians, cyclists, vehicles) impose a greater challenge due to their continuously changing location and behaviour. This paper presents a comprehensive review of the state-of-the-art object detection technologies focusing on both the sensory systems and algorithms used. It begins with a brief introduction on the autonomous driving operations and challenges. Then, different sensory systems employed on existing AVs are elaborated while illustrating their advantages, limitations and applications. Also, sensory systems employed by different research are reviewed. Moreover, due to the significant role Deep Neural Networks (DNN)s are playing in object detection tasks, different DNN-based networks are also highlighted. Afterwards, previous research on dynamic objects detection performed by AVs are reviewed in tabular forms. Finally, a conclusion summarizes the outcomes of the review and suggests future work towards the development of vehicles with higher automation levels.     

Towards the Unified Principles for Level 5 Autonomous Vehicles

The rapid advance of autonomous vehicles (AVs) has motivated new perspectives and potential challenges for existing modes of transportation. Currently, driving assistance systems of Level 3 and below have been widely produced, and several applications of Level 4 systems to specific situations have also been gradually developed. By improving the automation level and vehicle intelligence, these systems can be further advanced towards fully autonomous driving. However, general development concepts for Level 5 AVs remain unclear, and the existing methods employed in the development processes of Levels 0–4 have been mainly based on task-driven function development related to specific scenarios. Therefore, it is difficult to identify the problems encountered by high-level AVs. The essential logical and physical mechanisms of vehicles have hindered further progression towards Level 5 systems. By exploring the physical mechanisms behind high-level autonomous driving systems and analyzing the essence of driving, we put forward a coordinated and balanced framework based on the brain–cerebellum–organ concept through reasoning and deduction. Based on a mixed mode relying on the crow inference and parrot imitation approach, we explore the research paradigm of autonomous learning and prior knowledge to realize the characteristics of self-learning, self-adaptation, and self-transcendence for AVs. From a systematic, unified, and balanced point of view and based on least action principles and unified safety field concepts, we aim to provide a novel research concept and develop an effective approach for the research and development of high-level AVs, specifically at Level 5.     

Capped inhibitor-loaded halloysite nanoclay-based self-healing silica coatings for corrosion protection of mild steel

The effect of polymeric nanocapsule capping in benzotriazole encapsulated into halloysite nanoclay (HNTs) dispersed into hybrid silica coatings was investigated for corrosion protection of mild steel. Optimization of the amount of inhibitor-loaded halloysite nanotubes with and without capping in the coating sol was carried out. The prepared formulations were dip-coated on mild steel substrates using dip-coater and then cured at 130 °C for 1 h. Surface morphology and elemental analysis of the nanoclay were studied using scanning electron microscopy and energy dispersive X-ray spectroscopy. X-ray diffraction and Fourier Transform Infrared spectroscopy analyses were carried out to confirm the encapsulation and capping of the halloysite nanoclay. The anti-corrosion and autonomic-healing properties of bare and coated substrates in 3.5 wt% NaCl solution were studied using electrochemical impedance spectroscopy, potentiodynamic polarization measurements and scanning vibrating electrode technique for varying exposure times. The coatings generated from the capped inhibitor-loaded HNTs dispersed sol-gel matrix was seen to provide higher corrosion resistance when compared to uncapped HNT based silica coatings. Electrochemical studies carried out for capped inhibitor-loaded HNT based coatings have shown an increase in charge transfer resistance to 10⁸ Ω cm² from 10⁶ Ω cm² of uncapped inhibitor-loaded HNT based coatings.     

Autonomous detection and ascent of a step for an electric wheelchair

With the number of individuals using a wheelchair on the rise, the issue of removing architectural barriers, or at least overcoming them, has to be faced to improve independence, inclusiveness, and participation of wheelchair users. Some electric wheelchairs can climb and descend stairs and obstacles, however, the actual operations required to do so safely may be complex and may require an experienced or trained user. To overcome this issue, a method to first detect and classify a step and then autonomously climb it safely is proposed here. The same method is then applied and tested on an actual stair-climbing wheelchair prototype to prove its reliability in different conditions.     

A Lane-Level Road Marking Map Using a Monocular Camera

The essential requirement for precise localization of a self-driving car is a lane-level map which includes road markings (RMs). Obviously, we can build the lane-level map by running a mobile mapping system (MMS) which is equipped with a high-end 3D LiDAR and a number of high-cost sensors. This approach, however, is highly expensive and ineffective since a single high-end MMS must visit every place for mapping. In this paper, a lane-level RM mapping system using a monocular camera is developed. The developed system can be considered as an alternative to expensive high-end MMS. The developed RM map includes the information of road lanes (RLs) and symbolic road markings (SRMs). First, to build a lane-level RM map, the RMs are segmented at pixel level through the deep learning network. The network is named RMNet. The segmented RMs are then gathered to build a lane-level RM map. Second, the lane-level map is improved through loop-closure detection and graph optimization. To train the RMNet and build a lane-level RM map, a new dataset named SeRM set is developed. The set is a large dataset for lane-level RM mapping and it includes a total of 25157 pixel-wise annotated images and 21000 position labeled images. Finally, the proposed lane-level map building method is applied to SeRM set and its validity is demonstrated through experimentation.      

试论广西泥盆系层控铅锌矿

根据成矿作用、矿体产状及矿物组合等特征，可把广西泥盆系铅锌矿床划分为沉积－改造型，沉积－再造型，热液叠加型三种层控铅、锌矿矿床类型。成矿作用为多期、多阶段、多成因的。