Transportation Tasks Research Articles

Application of Heat Treatment to Aluminum Alloy Wires and Comparison of Full Alloy AAAC Conductor Manufactured with These Wires and Conventional ACSR Stranded Conductors

Conventional ACSR conductors are formed by stranding aluminum wires over a steel core. The steel core does not contribute to current carrying and is used only for carrying purposes. The steel core has a great role in the shedding of the line weight. With the increase in line weight, longer poles are used at shorter distances, but it cannot be abandoned because it undertakes the transport task. Nowadays, the search for alternatives to the ACSR conventional conductor has accelerated. One of these alternatives is AAAC (Full aluminum alloy conductors). The wires in AAAC are hardened with special alloy production and eliminate the need for a steel core as a carrier in the center. While producing the aluminum overhead line conductor, after the aluminum ingots are melted in the melting furnaces and taken to the resting furnaces, after the addition of alloy elements such as Si, Mg in certain proportions, it is solidified by pouring it into a copper mold through refractory runners, and after the rolling process, 9.5 mm or 12 mm Aluminum Alloy rod (wire rod) semi-finished products are produced. While producing wire in the diameter determined in the standards for the overhead line conductor to be produced, 9.5 mm or 12 mm Aluminum rod (wire rod) is thinned with a certain reduction ratio in the semi-finished drawing process, and aluminum wire of the desired diameter is obtained. In the drawing process, the rod is rolled with a reduction ratio suitable for the machine and the final product to be produced. The wires produced are brought together with the step ratio in the standards to form a conductor. In order to gain the mechanical and electrical properties of the produced aluminum alloy wire, heat is applied at a certain time and temperature in heat treatment furnaces. Considering the heat treatment time and degree applied in this study, the properties before and after the heat treatment will be compared and the effect of the heat treatment process on the wire will be shown with experimental studies. Mechanical and electrical tests of AAAC 774 AL7 conductor made of alloy wires and ACSR Pheasant conductors made of aluminum wires knitted on steel cores will be carried out and the two conductors will be compared considering some parameters in line selection.

Application of algorithmic models of machine learning to the freight transportation process

The results of the analysis of algorithmic models of machine learning application to the freight transportation process are given in this paper. Analysis of existing research allowed discovering a range of advantages in the application of computational intelligence in logistic systems, including increasing the accuracy of forecasting, reduction of transport costs, increasing the efficiency of cargo delivery, risks reduction, and search for key performance factors. In the research process, the main directions of application of algorithmic models of machine learning were determined. They are vehicle routing, choice of cargo type, transportation type and vehicle type; forecasting fuel consumption by vehicles, disruptions in transportation, transport costs, duration of the order fulfillment; evaluation of the rolling stock fleet and the efficiency of carrying out the transport task. Based on the researched publications, the most common algorithmic models of machine learning in freight transportation were identified, and their effectiveness was analyzed. Linear and logistic regression models are simple enough; however, they do not always provide high simulation results. Deep learning models are quite widely applied to all identified areas. Decision tree and random forest models often show the highest simulation performance. Models of k-nearest neighbors and support vectors should be used both in classification tasks, for example, in choosing the type of cargo and type of transportation, and for forecasting the fuel consumption and the duration of the transport process.

Mutli-Robot Cooperative Object Transportation with Guaranteed Safety and Convergence in Planar Obstacle Cluttered Workspaces via Configuration Space Decomposition

In this work, we consider the autonomous object transportation problem employing a team of mobile manipulators within a compact planar workspace with obstacles. As the object is allowed to translate and rotate and each robot is equipped with a manipulator consisting of one or more moving links, the overall system (object and mobile manipulators) should adapt its shape in a flexible way so that it fulfills the transportation task with safety. To this end, we built a sequence of configuration space cells, each of which defines an allowable set of configurations of the object, as well as explicit intervals for each manipulator’s states. Furthermore, appropriately designed under- and over-approximations of the free configuration space are used in an innovative way to guide the configuration space’s exploration without loss of completeness. In addition, we coupled methodologies based on Reference Governors and Prescribed Performance Control with harmonic maps, in order to design a distributed control law for implementing the transitions specified by the high-level planner, which possesses guaranteed invariance and global convergence properties, thus avoiding the requirement for synchronized motion as inherently dictated by the majority of the related works. Furthermore, the proposed low-level control law does not require continuous information exchange between the robots, which rely only on measurements of the object’s configuration and their own states. Finally, a transportation scenario within a complex warehouse workspace demonstrates the proposed approach and verifies its efficiency.

Cooperative Transportation With Mobile Manipulator: A Capability Map-Based Framework for Physical Human–Robot Collaboration

In the cooperative transportation task with a mobile manipulator (MM), the mobile robot and the manipulator must move simultaneously to adapt to the human motion. In addition, the motion of the MM is underconstrained due to redundancy, which makes MM real-time motion planning challenging. In this article, a capability map-based framework was proposed to enable real-time motion planning of the MM. In the motion planner, the dexterity of the MM, the formation of the human–robot system, and obstacle avoidance of the mobile robot are considered to get safe and human-like robot motion. Moreover, to make optimization faster and avoid local minimum, capability map, which represents the manipulability distribution of the MM in its workspace, is queried to determine the seed of the optimization algorithm. The proposed motion planner is self-contained and can be extended to the transportation task with multiple MMs. The effectiveness of this proposed framework is validated both in simulations and real-world experiments.

Improved dusty gas cleaning in open-cast automobile transport

Service life extension of internal combustion engines is an urgent task for open-cast automobile transport. Dust contamination of oil and constructive elements of the internal combustion engine accelerates its wear and tear, resulting in breakage. The authors developed a separation device with sections of double-T elements as a replacement for the first stage cleaning device in open-cast automobile transport. Numerical results show that the proposed separation device provides gas cleaning with high efficiency from fine, medium, and coarse solid particles. The use of this device improved the overall efficiency of gas cleaning and extended the service life of the fine filter, which has a positive effect on the internal combustion engine. Studies have shown that the separation device has an average efficiency of 81.2% with the following parameters: dust particle size 5–20 μm, dust particle density 1000–7000 kg/m3, inlet gas velocity 3–15 m/s.

Adaptive-Constrained Impedance Control for Human-Robot Co-Transportation.

Human-robot co-transportation allows for a human and a robot to perform an object transportation task cooperatively on a shared environment. This range of applications raises a great number of theoretical and practical challenges arising mainly from the unknown human-robot interaction model as well as from the difficulty of accurately model the robot dynamics. In this article, an adaptive impedance controller for human-robot co-transportation is put forward in task space. Vision and force sensing are employed to obtain the human hand position, and to measure the interaction force between the human and the robot. Using the latest developments in nonlinear control theory, we propose a robot end-effector controller to track the motion of the human partner under actuators' input constraints, unknown initial conditions, and unknown robot dynamics. The proposed adaptive impedance control algorithm offers a safe interaction between the human and the robot and achieves a smooth control behavior along the different phases of the co-transportation task. Simulations and experiments are conducted to illustrate the performance of the proposed techniques in a co-transportation task.

Robust control strategy for generalized N-trailer vehicles based on a dual-stage disturbance observer

Articulated vehicles with multiple trailers also called N-Trailers have been widely used for transportation tasks in industrial applications. Solutions for the control problem of N-Trailers have been formulated mostly for vehicles with solely on-axle hitching or off-axle hitching but in field applications such as agriculture, most of the structures used are Generalized N-Trailers (GNT) which have a combination of on- and off-axle hitching. Moreover, most of the solutions in literature were developed and tested for laboratory-scale platforms under ideal indoor conditions. However, in outdoors conditions, the motion performance is commonly degraded by model uncertainties, slipping of wheels, and trailers localization loss product of noisy or inaccurate sensor data. In this context, this paper reports the use of Active Disturbance Rejection Control (ADRC) with a Dual-Stage Disturbance Observer (DS-DO) to improve the backward trajectory-tracking performance of GNT in no ideal conditions, where the DS-DO aims to attenuate the effects of error propagation on the ADRC compensation loop and improve the overall closed-loop performance. The proposed ADRC+DS-DO has been validated in simulation and real experiments showing overall improvements on the controller effort reduction and reduction of up to 57% on the tracking error against a traditional ADRC approach already existent in the literature.

Inverse motion planning method for overhead crane systems with state constraints

AbstractThe trajectory planning problem with state constraints of overhead crane systems is considered in this paper. A new method, that is, an inverse motion planning method, is proposed. On this basis, a new trajectory planner is designed. The payload swing angle trajectory is designed first, and then by substituting it into the dynamic equations, the trolley trajectory is derived. For any transportation tasks, the adjustable parameter of the planner is computed by solving the state constraints such that the system states; that is, the trolley acceleration, trolley speed, and payload swing angle will not exceed their predefined constraints. Several experimental tests are conducted to verify the swing elimination performance of the proposed method.

Bacterial Memetic Algorithm for Asymmetric Capacitated Vehicle-Routing Problem

The vehicle-routing problem (VRP) has many variants, including the most accurate models of real-life transportation tasks, making it one of the most important mathematical problems in the field of logistics. Our goal was to design an algorithm that can race against the most recent solutions for VRP and capacitated VRP (CVRP), while also being applicable to real-life models with simulations of real transports. Our algorithm is a variant of the bacterial memetic algorithm (BMA), which we improve upon with novel operators and better methods for manual parameter optimization. The key to our performance is a balanced mixture of the global search of evolutionary algorithms, local search of 2-OPT variants, and the pseudo-global search of probabilistic construction algorithms. Our algorithm benefits from the advantages of all three methods resulting in fast convergence and avoidance of global minima. This is the first time BMA is applied for VRP, meaning that we had to adapt the method for the new problem. We compare our method with some of the most-used methods for VRP on the ABEFMP 1995 dataset. We provide comparison results with the coronavirus herd immunity optimizer, genetic algorithm, hybridization of genetic algorithm with neighborhood search, firefly algorithm, enhanced firefly algorithm, ant colony optimization, and variable neighborhood search. Our algorithm performed better on all data instances, yielding at least a 30% improvement. We present our best result on the Belgium 2017 dataset for future reference. Finally, we show that our algorithm is capable of handling real-life models. Here we are also illustrating the significance of the different parameters.

Multi-objective scheduling of a single mobile robot based on the grey wolf optimization algorithm

During the last decades, intelligent mobile robots have been recognized as one of the most promising and emerging solutions used for fulfilling material transport demands in intelligent manufacturing systems. One of the most significant characteristics of those demands is their multi-objectivity, where identified objectives might usually conflict. Therefore, obtaining the optimally scheduled robotic-based material transport system that is simultaneously facing several conflicting objectives is a highly challenging task. To address such a challenge, this paper proposes a novel multi-objective Grey Wolf Optimizer (MOGWO) methodology to efficiently schedule material transport systems based on an intelligent single mobile robot. The proposed optimization methodology includes the comprehensive analysis and the mathematical formulation of 13 novel fitness functions combined to form a Pareto front of the multi-objective optimization problem and a novel strategy for optimal exploration of multi-objective search space. Moreover, four metrics, i.e., Generational Distance (GD), Inverted Generational Distance (IGD), Spacing (SP), and Maximum Spread (MS), are employed to quantitively evaluate and compare the effectiveness of the proposed enhanced MOGWO algorithm with three state-of-the-art metaheuristic methods (MOGA, MOAOA, and MOPSO) on 25 benchmark problems. The results achieved through two experimental scenarios indicate that the enhanced MOGWO algorithm outperforms other algorithms in terms of convergence, coverage, and the robust optimal Pareto solution. Finally, transportation paths based on obtained scheduling plans are experimentally corroborated by the mobile robot RAICO (Robot with Artificial Intelligence based Cognition) within a physical model of the intelligent manufacturing environment. The achieved experimental results successfully demonstrate the efficiency of the proposed methodology for optimal multi-objective scheduling of material transport tasks based on a single mobile robotic system.

Novel Formulations and Improved Differential Evolution Algorithm for Optimal Lane Reservation With Task Merging

This paper investigates a new lane reservation problem with task merging that consists of optimally determining which lanes in a transportation network have to be reserved and designing reserved lane-based routes in the network for time-crucial transport tasks. Part of the tasks whose destinations are geographically close is merged to reduce the number of vehicles and transport costs. Reserved lanes can reduce the travel time of task vehicles passing through them, while they will generate negative impact on normal traffic, such as traffic delay to the vehicles on adjacent non-reserved lanes. The objective is to minimize the total negative impact of all reserved lanes. For this problem, two new integer linear programming (ILP) models are first developed. The complexity of the problem is proved to be NP-hard. Since commercial solver (like CPLEX) is time-consuming for solving it when the problem size increases, a fast and effective improved differential evolution algorithm (IDEA) is developed based on explored problem properties. Extensive experimental results for a real-life case and benchmark instances of up to 500 nodes in the network and 30 transport tasks show the favorable performance of the IDEA, as compared to CPLEX, differential evolution algorithm and genetic algorithm. Management insights are also drawn to support practical decision-making.

Energy benchmark for energy-efficient path planning of the automated guided vehicle

The automated guided vehicle (AGV) is a piece of promising advanced transport equipment that has been widely used in flexible manufacturing systems to increase productivity and automation. Previous studies about the AGV focused on improving the capacities of perception, navigation, and anti-collision as well as reducing the transport time, cost, and distance, but insufficient attention was paid to the energy consumption (EC) reduction of AGV. The energy benchmark is recognised as an effective analytical methodology and management tool that can improve energy efficiency. Nonetheless, research on the energy benchmark for the AGV is lacking. To finish a transport task, many AGV path plans are feasible, and we develop an energy benchmark to evaluate each path plan and select the energy-saving one. We also establish a dynamic rating system of energy efficiency which is consistent with the energy-saving potentials of the transport task. The case study shows that the transport EC is reduced by 10.98 %, validating the proposed energy benchmark methodology. In addition, the effects of AGV path plans on the EC of machine tools at the workstations are analysed. Lastly, we explore the relationship between the energy efficiency of AGV path plans and the locations of workstations.

Ocean shipping company’s encroachment with outsourcing competition

As a new source to increase profits, some ocean shipping (OS) companies are beginning to encroach on the downstream market to canvass for cargoes, which would form a co-opetition relationship between OS companies and forwarders. Meanwhile, forwarders need to complete the transportation tasks through outsourcing strategy selection, that is, choose a delegation decision to outsource all transportation to an OS company, or choose a control decision to outsource ocean transportation and hinterland transportation to the OS and hinterland shipping (HS) companies, respectively. This paper uses a game-theoretical model to study the equilibrium strategy of OS company’s encroachment and forwarders’ outsourcing, and to further explore the strategic interactions between the two strategies. We solve the model analytically and find that an OS company might be worse off from encroaching on the shipping market if the entry cost is high or the cargo-canvassing capacity is high, whereas forwarders are always worse off with the OS company’s encroachment. Surprisingly, forwarders can suppress the OS company’s encroachment by changing their outsourcing decisions from control to delegation, or by improving their cargo-canvassing capacities. Our study also shows that forwarders prefer control decisions in the non-encroachment scenario, and they prefer delegation decisions if the market competition is fierce in the encroachment scenario. As for the HS scheme, introducing competing HS companies into hinterland transportation is always the optimal choice. Further, we consider an extension of the effect of OS company’s encroachment timing and show that forwarders as the first movers can make the OS company be less possible to encroach on the shipping market.

Smooth-pursuit performance during Eye-typing from Memory indicates Mental Fatigue.

Mental fatigue is known to occur as a result of activities related to e.g. transportation, health-care, military operations and numerous other cognitive demanding tasks. Gaze tracking has wide-ranging applications, with the technology becoming more compact and processing power reducing. Though numerous techniques have been applied to measure mental fatigue using gaze tracking, smooth-pursuit movement, a natural eye movement generated when following a moving object with gaze, has not been explored with relation to mental fatigue. In this paper, we report the results from a smooth-pursuit movement based eye-typing experiment with varying task difficulty to generate cognitive load, performed in the morning and afternoon by 36 participants. We have investigated the effects of time-on-task and time of day on mental fatigue using self-reported questionnaires and smooth-pursuit performance, extracted from the gaze data. The self-reported mental fatigue increased due to time-on-task, but the time of day did not have an effect. The results illustrate that smooth-pursuit movement performance declined with time-on-task, with increased error in the gaze position and an inability to match the speed of the moving object. The findings exhibit the feasibility of mental fatigue detection using smoothpursuit movements during an eye-interactive task of eye-typing.

One-shot learning for autonomous aerial manipulation.

This paper is concerned with learning transferable contact models for aerial manipulation tasks. We investigate a contact-based approach for enabling unmanned aerial vehicles with cable-suspended passive grippers to compute the attach points on novel payloads for aerial transportation. This is the first time that the problem of autonomously generating contact points for such tasks has been investigated. Our approach builds on the underpinning idea that we can learn a probability density of contacts over objects’ surfaces from a single demonstration. We enhance this formulation for encoding aerial transportation tasks while maintaining the one-shot learning paradigm without handcrafting task-dependent features or employing ad-hoc heuristics; the only prior is extrapolated directly from a single demonstration. Our models only rely on the geometrical properties of the payloads computed from a point cloud, and they are robust to partial views. The effectiveness of our approach is evaluated in simulation, in which one or three quadcopters are requested to transport previously unseen payloads along a desired trajectory. The contact points and the quadcopters configurations are computed on-the-fly for each test by our approach and compared with a baseline method, a modified grasp learning algorithm from the literature. Empirical experiments show that the contacts generated by our approach yield a better controllability of the payload for a transportation task. We conclude this paper with a discussion on the strengths and limitations of the presented idea, and our suggested future research directions.

Safety of civil air transportation in figures and diagrams

Ensuring the safety of civil aviation is the most important transport and economic task. Its solution makes it possible to develop civil air transportation. The purpose of this work is to analyze the indicators of individual and social risk in the civil aviation of the Russian Federation and to identify the generalized causes of aviation accidents. The methods of statistical analysis and modeling were used. The dynamics of the individual risk of deaths shows a negative trend in 1992-2021, while a comparison of the calculated average values of the individual risk of deaths for air passengers over the past 22 years and the previous period available for calculation (1992-1999) shows equal values. The calculations of the social risk of deaths in air accidents for different periods show that the social risk for air passengers has little changed. The model “bow tie” based on the published materials of investigations of air crashes showed the causes of accidents of aircrafts, demonstrated obvious consequences and allowed us to formulate safety barriers.

Quantify the Road Link Performance and Capacity Using Deep Learning Models

The link performance and capacity are important quantitative features in road link performance assessment, and they play vital roles in many important transportation tasks, e.g., traffic assignment and dynamic routing. However, it remains a challenging task to quantify them, particularly in a dynamic traffic scenario requiring an accurate, fast, and dynamic output. This study proposes a tailored deep learning framework for the addressed problem, which combines important transport domain knowledge reflected by the Bureau of Public Road (BPR) link performance function. In specifics, the calibration of link performance function and the estimation of link travel time are combined in the proposed framework and realized by two neural network modules. Numerical experiments demonstrate the capability of the proposed framework to capture complex relationships between dynamic link capacity and various factors and show its value in estimating link travel time.

Multi-AGV Flexible Manufacturing Cell Scheduling Considering Charging

Because of their flexibility, controllability and convenience, Automated Guided Vehicles (AGV) have gradually gained popularity in intelligent manufacturing because to their adaptability, controllability, and simplicity. We examine the relationship between AGV scheduling tasks, charging thresholds, and power consumption, in order to address the issue of how AGV charging affects the scheduling of flexible manufacturing units with multiple AGVs. Aiming to promote AGVs load balance and reduce AGV charging times while meeting customer demands, we establish a scheduling model with the objective of minimizing the maximum completion time based on process sequence limitations, processing time restrictions, and workpiece transportation constraints. In accordance with the model’s characteristics, we code the machine, workpiece, and AGV independently, solve the model using a genetic algorithm, adjust the crossover mutation operator, and incorporate an elite retention strategy to the population initialization process to improve genetic diversity. Calculation examples are used to examine the marginal utility of the number of AGVs and electricity and validate the efficiency and viability of the scheduling model. The results show that the AVGs are effectively scheduled to complete transportation tasks and reduce the charging wait time. The multi-AGV flexible manufacturing cell scheduling can also help decision makers to seek AGVs load balance by simulation, reduce the charging times, and decrease the final completion time of manufacturing unit. In addition, AGV utilization can be maximized when the fleet size of AGV is 20%-40% of the number of workpieces.

Mathematical Model for the Generalized VRP Model

The Vehicle Routing Problem (VRP) is a highly investigated logistics problem. VRP can model in-plant and out-plant material handling or a whole supply chain. The first Vehicle Routing Problem article was published in 1959 by Dantzig and Ramser, and many varieties of VRP have appeared since then. Transport systems are becoming more and more customized these days, so it is necessary to develop a general system that covers many transport tasks. Based on the literature, several components of VRP have appeared, but the development of an integrated system with all components has not yet been completed by the researchers. An integrated system can be useful because it is easy to configure; many transportation tasks can be easily modeled with its help. Our purpose is to present a generalized VRP model and show, in the form of case studies, how many transport tasks the system can model by including (omitting) each component. In this article, a generalized system is introduced, which covers the main VRP types that have appeared over the years. In the introduction, the basic Vehicle Routing Problem is presented, where the most important Vehicle Routing Problem components published so far are also detailed. The paper also gives the mathematical model of the generalization of the Vehicle Routing Problem and some case studies of the model are presented.

A hybrid particle swarm optimization and simulated annealing algorithm for the job shop scheduling problem with transport resources

This work addresses a variant of the job shop scheduling problem in which jobs need to be transported to the machines processing their operations by a limited number of vehicles. Given that vehicles must deliver the jobs to the machines for processing and that machines need to finish processing the jobs before they can be transported, machine scheduling and vehicle scheduling are intertwined. A coordinated approach that solves these interrelated problems simultaneously improves the overall performance of the manufacturing system. In the current competitive business environment, and integrated approach is imperative as it boosts cost savings and on-time deliveries. Hence, the job shop scheduling problem with transport resources (JSPT) requires scheduling production operations and transport tasks simultaneously. The JSPT is studied considering the minimization of two alternative performance metrics, namely: makespan and exit time. Optimal solutions are found by a mixed integer linear programming (MILP) model. However, since integrated production and transportation scheduling is very complex, the MILP model can only handle small-sized problem instances. To find good quality solutions in reasonable computation times, we propose a hybrid particle swarm optimization and simulated annealing algorithm (PSOSA). Furthermore, we derive a fast lower bounding procedure that can be used to evaluate the performance of the heuristic solutions for larger instances. Extensive computational experiments are conducted on 73 benchmark instances, for each of the two performance metrics, to assess the efficacy and efficiency of the proposed PSOSA algorithm. These experiments show that the PSOSA outperforms state-of-the-art solution approaches and is very robust.