Transport Robot Research Articles

Aerosol Jet and Inkjet Printing Methods for Die-Level Packaging of Custom MEMS Devices

Abstract In this study, we propose an alternative, low-cost, packaging method using both inkjet and aerosol jet printing to fabricate functional interconnects for custom die-level packages assembled on Printed Circuit Boards (PCB). Our process involves the manufacturing of a stacked structure with insulating and conducting layers, fabricated using inkjet and aerosol jet printing, respectively. In the first part of the study, we characterized UV adhesive deposition on the standard resin-coated PCB to acquire the optimal parameters necessary for the fabrication of the insulating layer with thicknesses around 100 μm. In the second part of our study, we developed a precise method for printing silver conducting lines using an aerosol jet printer, enabling the realization of structures with features as small as 50 microns on non-planar surfaces. The insulating part of the MEMS/PCB structure serves as a ramp ensuring continuity of the aerosol jet printed connection between the PCB and the Si die. A 1 cm x 1 cm Si chip with a custom MEMS microrobot was used to demonstrate the feasibility and flexibility of our approach in practice. After fabrication, we evaluated the interconnects for conductivity and repeatability. The packaging and inspection process was carried out using Nexus, a unique robotic system integrating additive manufacturing, robotic transport, and metrology. The results show that we successfully fabricated printed interconnects between copper PCB pads and Si die cleanroom fabricated gold pads, while electrical characterization revealed resistances in the range of 1 Ω - 10 Ω. Our approach can be utilized in the manufacturing of electrical interconnects for custom devices on different substrates, including traditional and flexible PCBs. Furthermore, the applied printing techniques enable the use of other insulating or conducting inks and the formation of structures of custom geometries across a wide range of scales - 20 μm to 1 mm.

Continuous Signal Convolution Using FFT with Overlap for Transportation Robot Coordinate Determination

The paper discusses the development of a continuous discrete signal convolution algorithm using fast Fourier transform (FFT) with overlap for more accurate determination of the coordinates of a transportation robot. Two methods for determining the coordinates of a transportation robot in two-dimensional space are presented, where the convolution operation is used and where this algorithm can be applied. The algorithm is based on a method that allows the FFT to be performed on multiple computational units in parallel, such as on a TMS320F28377D digital signal processor, where three computational units are available that operate independently in parallel. The use of a FFT with overlap allows signal processing without data loss, which is critical for continuous signal processing.The study of the influence of the FFT window offset value on the frequency of obtaining the spectrum of the signal has been carried out, and as it was found that reducing the FFT window offset leads to an increase in the frequency of obtaining the spectrum of the signal, which, in turn, allows you to more accurately restore the amplitude of the original signal, that is, there is less loss of spectrum data. As results, the paper provides illustrations of the study in the form of three-dimensional graphs, where the spectra of the signal in time during the FFT with overlap, where as the initial signal is used a sinusoidal signal, which over time increases its frequency. Comparison of the results of the algorithm without overlap and with overlap at different offset values showed that the method with overlap provides significantly more accurate and frequent acquisition of spectrum data, avoiding data loss.The presented results are useful in robotics when it is required to accurately determine the coordinates of a transportation robot.

Predefined-time controller design for a multiple space transportation robots system based on Lp-Norm-Normalized Sign Function

This paper presents a predefined-time controller for Multiple Space transportation Robots System (MSRS), which can be applied in on-orbit assembly tasks to transport modules to pre-assembly configuration quickly. Firstly, to simplify the analysis and design of predefined-time controller, a Predefined-time Stability Criterion is proposed in the form of Composite Lyapunov Function (CLF-PSC). Besides simplicity, the CLF-PSC also has the advantage of less conservativeness due to utilization of initial state information. Secondly, a concept of Lp-Norm-Normalized Sign Function (LPNNSF) is proposed based on the CLF-PSC. Different from traditional norm-normalized sign function, the Lp-norm of LPNNSF can be selected arbitrarily according to practical control task requirements, which means that the proposed LPNNSF is more generalized and more convenient for calculation. Thirdly, a predefined-time disturbance observer and predefined-time controller are designed based on the LPNNSF. The observer has the property of predefined-time convergence to achieve quicker and more accurate estimation of the lumped disturbance. The controller has less control input and chattering phenomenon than traditional predefined-time controller. In addition, by introducing the observer into the controller, the closed-loop system enjoys high precision and strong robustness. Finally, the effectiveness of the proposed controller is verified by numerical simulations. By employing the controller, the MSRS can carry assembly modules to the desired pre-assembly configuration accurately within predefined time.

Stochastic Initial States Randomization Method for Robust Knowledge Transfer in Multi-Agent Reinforcement Learning

Reinforcement learning, which are also studied in the field of defense, face the problem of sample efficiency, which requires a large amount of data to train. Transfer learning has been introduced to address this problem, but its effectiveness is sometimes marginal because the model does not effectively leverage prior knowledge. In this study, we propose a stochastic initial state randomization(SISR) method to enable robust knowledge transfer that promote generalized and sufficient knowledge transfer. We developed a simulation environment involving a cooperative robot transportation task. Experimental results show that successful tasks are achieved when SISR is applied, while tasks fail when SISR is not applied. We also analyzed how the amount of state information collected by the agents changes with the application of SISR.

A decoupled solution to heterogeneous multi-formation planning and coordination for object transportation

Multi-robot formations have numerous applications, such as cooperative object transportation in intelligent warehouses. In this context, robots are tasked with delivering objects in formation while avoiding intra- and inter-formation collisions. This necessitates the development of solutions for multi-robot task allocation, formation generation, rigid formation route planning, and formation coordination. In this paper, we present a cooperative formation object transportation system for heterogeneous multi-robot systems which captures robot dynamics and avoids inter-formation collisions. Accounting for heterogeneous formations expands the applicability of the proposed robotic transport system. For formation generation, we propose an approach based on conflict-based search, which integrates high-level path planning with low-level trajectory optimisation. For heterogeneous formation planning, we present a two-stage iterative trajectory optimisation framework which adheres to the kinematic constraints of our heterogeneous multi-robot system while retaining formation rigidity. For multi-formation coordination, we use a loosely-coupled algorithm which can guarantee collision-free and deadlock-free formation navigation under minimal assumptions. We demonstrate the efficacy of our approach in simulation.

Knowledge-based multi-objective evolutionary algorithm for energy-efficient flexible job shop scheduling with mobile robot transportation

Energy-efficient production is a core requirement for manufacturing companies to transform and upgrade. The popularity of mobile robots (MRs) has led to a closer relationship between production and logistics in the workshop. However, the existing energy-efficient scheduling approaches ignore the energy consumption induced by logistics equipment. For this purpose, this paper investigates the energy-efficient flexible job shop scheduling problem with mobile robots (EFJSPMR). A knowledge-based multi-objective evolutionary algorithm (KBMOEA) is proposed for simultaneously optimizing the Makespan and total energy consumption (TEC), including the energy consumption of MRs. The proposed KBMOEA includes domain knowledge in two main aspects: i, a new active decoding method is introduced to improve the search efficiency while expanding the search space of the algorithm. ii, four problem properties are proposed passively incorporated into a local search strategy to enhance the performance of the algorithm. Additionally, an adaptive mechanism is constructed to balance the exploration and the exploitation by adjusting selection and mutation probabilities. Finally, three groups of the benchmarks (a total of 44 instances) are used for experiments to verify the effectiveness of the proposed algorithm. Comparison results with the other four state-of-the-art algorithms show that the proposed KBMOEA can obtain a Pareto front with better convergence and diversity on all instances.

Emergency supplies transportation robot trajectory tracking control based on Koopman and improved event‐triggered model predictive control

AbstractIn the emergency rescue and disposal of social public emergencies, supply transportation effectively provides a strong supply foundation and realistic conditions. The trajectory tracking control of emergency supplies transportation robot is the key technology to ensure the timeliness of transportation. In this article, the emergency supplies transportation robot is taken as the research object, based on Koopman operator theory, combined with radial basis function (RBF) neural network disturbance observer and adaptive prediction horizon event‐triggered model predictive control (APET‐MPC) algorithm to investigate the purely data‐driven trajectory tracking control problem of emergency supplies transportation robot when the model parameters and models are unknown. Firstly, the Koopman operator is used to establish a high‐dimensional linear model of the robot. Secondly, the RBF neural network disturbance observer is designed to estimate the disturbance during the robot operation and compensate it to the controller. Thirdly, APET‐MPC is used to optimize the trajectory tracking control of the emergency supplies transportation robot to reduce computational complexity. Finally, the performance of the proposed trajectory tracking controller is verified by Carsim/ Simulink joint simulation. The simulation results show that the model established by Koopman operator theory can achieve the high accuracy approximation of the robot. Compared with the MPC trajectory tracking controller, the APET‐MPC trajectory tracking controller based on RBF neural network disturbance observer (RBF‐APET‐MPC) improves the tracking accuracy of the robot and reduces the total triggering times of the system by more than 50%.

Snail-inspired water-enhanced soft sliding suction for climbing robots

Snails can stably slide across a surface with only a single high-payload sucker, offering an efficient adhesive locomotion mechanism for next-generation climbing robots. The critical factor for snails’ sliding suction behaviour is mucus secretion, which reduces friction and enhances suction. Inspired by this, we proposed an artificial sliding suction mechanism. The sliding suction utilizes water as an artificial mucus, which is widely available and evaporates with no residue. The sliding suction allows a lightweight robot (96 g) to slide vertically and upside down, achieving high speeds (rotation of 53°/s and translation of 19 mm/s) and high payload (1 kg as tested and 5.03 kg in theory), and does not require energy during adhesion. Here, we show that the sliding suction is a low-cost, energy-efficient, high-payload and clean adhesive locomotion strategy, which has high potential for use in climbing robots, outdoor inspection robots and robotic transportation.

Research on the Intelligent System Architecture and Control Strategy of Mining Robot Crowds

Despite the pressure of carbon emissions and clean energy, coal remains the economic backbone of many developing countries due to its abundant resources and widespread distribution. The stable supply of coal is also vital for the global economy and remains irreplaceable in the future global energy structure. China has been a major contributor to annual coal output, accounting for nearly 50% worldwide since 2014. However, despite implementing intelligent coal mining technology, China’s coal mining industry still employs over 1.5 million underground miners, posing significant safety risks associated with underground mining operations. Therefore, the introduction of coal mining robots in underground mines is an urgently needed scientific and technological solution for upgrading China’s and even the world’s coal energy industry. The working face needs a shearer, hydraulic support, a scraper conveyor, and other equipment for coordination. The deep integration of intelligent technology with factors such as “humans, machines, the environment, and management” in the workplace is the core content of intelligent coal mines. This paper puts forward an advanced framework for robot technology systems in coal mining, including single robots, robotized equipment, robot crowds, and unmanned systems. The framework clarifies the common key technologies of coal mining robot research and development and the cross-integration with new technologies such as 5G, the industrial internet, big data, artificial intelligence, and digital twins to improve the autonomous and intelligent application of coal mining robots. By establishing a scientific and complete standard system for coal mining robots, we aim to achieve the customized research and development and standardized production of various types of robot. A specific analysis is conducted on the research progress of common key technologies such as the explosion-proof design, mechanical system innovation, power drive, intelligent sensing, positioning and navigation, and underground communication of coal mining robots. The current research and application status of various types of coal mining robots in China are summarized. A new direction for future coal mining robot research and development is proposed. Robotic mining systems should be promoted to enhance the overall intelligence level and efficiency of mining equipment. To develop human–machine environment-integrated robots to improve the autonomy and collaboration level of coal mining robots, the digital twinning of the entire mine robot system should be accelerated; the normalized operation level of coal mine robots should be improved; research on coal mining robots, shield support robots, and transportation robots should be performed; intelligence should be achieved in fully mechanized mining faces; and equipment shield support for fully mechanized mining faces should be provided. The practical process of implementing coal mining robotization is summarized in this paper, and the technical and engineering feasibility of the coal mining machine population is verified.

A reconfigurable multi-terrain adaptive casualty transport aid base on Watt II six-bar linkage for industrial environment.

Introduction: This paper presents the Reconfigurable Multi-Terrain Adaptive Casualty Transport Aid (RMTACTA), an innovative solution addressing the critical need for rapid and safe pre-hospital casualty transport in industrial environments. The RMTACTA, leveraging the Watt II six-bar linkage, offers enhanced adaptability through six modes of motion, overcoming the limitations of traditional stretchers and stretcher vehicles by facilitating navigation across narrow and challenging terrains. Methods: The RMTACTA's design incorporates two branching four-bar mechanisms to form a compact, reconfigurable Watt II six-bar linkage mechanism. This setup is controlled via a single remote rope, allowing for easy transition between its multiple operational modes, including stretcher, stretcher vehicle, folding, gangway-passing, obstacle-crossing, and upright modes. The mechanical design and kinematics of this innovative linkage are detailed, alongside an analysis of the optimal design and mechanical evaluation of rope control. Results: A prototype of the RMTACTA was developed, embodying the proposed mechanical and kinematic solutions. Preliminary tests were conducted to verify the prototype's feasibility and operability across different terrains, demonstrating its capability to safely and efficiently transport casualties. Discussion: The development of the proposed Reconfigurable Multi-Terrain Adaptive Casualty Transport Aid (RMTACTA) introduces a novel perspective on the design of emergency medical transport robots and the enhancement of casualty evacuation strategies. Its innovative application of the Watt II six-bar linkage mechanism not only showcases the RMTACTA's versatility across varied terrains but also illuminates its potential utility in critical scenarios such as earthquake relief, maritime rescue, and battlefield medical support.

Concept of operation of the Agile Robot Transporter for ITER in-vessel maintenance

ITER machine availability is crucial for fusion reactors in order to be the main source of electricity, which is affected by many aspects. This paper focuses on maintenance aspects of the machine. As the plasma facing components inside the fusion reactors become highly activated caused by the neutrons generated by fusion reactions, maintenance of the fusion reactors needs to be done by remote means. In order to increase machine availability, it is required to have efficient maintenance scenario in terms of time and effort, which can be achieved through effective in-vessel remote handling (RH) operations, simplified maintenance of the RH system itself, well-coordinated logistics, and trained personnel, etc. All these aspects need to be investigated during the design phases, which requires a well-defined concept of operation of the RH systems. This paper describes the concept of operation of the Agile Robot Transporter (ART), an ITER in-vessel RH system, that is under development to carry out neutron and optical diagnostic calibrations, and removal/replacement of the blanket first wall samples. Based on the functional analysis of the required in-vessel tasks, system requirements are defined. Among them, key requirements to improve RH operation efficiency are identified and the ART is designed accordingly. Efficient deployment and removal operations of the ART are crucial so that it requires less vacuum vessel (VV) port opening and less cask transportation, which reduces needs for hands-on operations resulting in less human radiation exposure. Various and flexible tool exchange and storage concepts are proposed that allow parallel operations between the in-vessel and ex-vessel RH systems. Modular and re-configurable design is proposed for easy RH system maintenance, and effective rescue operations to rescue the RH system when it fails.

Light-Driven Small-Scale Soft Robots: Material, Design and Control

Abstract Small robots for drug transportation, environmental detection and military reconnaissance have been a popular research topic in the field of robotics. Recently, people have proposed using light-driven actuators to make flexible and remote-controllable small robots. Herein, we reviewed the research on light-driven soft robots in recent years. First, we summarized and compared the performance and fabrication method of light-driven actuators. Then, we classified and summarized the structures of robots according to their move mode. After that, we described how to control the robot. Finally, the challenges of light-driven robots are discussed.

Passive Visual Odometry of Mobile Transport Robots in Production Areas Using Anchor Points

The article considers the problem of determining the orientation of mobile transport robots, which are now actively used in almost all areas of industry. They make it possible to intensify production, free personnel from performing routine operations, and exclude the influence of the human factor from work. Based on the analysis of various variants of active orientation methods involving the use of binocular video systems installed on the vehicles themselves, it is proposed to use a passive orientation detection system. It includes a single stationary monocular video system, the replacement of onboard video systems on mobile robots with simple radiation sensors, as well as the use of anchor points in the area of their movement. The order of choice of anchor points in solving the problem of orientation of a mobile transport robot using a monocular video system, in particular for a rectangular interior space, is considered. Calculation formulas for determining the coordinates of the sensor from the pixel coordinates of its image obtained from a monocular video camera are determined. The general sequence of actions for determining the orientation of a mobile robotic platform is also considered. Unlike active systems, this method makes it possible to significantly simplify the hardware, significantly facilitate the analysis of the current position of the mobile robot and thereby reduce the computational complexity of calculations due to the fact that there is no need to use a complex mathematical apparatus. It is being replaced by simpler two-dimensional geometric calculations. This approach, due to the unified management, makes it possible to effectively coordinate the actions of a group of mobile transport robots when they are used together, greatly simplifies the solution of a number of tasks for optimizing the intra-shop movement of mobile vehicles.

GY-SLAM: A Dense Semantic SLAM System for Plant Factory Transport Robots.

Simultaneous Localization and Mapping (SLAM), as one of the core technologies in intelligent robotics, has gained substantial attention in recent years. Addressing the limitations of SLAM systems in dynamic environments, this research proposes a system specifically designed for plant factory transportation environments, named GY-SLAM. GY-SLAM incorporates a lightweight target detection network, GY, based on YOLOv5, which utilizes GhostNet as the backbone network. This integration is further enhanced with CoordConv coordinate convolution, CARAFE up-sampling operators, and the SE attention mechanism, leading to simultaneous improvements in detection accuracy and model complexity reduction. While mAP@0.5 increased by 0.514% to 95.364, the model simultaneously reduced the number of parameters by 43.976%, computational cost by 46.488%, and model size by 41.752%. Additionally, the system constructs pure static octree maps and grid maps. Tests conducted on the TUM dataset and a proprietary dataset demonstrate that GY-SLAM significantly outperforms ORB-SLAM3 in dynamic scenarios in terms of system localization accuracy and robustness. It shows a remarkable 92.59% improvement in RMSE for Absolute Trajectory Error (ATE), along with a 93.11% improvement in RMSE for the translational drift of Relative Pose Error (RPE) and a 92.89% improvement in RMSE for the rotational drift of RPE. Compared to YOLOv5s, the GY model brings a 41.5944% improvement in detection speed and a 17.7975% increase in SLAM operation speed to the system, indicating strong competitiveness and real-time capabilities. These results validate the effectiveness of GY-SLAM in dynamic environments and provide substantial support for the automation of logistics tasks by robots in specific contexts.

Potential of Assistive Robots in Clinical Nursing: An Observational Study of Nurses' Transportation Tasks in Rural Clinics of Bavaria, Germany.

Transportation tasks in nursing are common, often overlooked, and directly impact patient care time in the context of staff shortages and an aging society. Current studies lack a specific focus on transportation tasks, a gap our research aims to fill. By providing detailed data on transportation needs in nursing, our study establishes a crucial foundation for the development and integration of assistive robots in clinical settings. In July and September 2023, we conducted weekly observations of nurses to assess clinical transportation needs. We aim to understand the economic impact and the methods nurses use for transportation tasks. We conducted a participant observation using a standardized app-based form over a seven-day observation period in two rural clinics. N = 1830 transports were made by nurses and examined by descriptive analysis. Non-medical supplies account for 27.05% (n = 495) of all transports, followed by medical supplies at 17.32% (n = 317), pharmacotherapy at 14.10% (n = 258) and other other categories like meals or drinks contributing 12.68% (n = 232). Most transports had a factual transport time of under a minute, with patient transport and lab samples displaying more variability. In total, 77.15% of all transports were made by hand. Requirements to collect items or connect transports with patient care were included in 5% of all transports. Our economic evaluation highlighted meals as the most costly transport, with 9596.16 € per year in the observed clinics. Budget-friendly robots would amortize these costs over one year by transporting meals. We support understanding nurses' transportation needs via further research on assistive robots to validate our findings and determine the feasibility of transport robots.

Actuator fault-tolerant control allocation for cooperative transportation of multiple omnidirectional mobile robots

In this paper, we propose a fault detection, fault isolation, and control reconfiguration (FDIR) for multiple automated guided vehicles (AGVs) to accomplish object transportation under actuator failures. Firstly, the observers are designed for the AGVs to detect any occurring faults in actuators. Secondly, banks of observers are employed to isolate the broken actuators. Finally, according to the isolation results, the controllers are reconfigured to accommodate the faults. The stability of the proposed methods is verified using the Lyapunov technique. It is worth noting that the proposed FDIR method considers the uncertainties in system dynamics, enhancing its practicality and convenience for implementation. The effectiveness of the proposed algorithm is demonstrated via both numerical and experimental examples involving multiple omnidirectional AGVs experiencing actuator faults.

Digitization of Accounting in the Innovative Management of Autonomous Robotic Transport

The digitization of economic processes is advancing across all sectors, contributing to the development of Industry 5.0. A key element of this fifth industrial revolution is the activation of robotic economic activity. Recently, advancements in autonomous robotic transport have been implemented in practice. However, both the practical application of unmanned vehicles and scientific developments in this field have shown low efficiency in the implementation of projects for the autonomous transportation of goods and passengers. This inefficiency stems from insufficient attention to the accounting and management aspects of autonomous robotic transport operations. The scientific and practical novelty of this study lies in improving accounting and management practices in the context of digitalization, specifically by addressing the fundamental transformations in economic processes caused by the use of autonomous vehicles. The key organizational factors influencing accounting for robotic transport operations include the type of transported objects, fuel and energy resource consumption, human involvement, the capacity and number of goods (or passengers) transported at one time, continuous operation, maintainability, software update capabilities, autonomous interaction with other transport means, and communication and information sharing with customers of transport services. A method for digitizing the accounting of fuel and energy costs, personnel wages, social activity deductions, depreciation, operational costs, and other costs related to the functioning of autonomous robotic transport has been developed. This method leverages IoT data and considers the organizational prerequisites mentioned. The use of two-dimensional calculation units, such as "kilogramkilometre" and "passenger-kilometre" units, for the digitalization of cost calculations for passenger and cargo transportation via autonomous robotic transport has been proposed. Additionally, the procedure for determining the cost of transport services for end users and the formation of information arrays for the innovative management of transport enterprises has been refined. The elimination of organizational restrictions in managing autonomous transport operations, alongside the need for information synchronization between transport enterprises and other business entities within the information ecosystem of a smart city, highlights future research prospects in this area.

Integrated Genetic Algorithm with Dispatching Rules to solve the Flexible Job Shop Scheduling Problem under Multi-AMR Transportation Constraints

The Flexible Job Shop Scheduling Problem (FJSSP) is a challenging issue for industries and manufacturers. However, the transportation tasks in this problem should not be underestimated, as their impact is substantial, and they are frequently overlooked in the literature. In this paper, the FJSSP with multi-AMR (Autonomous mobile robot) transportation constraints is addressed. We developed a genetic algorithm with dispatching rules to solve the Flexible Job Shop Scheduling Problem with Multi-AMR Transportation Constraints by minimizing the total completion time. Two dispatching rules are used to assign transportation robots. The first rule is random assignment and the second rule is AMR per Job assignment. Our encoding and decoding process utilizes a three-layer sequence (machine sequence, operation sequence, and AMR sequence). Finally, we conducted comprehensive experiments on different benchmarks to assess the performance of the proposed algorithm with a variable number of AMR to transport the operations. The experimental results indicate that the proposed algorithm effectively and efficiently solve the problem.

Comprehensive and Simulated Modeling of a Centralized Transport Robot Control System

Comprehensive and Simulated Modeling of a Centralized Transport Robot Control System

What may automation cost? Activity-based costing for autonomous transport in urban logistics

A shift towards driverless vehicles in urban logistics will only happen if the use of the new technologies is economically beneficial. For this reason, this paper discusses the question of what autonomous shipment may cost in urban logistics. Using activity-based costing, the distinction between shipment with conventional trucks and autonomous transport robots is analysed. The calculation shows that the omission of the driver does not necessarily lead to a reduction of process costs. Only by also reducing technology costs, autonomous transport in urban logistics can gain economic benefits.