Mobile Base Research Articles

Robot base position and spacecraft cabin angle optimization via homogeneous stiffness domain index with nonlinear stiffness characteristics

The use of mobile robots for machining large components has received considerable research interest for the application of industrial robots in the machinery manufacturing sector. However, the low structural stiffness of industrial robots can result in poor machining quality under the action of cutting forces. Therefore, this paper proposes a simultaneous optimization method the mobile robot base position and cabin angle using homogeneous stiffness domain (HSD) index for large spacecraft cabins. First, a nonlinear joint stiffness model that considers the gravity compensator mechanism is established to describe the stiffness characteristics of heavy-duty robots more accurately. Subsequently, a HSD index is proposed to evaluate the overall stiffness values and stiffness fluctuation for all robot postures in the machining program. An optimization model is then established based on the HSD under the constraints of machining accessibility, joint angle limitation and singularity. The optimal base position and cabin angle are determined simultaneously using the sparrow search algorithm. Finally, simulation and milling experiments are used to demonstrate that the optimization method proposed in this paper can effectively improve the machining quality.

Whole Body Control of Mobile Manipulators With Series Elastic Actuators for Cart Pushing Tasks.

Human-centered environments provide affordance for the use of two-handed mobile manipulators. Yet robots designed to function in and physically interact with such environments are not yet capable of meeting human users' requirements. This work proposes a whole body control framework of a two-handed mobile manipulator driven by series elastic actuators (SEAs) for cart pushing tasks. A whole body dynamic model for an integrated mobile platform and on-board arms is revealed, which takes into account the interaction forces with the cart. Then, the explicit force/position control of the mobile manipulator is performed. It enables the robot to interact dynamically with the environment while providing motion, i.e., the manipulators provide both output force control and motion control for pushing a cart. To cope with the highly nonlinear system dynamics and parameter variation of a SEA-driven mobile manipulator, this work proposes an adaptive robust controller based on a novel integral barrier Lyapunov function for cart pushing tasks by considering model uncertainty. The proposed controller enables the mobile manipulator to complete cart pushing tasks by regulating the position and output force of the mobile base and arms. The experimental results show the effectiveness of this approach in cart pushing tasks.

Automated fiber switch with path verification enabled by an AI-powered multi-task mobile robot

As the capacity of optical transport networks undergoes significant growth, there is an ongoing discussion on how to effectively leverage both spectral and spatial degrees of freedom to scale future network capacity. This paper presents an artificial intelligence (AI)-powered multi-task robot comprising a collaborative robotic arm and a mobile robotic base designed for optical network automation. The robot demonstrates the capability of direct fiber switching, establishing static fiber links that consume zero power and have minimal insertion loss from fiber connectors. As a precautionary measure before physically switching fiber cables, the robot performs path verification by detecting robot-driven events using real-time coherent receivers, aiming to avoid accidental unplugging. Additionally, the robot showcases its mobility by efficiently navigating between different network racks and rooms while executing various tasks. Implementing the automation of network operations using robots has the potential to reduce both capital and operational expenditures.

Design of a Circular Micro-strip Patch Antenna for Improved Directivity and Gain of Mobile Communication Base Station

This study presents the design of Circular Micro-strip Patch Antenna (CMPA) for improved directivity and gain of mobile communication base station. The design incorporates a Rogger/5880 substrate material with a permittivity of 2.2, tangent angle of 0.00009 and a substrate height (thickness) of 0.98μm at a frequency of 1.65THz. Diversity Antenna Array Processing (DAAP) and Chebyshev Antenna Array (CAA) methods were used in the design. AutoCAD software and 2D data plotter were used for the diagrams and charts while Maple software was used for simulation. For angles of 0o, 30o, 60o, 90o, 120o, 150o and 180o, the array factors are 7.84dB, 7.85dB, 7.87dB, 7.88dB, 7.87dB, 7.85dB and 7.84db respectively. The results from the design demonstrated a remarkably low side lobe level of 0.01dB, indicating excellent suppression of unwanted radiation in undesired directions. Additionally, the antenna exhibits a Voltage Standing Wave Ratio (VSWR) of unity, ensuring maximum power transfer efficiency from the source to the load. Furthermore, the antenna shows a high directivity and gain of approximately 9dB, which enhances the signal strength and coverage range. The Half Power Beam-width (HPBW) and First Null Beam-width (FNBW) was measured as 300 and 600 respectively. These parameters depict the angular width of the main radiation beam and the sharpness of the beam pattern’s nulls. In all, the designed CMPA with its CAA based antenna array holds great potential for communication networks and for mobile communication base station, offering enhanced performance in terms of radiation characteristics and signal quality.

Development and Evaluation of Mobility and Excavation Rover Toward Lunar Base Construction

Development and Evaluation of Mobility and Excavation Rover Toward Lunar Base Construction

RETRACTED: SFL-MDrone: Synchronous federated learning enabled multi drones

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.

Cellular traffic forecasting based on inverted transformer for mobile perception dual-level base station sleep control

Due to the extensive implementation of the fifth generation wireless communication networks (5 G), numerous base stations are being strategically deployed in densely inhabited areas to address the escalating need for network traffic. Within wireless cellular networks, over 80% of energy consumption is primarily concentrated on the side of the base station. In comparison to 4 G base stations, the 5 G base stations exhibit notable enhancements in terms of bandwidth, peak rate, and transmission power. Nonetheless, they suffer from limited signal coverage and extensive resource consumption. To address this challenge effectively, the present study introduces a Mobile Perception Dual-Level (MPDL) base station sleep control strategy based on cellular traffic forecasting. The strategy aims to satisfy the demands for reduced power consumption and enhanced service quality by monitoring both the current network traffic of the base station and the user's mobility, consequently placing the base station into two different sleep states. Specifically, we first designed an inverted Transformer (iTransformer) model to accurately forecast cellular traffic. The model is capable of effectively capturing both the spatial and temporal attributes of cellular traffic. It also takes into account the impact of geographical features, such as Points of Interest (POIs) and the quantity of Base Stations (BSs), on the forecasting outcomes. Consequently, it attains precise predictions of the cellular traffic. Based on the forecasting outcomes of cellular traffic, this study presents a mobility perception dual-level sleep strategy. This strategy proposal differentiates sleep into deep sleep and light sleep, while also taking into account the mobility attributes of users. It incorporates considerations for Quality of Service (QoS) and energy consumption. We assess the performance and application impacts of cellular traffic forecasting and base station sleep strategy in real-world datasets. Several experiments and validations have confirmed that the MPDL, as proposed in this paper, effectively decreases power consumption by a range of 22% to 41% when compared to baselines.

An Object Deformation-Agnostic Framework for Human–Robot Collaborative Transportation

In this study, an adaptive object deformability-agnostic human-robot collaborative transportation framework is presented. The proposed framework enables to combine the haptic information transferred through the object with the human kinematic information obtained from a motion capture system to generate reactive whole-body motions on a mobile collaborative robot. Furthermore, it allows rotating the objects in an intuitive and accurate way during co-transportation based on an algorithm that detects the human rotation intention using the torso and hand movements. First, we validate the framework with the two extremities of the object deformability range (i.e., purely rigid aluminum rod and highly deformable rope) by utilizing a mobile manipulator which consists of an Omni-directional mobile base and a collaborative robotic arm. Next, its performance is compared with an admittance controller during a co-carry task of a partially deformable object in a 12-subjects user study. Quantitative and qualitative results of this experiment show that the proposed framework can effectively handle the transportation of objects regardless of their deformability and provides intuitive assistance to human partners. Finally, we have demonstrated the potential of our framework in a different scenario, where the human and the robot co-transport a manikin using a deformable sheet. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Transportation of objects which requires the cooperation of multiple partners, is a common task in industrial settings such as factories and warehouses. The existing human-robot collaboration solutions for this task have focused only on purely rigid objects, although deformable objects need to be carried frequently in real-world applications. In this paper, we introduce a human-robot collaborative transportation framework that can handle objects with different deformability ranging from purely rigid to highly deformable. In particular, the proposed framework generates whole-body movements on a mobile collaborative robot by combining of the haptic information transmitted through the object and the human motion information obtained from a motion capture system. Moreover, the framework includes an intuitive way to rotate the object during the execution based on human hand and torso motion. The results of the experiments where objects with various deformability characteristics were transported in collaboration with a mobile manipulator demonstrated the high potential of the proposed approach in a laboratory setting. In the future, we plan to employ a less expensive vision-based human motion tracking system instead of the IMU-based system used in this study. With this change, we will be able to eliminate the need for wearable sensors from the framework presented, which would enhance its usability in real-world scenarios.

Digital infrastructure empowerment and urban carbon emissions: Evidence from China

China is accelerating its entry into the digital age, and the contribution of digital infrastructure to the reduction of carbon emissions is becoming increasingly prominent. The influence mechanism of digital infrastructure on urban carbon emissions is empirically tested in this study from the standpoint of digital empowerment using panel data from 250 cities between 2008 and 2020. According to the study, digital infrastructure greatly lowers urban carbon emissions, with each additional mobile base station reducing carbon emissions by 36 tons. This conclusion remains robust even after controlling for endogeneity issues. The mechanism examination indicates that the decarbonization effect of digital infrastructure is mainly achieved by empowering enterprises, individuals, and governments with digital capabilities. Heterogeneity analysis reveals that the effects of digital infrastructure on carbon emissions vary after considering variations in the city's administrative hierarchy, industrial structure, and resource types in China. According to the study's findings, developing countries should expand digital infrastructure construction and fully harness its potential for energy saving and emissions reduction to meet their emission reduction targets. At the same time, differentiated emission reduction policies should be formulated to adapt to the heterogeneous characteristics among cities and ensure that emission reduction measures are effectively implemented in each city.

Enhancement of fuel cell based energy sustainability for cell on wheels mobile base stations used in disaster areas

In disaster areas, Cells on Wheels (COW) mobile infrastructures are one of the most widely used solutions to restore communications lost due to power grid collapse or physical damage. The biggest problem with such systems is the low operating time and the inability to ensure energy continuity due to logistical problems.In this proposed study, the solution to the stated problem is focused on, and the use of hydrogen, which is the most important energy option of the future, is proposed as an innovative approach. The PV-hydrogen/fuel cell hybrid energy generation and storage system ensures the continuity of energy required for COW. In this study, the system is analyzed under realistic conditions using radiation and temperature data from different seasons in the disaster area. The energy needed in the proposed system is provided by PV and fuel cell with a control strategy. In addition, the electrolyser in the system contributes to the continuity of the energy by producing hydrogen with the excess energy provided by the PV. COW mobile infrastructures, which have a working time of 3 h under normal conditions, have been provided with energy continuity throughout the day thanks to the proposed system. As a result, this study has revealed that it is a solution to an important problem, especially in disaster areas where information flow is very critical.

Energy-efficient indoor hybrid deployment strategy for 5G mobile small-cell base stations using JAFR Algorithm

In the context of 5th-generation (5G) mobile communication technology, deploying indoor small-cell base stations (SBS) to serve visitors has become common. However, indoor SBS is constrained by factors such as service capacity, signal interference, and structural layout. Merchants within large buildings frequently host diverse activities to attract visitors, significantly increasing indoor traffic and crowd-gathering phenomenon. Consequently, SBS faces challenges of excessive energy consumption, compromised communication quality, and an inability to provide service to all visitors. Merchants aim to deploy SBS that can effectively curtail energy consumption costs while fulfilling visitor needs. However, due to the intermittent nature of high footfall situations, employing additional fixed SBS is not economically viable. Therefore, we address the challenge of maintaining service quality and mitigating energy consumption of SBS during footfall fluctuations by proposing an SBS model with a dynamic sleep mechanism. We simulate the internal structure of a three-dimensional (3D) building and the footfall over time. Within this model, we leverage the flexibility of mobile small-cell base stations (MSBS) to seamlessly traverse service regions. We compute the transmission power and location of SBS and MSBS based on energy efficiency (EE), combining their strengths to tackle the challenge. This approach maintains SBS communication quality while curbing energy consumption. We attain the optimal hybrid deployment strategy by enhancing the adaptive differential evolution with optional external archive (JADE) algorithm and incorporating the final fitness formula, the adaptive ranking mutation operator strategy, and the disorder replacement strategy (DRS) in it to form the proposed joint adaptive fusion with ranking (JAFR) algorithm. Our comparative simulation experiments demonstrate the effectiveness of JAFR in addressing the challenges against conventional methods, recent differential evolution algorithms, and mobile base station (MBS) deployment approaches posed by this model. The results indicate that the JAFR algorithm yields superior SBS deployment strategies in most cases.

Posture Optimization of the TIAGo Highly-Redundant Robot for Grasping Operation

This study explores the optimization of the TIAGo robot’s configuration for grasping operation, with a focus on the context of aging. In fact, featuring a mobile base and a robotic arm, the TIAGo robot can conveniently aid individuals with disabilities, including those with motor and cognitive impairments in both domestic and clinical settings. Its capabilities include recognizing visual targets such as faces or gestures using stereo cameras, as well as interpreting vocal commands through acoustic sensors to execute tasks. For example, the robot can grasp and lift objects such as a glass of water and navigate autonomously in order to fulfill a request. The paper presents the position and differential kinematics that form the basis for using the robot in numerous application contexts. In the present case, they are used to evaluate the kinematic performance of the robot relative to an assigned pose in the search for the optimal configuration with respect to the higher-order infinite possible configurations. Ultimately, the article provides insight into how to effectively use the robot in gripping operations, as well as presenting kinematic models of the TIAGo robot.

Assessment of Radio-Frequency Radiation Exposure from Selected Mobile Base Stations in Yenagoa, Bayelsa State, Nigeria

The antennas from mobile base stations radiate non-ionizing radio frequency radiation into space. The human body can experience negative health effects from prolonged exposure to radiofrequency (RF) radiation. Because base station installations and telecommunication networks are growing, there is a growing need to address the health risks connected with exposure to telecommunication masts. Using the Electrosmog Meter Cornet Model, power density from many telecom masts owned by various network operators was measured in this investigation. At 10-meter intervals, the RF radiation in seven (7) settlements in Yenagoa town, located in the Yenagoa Local Government Area of Bayelsa State, was measured within radial distances of 100 meters. With a mean value of 4.0569mWm-2, the measured values vary from 0.650mWm-2 to 9.810mWm-2. Still, values evolved. Nevertheless, additional factors, such as adjacent electromagnetic sources' wave interference or reference base station interference, caused readings to fluctuate. It was discovered that the greatest power density level was 9.810 mWm-2, and it was measured 20 meters away from the base station at site MBS1. As per the International Commission on Non-ionizing Radiation Protection (ICNIRP) and other regulatory authorities, the standard limit of 4.5Wm-2 for the 900 MHz system is not exceeded by the radiation exposure level, as the findings show. This shows that the exposure levels in these areas are low enough to not seriously jeopardize the health of those living in the research area.

A Fairness-Enhanced Federated Learning Scheduling Mechanism for UAV-Assisted Emergency Communication.

As the frequency of natural disasters increases, the study of emergency communication becomes increasingly important. The use of federated learning (FL) in this scenario can facilitate communication collaboration between devices while protecting privacy, greatly improving system performance. Considering the complex geographic environment, the flexible mobility and large communication radius of unmanned aerial vehicles (UAVs) make them ideal auxiliary devices for wireless communication. Using the UAV as a mobile base station can better provide stable communication signals. However, the number of ground-based IoT terminals is large and closely distributed, so if all of them transmit data to the UAV, the UAV will not be able to take on all of the computation and communication tasks because of its limited energy. In addition, there is competition for spectrum resources among many terrestrial devices, and all devices transmitting data will bring about an extreme shortage of resources, which will lead to the degradation of model performance. This will bring indelible damage to the rescue of the disaster area and greatly threaten the life safety of the vulnerable and injured. Therefore, we use user scheduling to select some terrestrial devices to participate in the FL process. In order to avoid the resource waste generated by the terrestrial device resource prediction, we use the multi-armed bandit (MAB) algorithm for equipment evaluation. Considering the fairness issue of selection, we try to replace the single criterion with multiple criteria, using model freshness and energy consumption weighting as reward functions. The state of the art of our approach is demonstrated by simulations on the datasets.

Joint trajectory, transmission time and power optimization for multi-UAV data collecting system

Unmanned aerial vehicles (UAVs) have been generally applied in the field of communication due to their small size, flexible mobility, and convenient deployment. As a mobile base station, the UAV node can quickly establish a line-of-sight link with the ground node, thereby improving communication performance. In this paper, we study a multi-UAV assisted data collecting system. Specifically, in the case of limited system energy consumption, UAV flight energy consumption and ground node data transmission energy consumption are considered as an general limitation, and considering the channel interference between nodes, a multi-UAV assisted data collection model is studied. An non-convex problem that maximizes the minimum amount of data collected from ground nodes is further formulated. Since the original optimization problem is non-convex that difficult to solve directly, the problem is first decomposed into four sub-problems, and then the solution of each sub-problem is obtained by using successive convex approximation and block coordinate descent method. Finally, based on the solution of the four subproblems, an iterative algorithm for joint optimization of data transmission planning, transmission power, UAV trajectory and mission time is proposed. Simulation experiments show that the proposed algorithm can obtain more transmission data than the baseline algorithms.

Enhanced indoor positioning through human-robot collaboration

AbstractIndoor positioning is a critical component for numerous applications and services. However, GNSS systems face challenges in delivering accurate positioning information in indoor environments. Current indoor positioning research primarily concentrates on enhancing the positioning performance of individual terminals through various techniques. As we transition into the Internet of Things (IoT) era, former indoor positioning methods need refinement. In this paper, we propose a novel indoor positioning method that leverages robots as mobile base stations to mitigate the problem of inadequate fixed base stations and aims to enhance positioning accuracy by incorporating pedestrian inertial navigation data. The process involves several steps. First, the mobile robots accurately determine their positions and performing coordinate transformations to ensure consistency with pedestrian coordinate systems. Then, pedestrians use the ranging information from these robots along with their smartphones’ sensors for multi-source fusion positioning. Finally, an Extended Kalman Filter (EKF) is applied to fuse the multiple sources of data, considering various sources of errors, to provide enhanced positioning performance. Experimental results demonstrate the effectiveness of this approach in addressing indoor positioning challenges. This method could benefit numerous scenarios involving robots, enhancing pedestrian positioning accuracy and overall system robustness. The paper provides a comprehensive exploration of this proposed method, its implications, and potential directions for future advancements.

A Joint Optimization Algorithm for Trajectory Planning and Resource Allocation of Vehicle Mobile Base Stations for On-Demand Coverage Networks

In today’s urban hotspot regions, service traffic exhibits dynamic variations in both time and location. Traditional fixed macro base stations (FMBSs) are unable to meet these dynamic demands due to their fixed coverage and capacity. Therefore, this paper introduces a novel algorithm for the joint optimization of the placement of terrestrial vehicle-mounted mobile micro base stations (mBSs), the correlation of service clusters (SCs) with mBSs, and resource assignments. The objective is to maximize the matching degree between network capacity and service demands while adhering to constraints related to the power, coverage, and bandwidth of mBSs, as well as the data rate required for the services. Additionally, we investigate the mobility of the mBSs towards the SCs in the spatiotemporal changing service demand network and obtain optimal trajectories for the mBSs. We begin by formulating the problem of maximizing the matching degree by analyzing the capacity provided by the base stations and the network service demand. Subsequently, we derive solutions to the optimization problem using our algorithm. The simulation results demonstrate that the proposed algorithm can effectively meet the capacity demand of dynamically changing hotspot regions and achieve on-demand, resilient coverage of hotspot regions in the network.

ADAM: a robotic companion for enhanced quality of life in aging populations.

One of the major problems of today's society is the rapid aging of its population. Life expectancy is increasing, but the quality of life is not. Faced with the growing number of people who require cognitive or physical assistance, new technological tools are emerging to help them. In this article, we present the ADAM robot, a new robot designed for domestic physical assistance. It mainly consists of a mobile base, two arms with grippers and vision systems. All this allows the performance of physical tasks that require navigation and manipulation of the environment. Among ADAM's features are its modularity, its adaptability to indoor environments and its versatility to function as an experimental platform and for service applications. In addition, it is designed to work respecting the user's personal space and is collaborative, so it can learn from experiences taught by them. We present the design of the robot as well as examples of use in domestic environments both alone and in collaboration with other domestic platforms, demonstrating its potential.

Implementation of a Robotic Mobile Manipulator moving a NDT probe inside Steel Cylinder Concrete Pipes for Corrosion Assessment

This paper presents the full implementation of a mobile robotic manipulator moving a probe inside steel cylinder concrete pipes (SCCP) for corrosion assessment. Mechanical prototype based on an off-the-shelf mobile base and a custom-designed manipulator, control and monitoring software architecture, posture control and manipulation inside SCCP are presented. Simulations and testing under realistic operating conditions demonstrate a reliable solution providing repeatable results with high positioning accuracy.

Organizations as Algorithms: A New Metaphor for Advancing Management Theory

AbstractAccording to the ‘Point’ essay, management research's reliance on corporate data threatens to replace objective theory with profit‐biased ‘corporate empiricism’, undermining the scientific and ethical integrity of the field. In this ‘Counterpoint’ essay, we offer a more expansive understanding of big data and algorithmic processing and, by extension, see promising applications to management theory. Specifically, we propose a novel management metaphor: organizations as algorithms. This metaphor offers three insights for developing innovative, relevant, and grounded organization theory. First, agency is distributed in assemblages rather than being solely attributed to individuals, algorithms, or data. Second, machine‐readability serves as the immutable and mobile base for organizing and decision‐making. Third, prompting and programming transform the role of professional expertise and organizational relationships with technologies. Contrary to the ‘Point’ essay, we see no theoretical ‘end’ in sight; the organization as algorithm metaphor enables scholars to build innovative theories that account for the intricacies of algorithmic decision‐making.