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Industrial robots are extensively deployed to perform repetitive and simple tasks at high speed to reduce production time and improve productivity. In most cases, a compliant gripper is used for assembly tasks such as peg-in-hole assembly. A compliant mechanism in the gripper introduces flexibility that may cause oscillation in the grasped object. Such a flexible gripper–object system can be considered as an under-actuated object held by the gripper and the oscillations can be attributed to transient disturbance of the robot itself. The commercially available robots do not have a control mechanism to reduce such induced vibration. Thus, this paper aims to propose a contactless vision-based approach for vibration suppression which uses a predictive vibrational amplitude error-based second-stage controller.

The proposed predictive vibrational amplitude error-based second-stage controller is a real-time vibration control strategy that uses predicted error to estimate the second-stage controller output. Based on controller output, input trajectories were estimated for the internal controller of the robot. The control strategy efficiently handles the system delay to execute the control input trajectories when the oscillating object is at an extreme position.

The present controller works along with the internal controller of the robot without any interruption to suppress the residual vibration of the object. To demonstrate the robustness of the proposed controller, experimental implementation on Asea Brown Boveri make industrial robot (IRB) 1410 robot with a low frame rate camera has been carried out. In this experiment, two objects have been considered that have a low (<2.38 Hz) and high (>2.38 Hz) natural frequency. The proposed controller can suppress 95% of vibration amplitude in less than 3 s and reduce the stability time by 90% for a peg-in-hole assembly task.

The present vibration control strategy uses a camera with a low frame rate (25 fps) and the delays are handled intelligently to favour suppression of high-frequency vibration. The mathematical model and the second-stage controller implemented suppress vibration without modifying the robot dynamical model and the internal controller.

Industrial robots are extensively used in the robotic assembly of rigid objects, whereas the assembly of flexible objects using the same robot becomes cumbersome and challenging due to transient disturbance. The transient disturbance causes vibration in the flexible object during robotic manipulation and assembly. This is an important problem as the quick suppression of undesired vibrations reduces the cycle time and increases the efficiency of the assembly process. Thus, this study aims to propose a contactless robot vision-based real-time active vibration suppression approach to handle such a scenario.

A robot-assisted camera calibration method is developed to determine the extrinsic camera parameters with respect to the robot position. Thereafter, an innovative robot vision method is proposed to identify a flexible beam grasped by the robot gripper using a virtual marker and obtain the dimension, tip deflection as well as velocity of the same. To model the dynamic behaviour of the flexible beam, finite element method (FEM) is used. The measured dimensions, tip deflection and velocity of a flexible beam are fed to the FEM model to predict the maximum deflection. The difference between the maximum deflection and static deflection of the beam is used to compute the maximum error. Subsequently, the maximum error is used in the proposed predictive maximum error-based second-stage controller to send the control signal for vibration suppression. The control signal in form of trajectory is communicated to the industrial robot controller that accommodates various types of delays present in the system.

The effectiveness and robustness of the proposed controller have been validated using simulation and experimental implementation on an Asea Brown Boveri make IRB 1410 industrial robot with a standard low frame rate camera sensor. In this experiment, two metallic flexible beams of different dimensions with the same material properties have been considered. The robot vision method measures the dimension within an acceptable error limit i.e. ±3%. The controller can suppress vibration amplitude up to approximately 97% in an average time of 4.2 s and reduces the stability time up to approximately 93% while comparing with control and without control suppression time. The vibration suppression performance is also compared with the results of classical control method and some recent results available in literature.

The important contributions of the current work are the following: an innovative robot-assisted camera calibration method is proposed to determine the extrinsic camera parameters that eliminate the need for any reference such as a checkerboard, robotic assembly, vibration suppression, second-stage controller, camera calibration, flexible beam and robot vision; an approach for robot vision method is developed to identify the object using a virtual marker and measure its dimension grasped by the robot gripper accommodating perspective view; the developed robot vision-based controller works along with FEM model of the flexible beam to predict the tip position and helps in handling different dimensions and material types; an approach has been proposed to handle different types of delays that are part of implementation for effective suppression of vibration; proposed method uses a low frame rate and low-cost camera for the second-stage controller and the controller does not interfere with the internal controller of the industrial robot.

The main purpose of this chapter is dual fold: first, to offer an exhaustive examination of migration trends and factors influencing migrants' decisions to relocate. Second, to propose actionable suggestions aimed at minimising the harmful impacts of climate-induced migration originating from the Indian subcontinent.

This chapter adopts an intensive literature review and integrated approach emphasising safety and partaking rights to examine climate prejudices across various dimensions including age, vulnerability, background, gender, geographical location, pay levels, migrant/refugee/internally displaced status and their connections.

An in-depth assessment of evidence surfaced on environmental migrants reveal a complex interplay of social, environmental and personal dynamics collectively shaping migration patterns beyond purely economic considerations. Natural disasters and associated mental shocks, social tensions and safety concerns, livelihood diversification, educational and development opportunities in host region, family pressures, extent of possession of assets and potential health and well-being all pose as vital drivers in environmental migration.

A novel attempt is made to address the interconnected nature of climate change with other crises, exacerbating existing inequalities whilst emphasising the importance of incorporating diverse interdisciplinary and international perspectives, prioritising the voices of the victimised to effectively address the climate emergency.

Policy recommendations encompass enhancing central and local government support by establishing migrant supportive legal frameworks, expanding livelihood diversification programs and factoring in migrant returnee programmes.

The change in the technological environment within the macro-environment factors in recent years affects states, businesses, societies and individuals and concerns not only technology-based sectors but also many fields. In particular, trends such as artificial intelligence, metaverse, robotics, advanced connectivity, the Internet of Things, big data, small data, blockchain, cloud technologies and reality technologies, which are called new technology, are developing very quickly compared to the past and expanding their global usage areas. Creating strategies and policies without considering these factors creates problems in many areas. These problems are marketing, competition, cost, efficiency and productivity.

Reality technologies, which are the research area in this chapter and enable users to interact with the digital world, have a wide application area in the tourism industry. With technological tools such as smartphones and virtual reality (VR) glasses; personalisation, interactive experience, information gathering and decision-making; many different solutions are produced in areas such as education, service and security. Along with its many advantages, the disadvantages of reality technologies and the negative outputs of this transformation are significant for the understanding and future of the subject.

The purpose of this paper is to review the progress made in arc welding automation using trajectory planning, seam tracking and control methodologies.

This paper discusses key issues in trajectory planning towards achieving full automation of arc welding robots. The identified issues in trajectory planning are real-time control, optimization methods, seam tracking and control methodologies. Recent research is considered and brief conclusions are drawn.

The major difficulty towards realizing a fully intelligent robotic arc welding system remains an optimal blend and good understanding of trajectory planning, seam tracking and advanced control methodologies. An intelligent trajectory tracking ability is strongly required in robotic arc welding, due to the positional errors caused by several disturbances that prevent the development of quality welds. An exciting prospect will be the creation of an effective hybrid optimization technique which is expected to lead to new scientific knowledge by combining robotic systems with artificial intelligence.

This paper illustrates the vital role played by optimization methods for trajectory design in arc robotic welding automation, especially the non-gradient approaches (those based on certain characteristics and behaviour of biological, molecular, swarm of insects and neurobiological systems). Effective trajectory planning techniques leading to real-time control and sensing systems leading to seam tracking have also been studied.

The objective of this paper is to compare and evaluate the environmental performance of steel supply chains considering relevant environmental loss factors using Taguchi loss function (TLF) and design of experiments (DOE).

The different environmental loss factors in steel manufacturing supply chain were studied and the significant factors were identified. Their combined contributions along the significant factors were estimated using TLF and DOEs comparing environment losses at different scenarios.

The proposed methodology using TLF and DOEs was applied to three Indian steel manufacturing companies (Company A, Company B and Company C). The Company A with minimal average environmental loss score is found to be operating its supply chain with higher efficiency and has better environmental performance compared to the other two companies (B and C).

The results obtained are based on the study carried out in three Indian steel manufacturing companies. Therefore, the results cannot be generalized.

This paper will definitely show the direction for comparative environmental performance assessment of manufacturing industries in general and steel industries in specific considering environmental loss factors and environmental conditions. It determines individual performance across each environmental loss factor and their combined impact.

Although there is a need to have comparative performance analysis with respect to environmental losses among steel companies in developing countries such as India, but hardly any study has been reported in this direction. This work will definitely add the value to the supply chain literature in general and environment losses in steel manufacturing supply chain in specific.

This study aims to examine the relationship between tourism development and economic growth while considering exports simultaneously. Governments in many countries have been developing and deploying strategies to attract tourism receipts as a means for economic growth. However, assessing the potential impact of tourism on economic growth among large economies is still in its infancy.

Using a vector error correction model framework, this study examines the relationship among exports, gross domestic product (GDP) and tourism receipts (including international tourism receipts and domestic tourism receipts in two separate models) with macro data that covers two recent decades (1994-2013) in China.

The empirical findings confirm the existence of a long-term equilibrium relationship in each of these two tri-variate models. The empirical findings reveal that (1) both tourism-led-growth and export-led-growth hypotheses are supported, (2) the growth rate of tourism receipts exhibit a higher relevance with GDP growth than export growth and (3) the growth rate of international tourism shows a higher relevance with GDP growth than domestic tourism growth.

Using macroeconomic data collected by the Chinese government, the current study employs an advanced econometric methodology to explore the potential benefits of tourism on economic growth in China.

The purpose of this paper is to examine the impact of knowledge of inventory management practices (KIMP) on operational performance (OP) and business performance (BP). This study emphasized understanding the mediational effect of OPs among KIMP and BP in manufacturing firms of Indian small- and medium-sized enterprises (SMEs).

This study analysed the above relationship from the data collected from 351 key officials of 170 Indian SMEs. The detailed analysis, including reliability, validity and testing hypothesis, was done in advanced SmartPLS-SEM 3.3.3 software.

The findings show that KIMP directly influences OP; thus, a company's OP significantly impacts the overall BP. The KIMP has no significant effect on BP. In line with this, the study findings demonstrate a significant, high mediation effect of OPs between KIMP and BP.

The KIMP on OP has been sparsely studied earlier, and neither have they conceptualized in understanding its impact on BP. In such tumultuous times, key manufacturing executives should stress the importance of KIMP as a comprehensive perspective.

This paper aims to bring in augmented reality (AR) into navigation systems to rectify the issues mentioned. This paper proposes an AR enhanced navigation system for location automated teller machine (ATM) counters (AR-ATM) and branches of banks based on user’s choice. Upon selecting the ATM, the navigational path to the destination is drawn from the current location, thereby the user can reach the ATM through the optimal path.

Traditional navigation systems require users to map with the real world environment as and when required and also may lead to incorrect path due to minor difference in distance. The traditional navigation systems’ also does not take into consideration the ergonomics and safety of the user.

In this system, a camera lens is used, which is directed down the street at eye level and the application displays the location of ATMs and bank branches and also provides information about the locations like distance and time through the AR superimposed object.

The application also provides indoor navigation, especially in a multi-storeyed building. Experiments are performed on smartphones that support AR, and the results are promising with no lag in time frame of the real object and virtual object. To determine the factors that regulate the suggested AR tracking mechanism, a quantitative evaluation of the experimental data is also performed. The testing of implemented AR-ATM from the standpoint of end-users is undertaken to evaluate real-time usage comfortability, and the results have been determined to be extremely satisfactory.

The study aims to optimize truck routes by minimizing social and economic costs. It introduces a strategy involving diverse drones and their potential for reusing at DNs based on flight range. In HTDRP-DC, trucks can select and transport various drones to LDs to reduce deprivation time. This study estimates the nonlinear deprivation cost function using a linear two-piece-wise function, leading to MILP formulations. A heuristic-based Benders Decomposition approach is implemented to address medium and large instances. Valid inequalities and a heuristic method enhance convergence boundaries, ensuring an efficient solution methodology.

Research has yet to address critical factors in disaster logistics: minimizing the social and economic costs simultaneously and using drones in relief distribution; deprivation as a social cost measures the human suffering from a shortage of relief supplies. The proposed hybrid truck-drone routing problem minimizing deprivation cost (HTDRP-DC) involves distributing relief supplies to dispersed demand nodes with undamaged (LDs) or damaged (DNs) access roads, utilizing multiple trucks and diverse drones. A Benders Decomposition approach is enhanced by accelerating techniques.

Incorporating deprivation and economic costs results in selecting optimal routes, effectively reducing the time required to assist affected areas. Additionally, employing various drone types and their reuse in damaged nodes reduces deprivation time and associated deprivation costs. The study employs valid inequalities and the heuristic method to solve the master problem, substantially reducing computational time and iterations compared to GAMS and classical Benders Decomposition Algorithm. The proposed heuristic-based Benders Decomposition approach is applied to a disaster in Tehran, demonstrating efficient solutions for the HTDRP-DC regarding computational time and convergence rate.

Current research introduces an HTDRP-DC problem that addresses minimizing deprivation costs considering the vehicle’s arrival time as the deprivation time, offering a unique solution to optimize route selection in relief distribution. Furthermore, integrating heuristic methods and valid inequalities into the Benders Decomposition approach enhances its effectiveness in solving complex routing challenges in disaster scenarios.