As one of the core parts of flexible manufacturing systems, material handling involves storage and transportation of materials between workstations with automated vehicles. The improvement in material handling can impulse the overall efficiency of the manufacturing system. However, the occurrence of dynamic events during the optimisation of task arrangements poses a challenge that requires adaptability and effectiveness. In this paper, we aim at the scheduling of automated guided vehicles for dynamic material handling. Motivated by some real-world scenarios, unknown new tasks and unexpected vehicle breakdowns are regarded as dynamic events in our problem. We formulate the problem as a constrained Markov decision process which takes into account tardiness and available vehicles as cumulative and instantaneous constraints, respectively. An adaptive constrained reinforcement learning algorithm that combines Lagrangian relaxation and invalid action masking, named RCPOM, is proposed to address the problem with two hybrid constraints. Moreover, a gym-like dynamic material handling simulator, named DMH-GYM, is developed and equipped with diverse problem instances, which can be used as benchmarks for dynamic material handling. Experimental results on the problem instances demonstrate the outstanding performance of our proposed approach compared with eight state-of-the-art constrained and non-constrained reinforcement learning algorithms, and widely used dispatching rules for material handling.

This work aims at showing that it is feasible and safe to use a swarm of Unmanned Aerial Vehicles (UAVs) indoors alongside humans. UAVs are increasingly being integrated under the Industry 4.0 framework. UAV swarms are primarily deployed outdoors in civil and military applications, but the opportunities for using them in manufacturing and supply chain management are immense. There is extensive research on UAV technology, e.g., localization, control, and computer vision, but less research on the practical application of UAVs in industry. UAV technology could improve data collection and monitoring, enhance decision-making in an Internet of Things framework and automate time-consuming and redundant tasks in the industry. However, there is a gap between the technological developments of UAVs and their integration into the supply chain. Therefore, this work focuses on automating the task of transporting packages utilizing a swarm of small UAVs operating alongside humans. MoCap system, ROS, and unity are used for localization, inter-process communication and visualization. Multiple experiments are performed with the UAVs in wander and swarm mode in a warehouse like environment.

Human-technology collaboration relies on verbal and non-verbal Christopher Reining communication. Another field of application is the detection of hand Increasing automation in production and logistics movements. This not only facilitates human-machine with simultaneous increases in the complexity of manual interaction [3] but also communication in the context of processes is leading to more and more interaction recognising sign language. Synergetic collaboration application, the industry deserves special mention. While fitness trackers use HAR human factor is still a crucial variable, which is wrongly to distinguish between step and no step, the movements assumed to be deterministic in planning and simulation to be analysed in the industry are more complex. The time data is necessary to implement a datadriven movements become more complex and more detailed, simulation that considers the non-deterministic automatic recognition of activities will become less motion behaviour of humans.

Moving products from delivery trucks to storage areas, then to shelves, is hazardous work. Forklift incidents, lifting injuries and falling objects are some of the hazards workers face. Data from the Bureau of Labor Statistics shows that 10 occupations accounted for 33.2% of all private industry cases involving days away from work in 2018 and 2019. Of these, laborers and freight, stock, and material movers (hand) had the highest number of DAFW cases: 64,160. So, how can employers prevent injuries among warehouse workers or other workers who move materials?

Drives play an essential role in automation technology. Stating its importance, K T Chougule, DGM – drive solutions, factory automation & industrial division, Mitsubishi Electric India says, "Generally 70% of the electrical load is motor load in the industry and to run the motor with variable speed according to the machine/process application requires a drive. VFDs also play a critical role in energy saving particularly in variable torque application, where energy is saved considering the difference between process demand & supply, mainly for fan & pump application. Energy saving market is growing very fast in India due to relatively high cost of power in India." Further he adds, "Drive not only gives variable speed, it can also deliver smooth start-stop, which reduces mechanical impact on machines or equipment, improves the power factor of the system and also reduces the maximum demand (MD) of factory Drive plays a key role in the automation technology, and the drive market is more than 50% of total factory automation market."

Over the last few years, automation has gained immense popularity in various industries. And the manufacturing industry sits on the cutting edge of automation technology, ahead of other industries. With automation, manufacturing companies have revolutionized the way activities were once performed. With autonomous systems coming into the picture, human participation and involvement have reduced significantly. As a result, work gets done faster and more importantly, with greater precision. Though automation changes the nature of employment, it has brought in huge profit opportunities to the manufacturing industry.

Tencent, Alibaba, Baidu and JD.com from China are in a global competition with Google/Alphabet, Apple, Facebook, Walmart and Amazon from the USA and SoftBank from Japan. All are agressively searching for talent, intellectual property, market share, logistics and supply chain technology, and presence all around the world. These leading tech-savvy companies have many things in common. Foremost, they are all in pursuit of global growth and the funding, technology and talent to propel that growth. And they all are investing in voice assistance and other forms of AI and robotics.

It's a good sign for the robotics industry that more and more robotics companies are starting to make major announcements at specialized events and trade shows, indicating that their robots are ready for tough, real-world applications. This week at ProMat, "the premier showcase of material handling, supply chain, and logistics solutions," Fetch Robotics is showing off two very new, and very large, stuff-transporting robots. This video shows the Freight 500, which can handle 500 kilograms of payload, or generally something about the size of a "case," which I guess is a standard unit in the area of "material handling, supply chain, and logistics solutions." The Freight 1500 weighs just under 470 kg all by itself, but it's only 35.5 centimeters (14 inches) tall, which is the same height as its smaller siblings. It has lidar sensors front and back, a forward-looking RGBD camera, and can run for up to 9 hours while recharging itself to 90 percent in just an hour.

Ontario tech firm Clearpath Robotics has raised 30 million in a Series B round of venture funding to scale up production of its self-driving vehicles. Intsead, Clearpath makes vehicles that autonomously move boxes and pallets around factories, warehouses and distribution centers. Clearpath CEO and co-founder Matt Rendall tells TechCrunch, "Boxes and pallets moving around the world in the global supply chain are the circulatory system for global commerce. We believe if we can move them more efficiently we can do profound things for the economy." Founded in 2008, originally as a computer vision and robotics engineering consultancy, Clearpath today employs 150 full-time in Kitchener.

It appears, however, that we [in AI] are now (finally!) on the verge of practicality in a number of specialities within machine intelligence more or less simultaneously. This can be expected to result in the short term in a qualitative shift in the nature of the field itself, and to result in the longer term in a shift in the way certain industries go about their businessThis paper will discuss three specific areas of work in machine intelligence that MIC [Machine Intelligence Corporation] feels are ripe for commercial application: machine vision, naturallanguage access to computers, and expert systems. It will close with some observations on what makes these areas appropriate for application at this time, and on the difference between a technical solution to a problem and a product.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.