With diverse mobile fleets comes an extended need for communication between the different types and brands of robots. Interoperability refers to the ability of computer systems or programs to exchange information. At the moment, there is a clear lack of industry-wide adoption of interoperability practices — especially when it comes to mobile robot fleets. 

The need for interoperability is driven by two main factors: the growing demand for mobile robots and the diversification of robotic fleets. In this article, we will discuss how a universal fleet management system can address these interoperability challenges.

Growing Demand for Mobile Robots

With the tremendous growth of the robotics industry, the global robotics market is now estimated to reach USD 209.38B by 2025, growing at a 26% Compound Annual Growth Rate (CAGR). While such market growth offers many opportunities, it also comes with quite some challenges — especially with the ongoing adoption of Autonomous Mobile Robots (AMRs). 

With this increasing demand for mobile robots, it is no surprise that many warehouses around the world are automating their facilities. In fact, the warehouse automation market is projected to reach a value of USD 30B in 2026 at a 10.41% CAGR during the forecast period — up from USD 15B back in 2019.

Robotics Diversification Trend

With technological advances, mobile robots are seeing new levels of specialization as they are now capable of serving more specific use cases. The growing demand and diversification of mobile robots will result in manufacturers not being able to keep up with the demand. This means that companies will need to buy robots from multiple manufacturers to fulfill their operational needs. 

This will result in even more diverse mobile robot fleets. We can already see now that many warehouses, hospitals, logistic centers, and factories are deploying different types of robots from different manufacturers. They could have a specific type of robot for lifting heavy items, one from a different brand for lifting small items, another one for security, one for cleaning, etc. 

As for now, this can cause real problems for warehouse operations. Each manufacturer supplies its robot with its own operating system and when one of their robots is introduced to a fleet with robots from other manufacturers, these robots would not be able to communicate with each other. It is of great importance that as robots become more autonomous, they need to begin to communicate effectively to avoid collisions, serious accidents on the work floor, delays in operations, and so forth. 

Interoperability Pain Points

In their recent report “Robotics Interoperability: A solution to the communication issues of diverse mobile robot fleets”, Meili Robots have included a case study that explores the pain points of interoperability.

The project was carried out at a Danish hospital that has deployed different types of mobile robots from different brands. The project aimed to test, adjust, and customize a fleet management solution. It was no surprise that the outcome showed that the robots’ own independent control systems were unable to integrate with the hospital’s logistics system or the operating systems of other robots within the fleet. 

This proved that there is a need for a universal fleet manager that offers a full overview of the entire fleet — meaning all types and brands of robots — with detailed information on the individual robots as well as data analytics features. It is crucial that, in order to avoid collisions or other accidents, third-party robots’ routes, speed, locations, etc. can be controlled in a leveled way. This will also optimize operational efficiency. 

If you would like a more in-depth analysis of the importance of interoperability in the robotics industry, you can download the full report for free here. 

A Universal Fleet Management System for Mobile Robots and Its Benefits

What Is a Universal Fleet Management System?

A universal fleet management system is a system that can perform the complete and centralized management of a fleet of mixed vehicles. These vehicles can be of different types, brands, or sizes — in this case, a fleet of mixed mobile robots. 

These types of systems can include multiple important features such as task management, fleet monitoring (the location of each robot, statuses, battery levels, missions in progress, etc.), route planning, traffic control, and/or data analytics. Some advanced systems also incorporate artificial intelligence and machine learning to bring their functionalities to another level of automation, making their systems smarter and more effective while at the same time saving operational costs and energy. 

3 Major Benefits of a Universal Fleet Management System

In addition to the benefits mentioned above, there are three significant advantages of a universal fleet management system that are worth taking into consideration. Let’s take a look at them.

Increase Operational Efficiency

With a universal fleet management system like Meili FMS, you can easily detect disruptions or other issues as they occur and notify the prospective robots in real-time. Via the comprehensive overview that comes with a universal fleet manager, you can not only respond to problems more quickly and remotely, but you can also reduce the downtime of your robots significantly while simultaneously increasing the efficiency of your fleet.

Optimize Operational Safety

Traffic control is one of the most essential features of a fleet management system as it enables your entire fleet to detect other robots, forklifts, human workers, and blocked areas — making it easier for them to detect and predict bottlenecks. For example, rather than driving right into a predicted bottleneck and causing collisions, the robot can now automatically create a new route, divert, and reach its destination in a safer and more efficient way.

Accelerate Sustainable Operations

It happens too often that robots are driving around a facility without a payload, wasting lots of energy. Through its smart tasking algorithms, a universal fleet manager like Meili FMS provides you with an automated task allocation feature. This allows you to assign tasks to the right robots at the right time and eliminate unnecessary fleet idle time — thereby, saving lots of energy and improving your sustainability records.

Final Thoughts

Evidently, the robotics industry is facing a major challenge: the demand for robots keeps growing while robots are also becoming more and more specialized — meaning that due to their unique, individual operating systems, they cannot communicate amongst each other. 

In order to enable this communication and prevent operational delays, accidents, and collisions, a universal fleet management system is needed. Not only will this optimize operational processes and increase profits, but it will also help scale businesses to the next level.

The post Universal Fleet Management System: The Answer to Robotics Interoperability Issues first appeared on AITechTrend.

Our hope is to use this comprehensive review to explain AI to an audience of politicians, opinion leaders, and interested observers, and to show how AI has changed the world and raised important questions for society, the economy, and governance.

Challenges that AI will face

Nonetheless, in a world increasingly predisposed to technological innovation, there are already concerns that AI is already being applied to decision-making processes, which may have negative consequences.

Below are the challenges that can potentially arise in the use of AI:

• Dangerous Investments For several years now, a heated debate has raged regarding the ethical implications of AI and how to regulate it.
• AI systems, which often learn from experience, may one day overrule existing laws and morals. As the prevalence of AI spreads, so too will the impact of its decisions on the real world.
• Faulty ML and AI Another significant challenge are that while today’s AI is impressive in its ability to learn, it lacks the ability to think critically.

The internet of things

The industry trend has seen the rise of “the internet of things” (IoT) with the seemingly exponential growth of connected devices that increasingly gather data to improve the way in which a variety of industries operate. One such IoT-driven industry is banking and financial services. Today, AI is now widely used in such industries to ease operations and provide customer experience.

However, there are many challenges that will face this new age of banking.

AI in the workplace

IT departments are continuing to build and implement AI in their workplace initiatives, as reported in a recent survey. These efforts are being undertaken by organizations of all sizes, and while many businesses are still learning about the technology and its potential, others are getting a better understanding of how it works and what it can do for them. The survey results indicate that even though the use of AI has started to emerge in many business situations, its future is still far from guaranteed. 

AI and the military

Most military agencies are now using AI to improve their arsenal of military weapons and to improve the outcome of their military actions. This use of AI was thrust to the fore after Russia recently publicly stated that their military agencies will incorporate AI within their arsenal by 2027.

As Russia is the largest nuclear power in the world, this move had the military cognoscenti examining the implications of AI for warfare and the future of nuclear weapons. One of the early adopters in the military application of AI is Russia. So far, over 20 military AI systems have been developed, and some of them have already been introduced in active service.

These military systems are being used to collect, analyze and disseminate information in the interests of the Russian military.

AI and the medical field

One of the latest AI-enabled medical breakthroughs is the development of a device that could speed up the recovery of stroke patients. Called the Neurological Assist Device (NAD), this lightweight robot, fitted with a flexible scalp electrode and neck connector, uses an advanced artificial intelligence system to study a patient’s brain signals and simulate a 12-month-long seizure.

Once it has simulated a seizure for a certain period of time, it connects to the patient’s machine and applies an electric current that helps the patient recover.

Currently, the NAD device has already been successfully tested in Switzerland, but the health ministry of Switzerland is not expected to approve the device until December 2019.

AI in warfare

AI is making headlines and has already been at the heart of the biggest conflicts in history. The AI Arms Race is making itself felt, with the power to produce weapons that are able to predict our movements, covertly eliminate our enemies and even predict our movements.

Vinod Nair, Head, Corporate, Middle East, India, and Africa, IBM, shares his views on the challenge that Artificial Intelligence has to face. With so many artificial intelligence technologies, what can one do that one cannot do? There are as many definitions of AI as there are of machines. However, the one that tends to be generally agreed upon is that AI systems will work intelligently alongside humans in a variety of domains.

AI and the environment

Despite the potential of AI and its impact on our lives, it is not without challenges. AI uses data to improve itself and make increasingly complex predictions about the future. This means that many of the problems it is confronted with are purely academic, too vast in nature to be solvable by humanity alone. In fact, solving these AI-specific challenges is in some respects a kind of capstone to AI research.

For instance, research for the Large Scale Visual Analytics challenge, which aims to develop an AI-enhanced system to identify dangerous wildfires, has recently wrapped up.

The system’s accuracy was 86%, which fell short of the researchers’ target of 90%. However, in other cases, research is advancing toward solving fundamental problems in the field.

Conclusion

In this article, we have discussed the top AI challenges that we should all be aware of to make sure that the benefits of implementing AI systems outweigh the risks and challenges. Do you think you can overcome the challenges listed above? Let us know in the comments below.

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