Enterprises are sold on automation. Compared to legacy processes, automation results in greater productivity, even 24/7 operations, if desired. Furthermore, work is efficient and consistent — machines don’t lose focus near the end of a shift. Additionally, automated tasks are less dependent on specific employees holding on to tribal knowledge that helps them do their jobs well, and when employee turnover occurs, employees don’t take that information with them.
Enterprises have realized that automation overcomes those challenges. However, what enterprises haven’t fully discovered is that benefits from automation can grow exponentially when they create autonomous processes with smart solutions that work together throughout their entire operations.
Operations tend to automate processes iteratively in steps such as:
- Beginning with manual processes, completely reliant on humans to perform work
- Next, providing tools to assist employees to do their jobs more efficiently or accurately, creating a semi-automated stage in which there are only minimal changes to standard operating procedures
- Then, shifting to enable machines to do more work, further increasing efficiency, reducing costs, and increasing throughput, and moving employees to inspector or operator roles.
- Processes evolve from “automated” to “autonomous,” and human intervention becomes less and less necessary as machine learning (ML) and other forms of artificial intelligence (AI) recognize patterns and make decisions.
- Finally, fully autonomous processes emerge with systems that learn, “think,” and make decisions or take actions on their own.
When a process is autonomous, a human doesn’t need to tell the machine what to do next. The machine knows when to work faster or stop production to prevent damage or hazards. It also knows when things are working as they should and when they aren’t – and how to get processes back on track.
Enterprises with disparate automated processes, autonomous systems, and robots will benefit from the emerging phase in creating efficient operations: swarm autonomy.
This next phase of the evolution of autonomous processes connects smart systems to use the data they collect and generate to inform other processes throughout the operation. An example is enabling packing machines to communicate with autonomous mobile robots (AMRs) that bring them supplies such as boxes and tape – and even allowing the AMRs to communicate with each other. Within the swarm autonomy model, machines work together to create seamlessly orchestrated processes.
Innovators are creating solutions that bring the vision of symbiotic autonomy to life. For example, FARobot Inc., the joint venture of ADLINK and Hon Hai Technology Group (Foxconn) was established in 2020 to address the ever-growing needs in manufacturing and logistics automation to enhance production agility. It developed World’s first Swarm Autonomy solution by simulating the natural swarm principle operated by bees or ants, enabling robots’ collective material handling in dynamic and unstructured shop floors with safe and harmonized navigation, and streamlined intralogistics processes.
Backed by Swarm Core platform the robotic orchestration software, and the Swarm Mobile Robots (SMR) series, Swarm Autonomy solidarizes a group of SMRs, multi-brand AMRs, AGVs, and material transport equipment to work together for optimized efficiency. The Swarm Core platform enables plug-and-play deployment of a hybrid fleet, therefore highly scalable for manufacturers of any size.
Another example is the Fair Friend Group’s smart factory. The global tool manufacturer wanted to improve flexibility, efficiency and cost control when faced with labor shortage challenges and evolving demands that required logistics upgrades. The manufacturer used Distributed Data Service (DDS) as middleware combined with private 5G low-latency and high-speed transmission to give its fleet of AGVs the responsiveness and collaboration capabilities. Fair Friend Group’s first swarm autonomy implementation enabled AMRs to transport parts and components to inspection sites.
ROS 2 has been instrumental in enabling autonomous systems. It expands on ROS capabilities by facilitating support for multi-robot systems, safety certifications, and security. It also supports edge computing, which helps solve latency and bandwidth challenges, and it’s open source, so solution architects can add functionality or components and extract all data types necessary. Additionally, DSS is key to enabling data to flow from machine to machine, machine to enterprise system – as well as machine to human – in autonomous processes.
AMRs, AGVs, and other machines on the move in autonomous processes also require simultaneous localization and mapping (SLAM) that enables the robot or vehicle to follow a path or avoid obstacles. The right hardware and components are also necessary to give autonomous systems the ability to understand their environments and the task at hand. Smart camera systems and sensors, for example, give autonomous mobile robots “eyes” so they can pick and deliver the correct items.
Another consideration in creating a future-proof autonomous process is scalability. It may seem that more robots designed for more tasks would increase production and efficiency. However, without considering how the system will scale, you could encounter path and sensor interference that limit – or detract from – performance. ADLINK addresses the challenge of scalability with the ROScube Series robot controllers for advanced robotics applications. It includes high-performance edge AI analytics to support autonomous processes in dynamic environments.
Although environments of fully connected autonomous processes are now goals rather than realities for manufacturers, the exciting news is that all of the technology necessary to create them is available. Add your creativity and talent for innovation, and you can move closer toward autonomous operations today.
Learn more about how a partnership with ADLINK will support autonomous processes.