Skip to main content

Global Industrial Robotics Revenue will Reach $22 Billion

The industrial and collaborative robotics market is gaining momentum, as more vendors and industries embrace automation. Development in cloud robotics, deep learning based machine operation, and a wider ecosystem will enable robots to become more reliable, versatile and efficient.

The industrial robotics sector is already experiencing robust growth. The revenues of commercial robots in manufacturing are forecasted to grow from $166 million in 2018 to $22 billion by 2027, according to the latest worldwide market study by ABI Research.

Industrial Robotics Market Development

The newest trend is complementary robotics technologies that put mobile robots on the factory floor. Made up of automated guided vehicles (AGVs) and autonomous mobile robots (AMRs), these robots will complement existing robotic arms in factories that are increasingly becoming more autonomous and smarter.

There has been plenty of debate within the industry on the different benefits of AGVs and AMRs. While AGVs are a much cheaper precursor to AMRs, they require floor markers to guide their movement and are more ideal in greenfield deployments. For those wanting infrastructure-free navigation and flexible production line, AMRs represent the future standard.

"The advancements in machine vision, simultaneous localization and mapping (SLAM), swarm intelligence, and sensor fusion are making it possible for mobile robots to operate in unstructured environments such as the factory warehouse and the assembly area," said Lian Jye Su, principal analyst at ABI Research.

These technologies are being supported by many cameras and sensors, such as LiDAR and radar. Moving forward, the robot can benefit from the integration of deep learning algorithms with sensor fusion and swarm intelligence.

In addition, as factories undergo digital transformation, more factories will start to adopt smart manufacturing platforms. With this development, the value proposition of cloud robotics becomes more relevant.

However, there are still many challenges related to the adoption and deployment of cloud robotics. Data security, data analytics, and virtualization services must be in place before connecting any robot to an industrial cloud platform.

Outlook for More Industrial Robotics Applications

As robotic technologies continue to mature, different vendors are starting to engage in ecosystem play. Universal Robot, the world’s largest collaborative robot arm vendor, has its own ecosystem called UR+, which features over fifty partners in grippers, accessories, and software platforms.

According to the ABI assessment, industrial factory embrace of collaborative robots, AGVs, and AMRs indicates that manufacturers are also embracing versatility and modularity.

"The increasing number of stock keeping units (SKUs) and short product life cycles necessitate the deployment of robotics solutions that can be retrained and redeployed for different manufacturing processes and factory layouts," Su concluded.

Popular posts from this blog

Securing the Future of Cellular IoT Apps

The Internet of Things (IoT) continues to expand. According to the latest worldwide market study by Juniper Research, they forecast a 90 percent growth in cellular IoT devices by 2028, with the global number reaching 6.5 billion. This exponential rise presents both exciting opportunities and significant challenges. While the growth of cellular IoT unlocks a vast potential for innovation in smart cities, industrial automation, and remote monitoring, it also requires device management and security advancements. Cellular IoT Market Development Juniper's research highlights the critical role of intelligent infrastructure management solutions. These platforms will empower the users to automate critical tasks such as device configuration, real-time security management, and optimized wireless connectivity. The surge in cellular data usage, projected to reach 46 petabytes by 2028 compared to 21 petabytes today, further underscores the need for automation. This is where federated learning i