Mobile Robots: What’s ahead in 2025?
2024 saw mobile robots continue to move past their innovation stage as more industries adopt automation to tackle labor shortages, reduce costs, and increase efficiencies. However, both AGVs and AMRs still face challenges in improving ease of robustness, affordability and simplicity. These are the key trends we expect to see in 2025.
1. Rising adoption and market growth
The mobile robot market is projected to grow significantly, with global shipments on track to reach 2.8 million units annually by 2030. This growth is especially prominent in warehouse automation, where the market size is forecast to rise from $9.33 billion in 2025 to $21.08 billion by 2030.
Larger organisations continue to lead adoption, while smaller businesses remain cautious due to high upfront costs and integration complexity. Overcoming these challenges will be key to unlocking wider adoption.
2. Complexity remains a barrier
Mobile robot systems still require significant configuration, making deployment costly and time-consuming. While AGVs rely on fixed infrastructure like magnetic strips, AMRs offer more flexibility with advanced sensors. However, this shift from line-following to free-moving robots introduces its own challenges.
Localization remains a key hurdle. LiDAR SLAM, the most commonly used localization method, struggles with reflective surfaces and scene changes, limiting robustness. Visual SLAM, while more flexible, faces challenges in dynamic lighting and requires expensive cameras and high computational power. Both technologies, despite their advantages, are not well-suited for the unpredictability of real-world environments.
This year we can expect a growing demand for solutions that balance flexibility and simplicity—offering robust performance, lower costs, and faster deployment without relying on complex infrastructure or high computational demands. This focus on simplicity will be essential for mobile robots to scale and meet the operational needs of diverse industries.
3. A shift towards total cost of ownership
The growing focus on simplicity may start to drive a shift towards evaluating total cost of ownership (TCO) over upfront hardware costs alone. Manufacturers are realizing that the true cost of mobile robots extends beyond the bill of materials to include installation, maintenance, downtime, and the ongoing costs associated with human intervention, teleoperation, and poor Mean Time to Failure (MTTF)
For many robot manufacturers, the complexity of deployment and the ongoing costs of maintaining these sophisticated systems can erode ROI. Solutions that reduce setup time, minimize remapping, and operate efficiently in dynamic environments are increasingly favoured for their ability to lower TCO. This trend will increase the demand for practical, cost-effective solutions that deliver long-term value rather than quick wins.
4. Expanding use cases beyond warehousing
While warehousing remains a key sector for mobile robots, we’ll continue to see increased use in multiple industries, such as agriculture, construction, and last-mile delivery. These dynamic environments introduce a critical challenge—how robots interact with humans in unpredictable, shared spaces. AGVs were designed for structured workflows but struggle in settings with frequent human activity and unexpected changes. Stories of robots caught in loops with humans or requiring “wrangling” highlight the need for systems that adapt to human behavior without frequent intervention.
The demand for adaptive technologies that can handle real-world unpredictability and support effective human-robot interaction is growing. Addressing these challenges will be key to unlocking the next phase of growth and proving the value of mobile robots across industries.
5. A pivotal moment for AI
AI has scaled successfully to a wide variety of tasks and is successfully solving some very serious and valuable problems for humanity. Many have been counting on AI to overcome key hurdles in autonomy, from navigation to decision-making. However, the industry is increasingly recognizing its limitations—cost, complexity, and reliance on vast datasets—along with growing environmental concerns.
As data requirements and operational costs continue to rise, reliance on data-intensive systems is prompting a reassessment from the burden of traditional AI frameworks. We’re likely to see a growing shift towards alternatives as businesses seek smarter ways to integrate mobile robots.
6. Opteran’s focus for 2025
In 2025 our mission is clear: driving Natural Intelligence for machines through neuromorphic software – not data science. As mobile robots expand beyond structured spaces into unpredictable, shared spaces, Natural Intelligence presents a pathway to practical, scalable deployments in the most complex environments.
We’re relentlessly advancing autonomy to deliver:
– Robustness to thrive in every environment
– Affordability to scale without expensive hardware
– Simplicity to deploy without infrastructure
All without the CO₂ generating, synthetic data driven AI being promised.
By harnessing evolution, we can create a data-free foundation model for machines to behave like nature. We can redefine autonomy to make it as robust and efficient as nature—underground, on the ground, in the air and even off-world.
