Advances in AI, sensing and control systems have dramatically expanded what robots can do. The challenge now is ensuring they can do it consistently, safely and predictably over time.
The question is no longer simply whether a robot can perform a task. It is whether it can perform that task reliably over thousands, millions or even billions of operating cycles in real-world conditions.
As deployment becomes more widespread, reliability is becoming a key factor in determining whether robotics technologies can scale successfully.
Robotics applications vary dramatically, but the underlying reliability challenge is remarkably consistent. For a humanoid robot, every actuator, sensor, controller, joint and structural component must work together as the system performs complex tasks in unpredictable environments. A weakness in one subsystem can affect overall performance, safety and confidence in the technology.
Some humanoid robotics developers are therefore embedding Design for Reliability (DfR) methodologies throughout development, using tools such as ReliaSoft to assess reliability at both component and system level.
The situation is different for organisations operating large fleets of warehouse robots. Here, reliability, availability, and maintainability (RAM) become the focus across hundreds or thousands of assets, where even relatively small issues can escalate into significant operational disruption.
One major warehouse automation organisation uses ReliaSoft RAM analysis tools as part of its reliability engineering approach for managing a large robotic fleet. This helps teams understand how reliability, maintenance and availability combine to influence performance at scale.
In medical robotics, reliability directly affects precision, safety, regulatory compliance and confidence throughout the product lifecycle.
A global medical technology organisation developing robotic surgical systems uses the ReliaSoft DfR suite as part of its development process. Weibull++ supports component life and test data analysis, while XFMEA helps teams identify and mitigate potential risks, supporting the rigorous reliability and compliance requirements of medical technology development.
Subsea robotics presents a different set of challenge. Systems operate in harsh environments where access is difficult, maintenance opportunities are limited and failures can be extremely costly.
One developer of advanced seabed imaging technology uses ReliaSoft BlockSim to analyse equipment reliability and develop Reliability Block Diagrams (RBDs), helping engineers understand how individual equipment failures could affect overall mission capability.
Different applications. Different environments. Different risks.
But the objective remains the same: to build systems that continue to perform as expected throughout their operational life.
Historically, reliability has often been validated later in development: build a prototype, test it, identify failures and improve the design.
For modern robotics, that approach is increasingly difficult to sustain. Today's robotic systems combine mechanical structures, electronics, sensors, software, communications and control algorithms within highly interconnected architectures. As complexity grows, so does the cost of discovering problems late in the development process.
Many engineering organisations are now moving reliability activities further upstream.
Design for Reliability provides a systematic approach to identifying and reducing risk before systems reach deployment. Techniques such as Failure Mode and Effects Analysis (FMEA), reliability prediction and life data analysis help teams understand potential weaknesses earlier, prioritise testing and make more informed design decisions.
Instead of relying on failures to expose weaknesses, teams can design reliability into a system from the very beginning.
A robot is more than a collection of reliable parts. It is an interconnected system in which motors, actuators, gearboxes, power systems, sensors, electronics, communications, software and structural elements all contribute to overall performance.
For this reason, system-level reliability analysis is playing a growing role in robotics development.
Reliability Block Diagrams (RBDs), Fault Tree Analysis (FTA), RAM analysis and availability modelling help engineers understand how failures propagate and how individual components affect overall system performance.
For a subsea robotics developer, that might mean modelling how the loss of a single subsystem affects mission capability.
For a humanoid robotics developer, it could mean identifying which actuators, joints or electronic components have the greatest influence on overall reliability.
For a warehouse automation provider, it may involve evaluating fleet availability and understanding how maintenance strategies and repair times affect uptime.
These techniques help answer questions that component-level testing alone cannot address.
Modern robots generate enormous volumes of data on loads, vibration, temperature, torque, movement, duty cycles and environmental conditions.
But collecting data does not, by itself, make a system more reliable. Its value lies in how that data is used within a structured reliability engineering process.
Life data analysis can help teams understand how components behave over time. Reliability models can be updated as new evidence becomes available. Operational data can reveal unexpected loading conditions, emerging failure trends and weaknesses in original design assumptions.
This creates an ongoing connection between development and operation: predict, test, observe, learn and improve. As robots become more autonomous and operate across more diverse environments, the link between operational data and reliability engineering will play an increasingly important role.
As robotics deployments grow, reliability becomes both operational and an economic consideration.
When an organisation operates thousands of robots, relatively minor differences in component reliability, repair time or maintenance strategy can have a significant impact on system availability and productivity.
This brings Reliability, Availability and Maintainability (RAM) into sharper focus.
Engineering teams need to understand not only how often a component or system may fail, but also what happens when it does. How quickly can it be repaired? Is redundancy available? What maintenance strategy will maximise uptime? What effect does downtime have on overall fleet performance?
These questions are especially relevant in sectors such as warehouse automation, manufacturing and logistics, where robotic assets are directly linked to operational throughput.
In these environments, reliability engineering extends beyond product design and becomes an integral part of operational planning.
Across the robotics industry, reliability is becoming the factor that separates impressive demonstrations from technologies capable of sustained real-world deployment. Humanoid robotics developers need confidence that complex systems will perform consistently beyond the laboratory. Warehouse operators depend on robotic fleets that deliver high availability and productivity. Medical robotics manufacturers require precision, safety and confidence throughout the product lifecycle. Developers of autonomous marine and industrial systems need assurance that critical assets can continue operating where maintenance opportunities are limited.
In each case, reliability influences scalability, availability, lifecycle cost, maintenance requirements, customer confidence and ultimately commercial success.
Organisations that prioritise reliability early in development can reduce redesign risks, improve uptime, make more informed engineering decisions and build stronger evidence of product performance.
Reliability is no longer just an engineering consideration. It is becoming a factor in how effectively they scale, compete and deliver long-term value.
The robotics industry has made significant advances in intelligence, autonomy and capability. But the next wave of innovation will not be defined solely by what robots can do. It will be defined by how reliably they can do it.
Whether developing humanoid robots, managing warehouse fleets, advancing robotic surgery or deploying autonomous subsea systems, organisations face a common challenge: creating systems that can be trusted to perform consistently over time.
That requires reliability to be considered throughout the lifecycle, from early concept and component selection through system design, testing, deployment and operation.
The organisations that succeed in the next decade of robotics will not simply build the smartest robots.
They will build the ones people can depend on.
ReliaSoft by HBK helps engineering teams predict, analyse, improve and manage reliability throughout the product lifecycle.
From Design for Reliability, FMEA and life data analysis to Reliability Block Diagrams, RAM analysis and system reliability modelling, ReliaSoft provides a structured framework for understanding risk and supporting more informed engineering decisions.
Across applications ranging from individual robotic components to complex systems and large operational fleets, these tools help organisations address reliability from development through deployment.
This will bring together HBM, Brüel & Kjær, nCode, ReliaSoft, MicroStrain and Discom brands, helping you innovate faster for a cleaner, healthier, and more productive world.
