Futuristic factory floor with humans and robots collaborating seamlessly.

Safer Robots, Happier Humans: How Sensor-Free Tech Could Revolutionize Workplace Safety

Theo Raines in Tech & Innovation May 2025 • 4 min read.

"Unlock the potential of collaborative robots with groundbreaking sensor-less force detection for safer, more intuitive human-robot interactions."

Imagine a factory floor where robots and humans work side-by-side, seamlessly collaborating to assemble products, move materials, and perform intricate tasks. This vision of human-robot collaboration (HRC) is rapidly becoming a reality, promising increased efficiency, improved safety, and a more engaging work environment. But to truly unlock the potential of HRC, we need robots that can sense and respond to their human partners in a safe and intuitive way.

Traditionally, achieving this level of responsiveness has required equipping robots with a variety of external sensors, such as force/torque sensors, tactile sensors, and vision systems. These sensors provide robots with the data they need to detect human presence, understand human intent, and react appropriately to unexpected contact. However, adding these sensors can be costly, complex, and difficult to integrate into existing robotic systems.

Now, a groundbreaking new approach is emerging that eliminates the need for external sensors altogether. This sensor-less force detection technology uses sophisticated algorithms and dynamic models to allow robots to "feel" their environment and detect external forces without relying on additional hardware. This innovative approach promises to make collaborative robots more accessible, affordable, and adaptable, paving the way for a new era of human-robot collaboration.

Why Sensor-less Force Detection is a Game-Changer for Industrial Robots

Futuristic factory floor with humans and robots collaborating seamlessly.

The core idea behind sensor-less force detection is to leverage the robot's existing internal sensors – such as joint position sensors and actuator current/torque sensors – to infer information about external forces acting on the robot. By analyzing the data from these sensors in conjunction with a detailed dynamic model of the robot, it's possible to estimate the magnitude and direction of external forces without the need for dedicated force sensors.

Here's how this technology works in practice:

Dynamic Modeling: A detailed dynamic model of the robot is created, capturing its inertial properties, friction characteristics, and other relevant parameters.
Parameter Identification: The parameters of the dynamic model are identified using advanced algorithms and experimental data.
Torque Residual Analysis: The difference between the predicted torque (based on the dynamic model) and the measured torque (from the robot's internal sensors) is analyzed to estimate external forces.
Threshold Detection: A threshold is set to distinguish between normal operating forces and external forces that may indicate human contact.

When an external force is detected, the robot can respond in a variety of ways, such as stopping its motion, adjusting its trajectory, or alerting a human operator. This allows the robot to work safely and collaboratively with humans in a shared workspace.

The Future of Human-Robot Collaboration is Sensor-Free

Sensor-less force detection technology has the potential to revolutionize human-robot collaboration, making industrial robots safer, more adaptable, and more accessible. By eliminating the need for external sensors, this innovative approach reduces costs, simplifies integration, and opens up new possibilities for collaborative manufacturing, assembly, and other industrial applications. As the technology continues to develop and mature, we can expect to see sensor-less force detection playing an increasingly important role in the future of human-robot collaboration, creating safer and more efficient workplaces for everyone.

Everything You Need To Know

What exactly is Human-Robot Collaboration, and why is it becoming so important in manufacturing?

Human-Robot Collaboration (HRC) refers to scenarios where robots and humans work together in a shared workspace to achieve common goals. This collaboration can involve tasks such as assembly, material handling, and other intricate processes. HRC aims to combine the strengths of both humans (e.g., adaptability, problem-solving) and robots (e.g., precision, strength) to improve efficiency, safety, and create more engaging work environments. The implications of effective HRC include increased productivity, reduced risk of workplace injuries, and the potential for new and innovative manufacturing processes. The rise of HRC also necessitates the development of advanced safety measures and technologies, such as sensor-less force detection, to ensure the well-being of human workers.

Can you explain what sensor-less force detection is and why it's considered a game-changer for industrial robots?

Sensor-less force detection is a technology that allows robots to perceive external forces acting upon them without using dedicated external sensors. It relies on analyzing data from the robot's internal sensors, such as joint position sensors and actuator current/torque sensors, along with a detailed dynamic model of the robot. This dynamic model captures the robot's inertial properties and friction characteristics, which are then used to predict the expected torques. By comparing the predicted torques with the actual measured torques, the system can estimate the magnitude and direction of external forces. Sensor-less force detection is significant because it reduces the cost and complexity associated with traditional force-sensing methods, making collaborative robots more accessible and adaptable.

What is a 'dynamic model' in the context of sensor-less force detection, and why is it so important?

The dynamic model in sensor-less force detection is a detailed mathematical representation of the robot's physical properties and behavior. It includes parameters such as the robot's mass, inertia, friction coefficients, and joint stiffness. Creating an accurate dynamic model is crucial for sensor-less force detection because it allows the system to predict the expected torques and forces acting on the robot under normal operating conditions. This prediction is then compared with the actual measured torques to estimate external forces. The accuracy of the dynamic model directly affects the sensitivity and reliability of the sensor-less force detection system. Parameter identification is a key part of developing a good dynamic model.

How does 'torque residual analysis' work in sensor-less force detection, and what does it tell us?

Torque residual analysis is the process of comparing the predicted torque (calculated using the dynamic model) with the measured torque (obtained from the robot's internal sensors) to estimate external forces acting on the robot. The difference between the predicted and measured torques, known as the torque residual, represents the external forces that are not accounted for by the dynamic model. By analyzing the magnitude and direction of the torque residual, the system can determine the presence and characteristics of external forces, such as those resulting from human contact. This information is then used to trigger appropriate responses, such as stopping the robot's motion or adjusting its trajectory.

What is 'threshold detection,' and how does it contribute to safety in sensor-less force detection systems?

Threshold detection in the context of sensor-less force detection involves setting a specific value or range to differentiate between normal operational forces and external forces that may indicate unintended contact, such as with a human. When the estimated external force, derived from torque residual analysis, exceeds this pre-defined threshold, the robot recognizes this as a potential safety concern. This triggers a pre-programmed safety response, which could include halting the robot's movements, altering its path, or alerting a human operator. Properly calibrating the threshold is crucial; setting it too low may cause false positives, unnecessarily halting operations, while setting it too high could delay response to actual collisions, risking injury or damage.