Joint Torque Sensing vs Motor Current Estimation for Collision Detection

Joint torque sensing excels at high-fidelity, high-bandwidth force measurement because it measures physical deflection directly at the output of the gearbox. This approach captures the full spectrum of collision dynamics, including high-frequency stress waves that travel through a robot's rigid structure upon impact. For example, a dedicated strain-gauge sensor can detect a hard collision and trigger a safety stop in under 2 milliseconds, a response speed that is critical for power-and-force-limiting applications under ISO/TS 15066.

Motor current estimation takes a different approach by using a mathematical model of the motor and transmission to infer joint torque from the current required to generate motion. This results in a cost-effective and mechanically robust solution that eliminates the need for fragile sensor cabling and complex calibration. However, the trade-off is a significant loss of sensitivity; the signal is heavily filtered by the inertia and friction of the gearbox, making it nearly impossible to detect subtle, low-force contacts or to distinguish a collision from a simple acceleration spike.

The key trade-off: If your priority is achieving the highest possible safety rating and enabling true hand-guiding in unstructured environments, choose direct joint torque sensing. If you prioritize system cost, mechanical durability, and zero-backlash integration for a fenced-off or strictly speed-limited cobot, choose motor current estimation. The decision hinges on whether your application requires detecting a 5N contact force or simply reacting to a high-energy impact.

Joint Torque Sensing excels at high-bandwidth, high-fidelity force detection because it measures the physical deformation of a sensing element directly at the output of the gearbox. This direct measurement path eliminates the non-linear friction, cogging torque, and inertia masking inherent in the drive train. For example, dedicated sensors from Bota Systems or ATI Industrial Automation can achieve a signal-to-noise ratio that reliably detects forces as low as 0.5 Nm at a 2 kHz bandwidth, making them the gold standard for safety-rated Power and Force Limiting (PFL) applications under ISO/TS 15066.

Motor Current Estimation takes a fundamentally different approach by using a mathematical model of the motor and transmission to infer joint torque from the motor's electrical current. This strategy results in a zero-backlash, mechanically robust joint without the added weight, cost, or compliance of a physical sensor. However, the trade-off is a significant loss of sensitivity at low frequencies and during static contact. The friction models required to compensate for harmonic drives or planetary gearboxes typically leave a residual error of 5-15% of the motor's rated torque, creating a 'blind spot' for small collision forces that is difficult to distinguish from thermal drift or gear wear.

The key trade-off: If your priority is achieving the highest possible safety rating and the ability to detect transient, low-force contacts for true human-robot collaboration, choose Joint Torque Sensing. The sensor's direct physical measurement provides the bandwidth and resolution needed for safety-rated contact detection without complex model tuning. If you prioritize mechanical simplicity, cost reduction, and a stiffer joint structure for high-precision tasks like assembly, choose Motor Current Estimation, but be prepared to implement a conservative safety-rated monitored stop or use external safety scanners to compensate for the sensor's inherent blind spots during slow, quasi-static movements.