Dynamics | MAAL

Sensorless contact wrench estimation for industrial robots

Tue, 11 Aug 2026 00:00:00 +0000

Robots used for polishing, assembly, and manipulation need reliable contact-force feedback. A six-axis wrist force/torque sensor provides direct measurements, but adds cost, wiring, payload, and integration constraints. This project estimates the same six-dimensional contact wrench from signals already available on the robot: joint motion and motor current.

Dual-stage estimation

The estimator combines a physics-based robot model with two learned residual corrections:

Free-space dynamics compensation. An LSTM learns joint-torque errors caused by friction, backlash, hysteresis, parameter mismatch, and torque-conversion bias using contact-free trajectories.
Contact wrench compensation. A temporal encoder and residual network correct the remaining task-space error during contact and predict an input-dependent uncertainty for each wrench axis.

Separating these responsibilities keeps the estimate anchored to robot mechanics while allowing each learning stage to focus on a distinct error source.

Experimental validation

The method was evaluated on a six-axis industrial manipulator. A cable-and-pulley rig applied loads of 0.5, 1.0, and 1.5 kg from multiple directions. A wrist load cell sampled at 1 kHz supplied training labels and evaluation ground truth, but was not used as an input during sensorless inference.

Stage I reduced the Joint 1 residual-torque RMSE from 4.2649 to 0.8591 Nm and the Joint 2 RMSE from 3.1453 to 1.0069 Nm. Across the final six-axis wrench evaluation, the dual-stage method achieved the lowest reported RMSE, maximum error, and relative error on every force and moment axis.

Metric	Fx	Fy	Fz	Mx	My	Mz
RMSE	1.5959 N	0.7067 N	1.9678 N	0.1367 Nm	0.1141 Nm	0.0538 Nm
Maximum error	5.9546 N	4.4262 N	5.3414 N	0.7739 Nm	0.4898 Nm	0.4104 Nm
Relative error	13.8%	5.8%	13.4%	17.6%	22.2%	8.3%

Why it matters

The main contribution is not simply a deeper network, but a physically meaningful decomposition of the estimation problem. The intermediate output makes each correction inspectable, while the probabilistic second stage provides a condition-dependent confidence signal. This creates a practical path toward lower-cost force-aware robots and future closed-loop force control without a permanently installed wrist sensor.

Flexible industrial robot dynamics and control

Tue, 06 Jan 2026 00:00:00 +0000

Industrial robots change stiffness and vibration behavior with their position and posture. This project establishes dynamic models that capture both joint and link flexibility without making the model too costly to use in control.

Approach

Represent geometrically nonlinear links with strain-based beam elements and modal coordinates.
Identify configuration-dependent vibration from experimental data.
Generate nonlinear filtered B-spline trajectories that suppress motion-induced vibration.
Validate compensation strategies on industrial robotic platforms.

Why it matters

More accurate dynamic models allow robots to move faster while maintaining precision. The same methods support robotic machining, additive manufacturing, and large-reach manipulation.

Boom-lift-mounted robotic systems

Thu, 01 Jan 2026 00:00:00 +0000

Mounting a robotic manipulator on a boom lift extends its workspace, but the flexible, extendable support introduces configuration-dependent vibration and positioning error.

This project models the coupled boom-and-robot system, identifies changing structural dynamics, and develops trajectory and active-compensation methods for accurate long-reach operations.

Applications include inspection, maintenance, construction, and manufacturing tasks where conventional fixed-base robots cannot reach.