Acoustic Feedback for Closed-Loop Force Control in Robotic Grinding
Authors: Zongyuan Zhang, Christopher Lehnert, Will N. Browne, Jonathan M. Roberts
Organizations: School of Electrical Engineering and Robotics, Queensland University of Technology, 2 George St, Brisbane, 4000, Queensland, Australia · Australian Cobotics Centre, Queensland University of Technology, 2 George St, Brisbane, 4000, Queensland, Australia · Centre for Robotics, Queensland University of Technology, 2 George St, Brisbane, 4000, Queensland, Australia
Acoustic feedback is a critical indicator for assessing the contact condition between the tool and the workpiece when humans perform grinding tasks with rotary tools. In contrast, robotic grinding systems typically rely on force sensing, with acoustic information largely ignored. This reliance on force sensors is costly and difficult to adapt to different grinding tools, whereas audio sensors (microphones) are low-cost and can be mounted on any medium that conducts grinding sound. This paper introduces a low-cost Acoustic Feedback Robotic Grinding System (AFRG) that captures audio signals with a contact microphone, estimates grinding force from the audio in real time, and enables closed-loop force control of the grinding process. Compared with conventional force-sensing approaches, AFRG achieves a 4-fold improvement in consistency across different grinding disc conditions. AFRG relies solely on a low-cost microphone, which is approximately 200-fold cheaper than conventional force sensors, as the sensing modality, providing an easily deployable, cost-effective robotic grinding solution.
Figures & tables
Fig. 1: Acoustic Feedback Robotic Grinding System (AFRG) uses acoustic signals for closed-loop force control in robotic grinding. Instead of expensive force sensors, AFRG employs a cost-effective contact microphone to estimate the grinding force. The workflow involves audio recording, signal processing, and regression to estimate the force, which is then used as feedback to regulate the grinding process. An example of the grinding result is shown below the figure, where the uniform grinding depth demonstrates a consistent Material Removal Rate (MRR).
Fig. 2: (a) The forces, torque, and velocity of the tool relative to the workpiece surface (side view). (b) Different conditions of the grinding disc on the rotary tool during operation (top view).
Fig. 3: Schematic of the AFRG. It consists of three components: Real-Time PSD Encoder for data processing, PSDRegNet for force estimation, and the Force-Position Hybrid Controller for closed-loop manipulator control. Here, ti denotes a given time step, Xi is the stacked PSD encoder output over the n -frame receptive field, Fi represents the estimated normal force, Pi is the end-effector position, and Ui is the joint velocity commands.
Layer
Output Size
Details
Input PSD array
1×35×40
–
Conv2D
16×33×38
kernel=5, padding=1
BatchNorm2D
16×33×38
–
ReLU
16×33×38
–
Conv2D
32×31×36
kernel=5, padding=1
BatchNorm2D
32×31×36
–
TABLE I: Network architecture for PSDRegNet
Fig. 4: Experimental setup consisting of a 6-DOF UR5 robotic arm, an F/T sensor, a rotary grinding tool with an aluminium oxide disc, a contact microphone, and rectangular workpiece bars made of aluminium alloy or stainless steel. This setup allows grinding to be performed using either F/T sensor-measured forces or acoustically estimated forces, thereby providing a reliable benchmark to evaluate the proposed method.
Fig. 5: Representative dataset samples are shown for continuous (left) and intermittent (right) grinding. The force axis is inverted to better visualise the correlation between the PSD arrays and the force signal, which are not directly aligned.
Setting
Value
Loss function
MSE loss
Optimizer
Adam
Weight decay
1×10−4
Train/validation split
80% / 20%
Number of epochs
5
Batch size
64
TABLE II: Training settings for PSDRegNet
Fig. 6: Force estimation results. (a) Distribution of measured and estimated forces, where closer overlap indicates higher accuracy. (b) Residual distribution, which ideally follows a normal distribution. (c) Scatter plot of estimated versus measured forces, where clustering along the x=y line indicates better accuracy. Outliers are highlighted in orange.
Fig. 7: Workpieces (left side of each image) after grinding along a straight-line trajectory from top to bottom, horizontally stretched for clarity. (a, b) 3 N target force: (a) annotated, (b) unannotated. (c, d) 4 N target force: (c) annotated, (d) unannotated. (e) Image in original aspect ratio corresponding to (d).
Fig. 8: Force control performance of AFRG during a straight-line grinding. The measured normal force is compared to the target force, with annotations indicating rise time, overshoot, and steady-state error.
Fig. 9: Absolute error between estimated and measured forces during straight-line grinding with AFRG, evaluated within the steady-state window.
Fig. 10: Workpiece surfaces after 1–5 fixed-point grinding trials, horizontally stretched for clarity. (a, b) F/T sensor-based force control: (a) annotated, (b) unannotated. (c, d) AFRG: (c) annotated, (d) unannotated. AFRG shows significantly improved MRR stability, with grinding depth variation approximately four times smaller compared to F/T sensor-based control.
Force-controlled robotic manipulation studies how robots regulate contact forces and compliance to grasp, assemble, and work safely with objects and surfaces.
Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST), Nara, Japan. · Haptics Research Center, Keio University, Kanagawa, Japan. · Graduate School of Informatics, Kyoto University, Kyoto, Japan. +1
Robotics and Biology Laboratory, Technische Universität Berlin · Science of Intelligence, Research Cluster of Excellence, Berlin · Robotics Institute Germany