Acoustic Feedback for Closed-Loop Force Control in Robotic Grinding

Abstract

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 (10)

• Figure 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).
• Figure 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).
• Figure 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, $t_i$ denotes a given time step, $\mathbf{X}_i$ is the stacked PSD encoder output over the $n$-frame receptive field, $F_i$ represents the estimated normal force, $P_i$ is the end-effector position, and $U_i$ is the joint velocity commands.
• Figure 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.
• Figure 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.