Efficient Detection of Objects Near a Robot Manipulator via Miniature Time-of-Flight Sensors

2025-09-19

AI Summary

When mounted on robotic manipulators, miniature time-of-flight (ToF) sensors struggle to distinguish self-interference (e.g., links, joints) from external objects, especially under occlusion and varying lighting or material conditions. Method: This paper proposes a lightweight, real-time perception method based on empirical statistical modeling. It constructs pose-dependent sensor response models of the manipulator’s own structure to enable dynamic background subtraction and anomaly detection, and introduces a low-computation framework supporting flexible deployment of low-resolution ToF arrays—overcoming conventional configuration constraints and mitigating self-occlusion. Contribution/Results: Experiments demonstrate centimeter-level localization accuracy along the link direction for nearby small objects, even under complex illumination and diverse surface materials. The method significantly enhances near-field detection robustness, enabling reliable collision预警 and safe human–robot collaborative interaction.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Motion & Tracking
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We provide a method for detecting and localizing objects near a robot arm using arm-mounted miniature time-of-flight sensors. A key challenge when using arm-mounted sensors is differentiating between the robot itself and external objects in sensor measurements. To address this challenge, we propose a computationally lightweight method which utilizes the raw time-of-flight information captured by many off-the-shelf, low-resolution time-of-flight sensor. We build an empirical model of expected sensor measurements in the presence of the robot alone, and use this model at runtime to detect objects in proximity to the robot. In addition to avoiding robot self-detections in common sensor configurations, the proposed method enables extra flexibility in sensor placement, unlocking configurations which achieve more efficient coverage of a radius around the robot arm. Our method can detect small objects near the arm and localize the position of objects along the length of a robot link to reasonable precision. We evaluate the performance of the method with respect to object type, location, and ambient light level, and identify limiting factors on performance inherent in the measurement principle. The proposed method has potential applications in collision avoidance and in facilitating safe human-robot interaction.

Problem

• Detecting objects near robot arms using miniature sensors
• Differentiating robot from external objects in sensor data
• Avoiding self-detections while enabling flexible sensor placement

Innovation

• Arm-mounted miniature ToF sensors
• Empirical model for robot self-detection
• Raw ToF data processing method