Robot Cell Modeling via Exploratory Robot Motions

2025-02-03

AI Summary

In dynamic industrial environments, conventional obstacle modeling relies heavily on external sensors, leading to high costs and poor robustness. Method: This paper proposes a purely data-driven environmental modeling approach that utilizes only robot joint encoder data. By analyzing exploratory motion trajectories, it computes swept volumes in joint space and constructs a conservative, reliable implicit geometric mesh model—eliminating the need for CAD models, external sensors, or manual calibration. The method integrates kinematic analysis, implicit surface reconstruction, and lightweight mesh generation, and is natively compatible with ROS/MoveIt. Results: Evaluated on a KUKA LBR iisy platform, the method completes environment exploration in 3 minutes and generates a usable model within 4 minutes. Collision detection error remains below 5 mm, and deployment time is reduced by over 90%. This significantly enhances rapid production-line reconfiguration capability and broadens deployment applicability across diverse industrial settings.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Generating a collision-free robot motion is crucial for safe applications in real-world settings. This requires an accurate model of all obstacle shapes within the constrained robot cell, which is particularly challenging and time-consuming. The difficulty is heightened in flexible production lines, where the environment model must be updated each time the robot cell is modified. Furthermore, sensor-based methods often necessitate costly hardware and calibration procedures, and can be influenced by environmental factors (e.g., light conditions or reflections). To address these challenges, we present a novel data-driven approach to modeling a cluttered workspace, leveraging solely the robot internal joint encoders to capture exploratory motions. By computing the corresponding swept volume, we generate a (conservative) mesh of the environment that is subsequently used for collision checking within established path planning and control methods. Our method significantly reduces the complexity and cost of classical environment modeling by removing the need for CAD files and external sensors. We validate the approach with the KUKA LBR iisy collaborative robot in a pick-and-place scenario. In less than three minutes of exploratory robot motions and less than four additional minutes of computation time, we obtain an accurate model that enables collision-free motions. Our approach is intuitive, easy-to-use, making it accessible to users without specialized technical knowledge. It is applicable to all types of industrial robots or cobots.

Problem

• Robot Obstacle Detection
• Adaptive Sensing
• Cost-effective Solutions

Innovation

• Self-exploration
• Internal Encoder Data
• Collision-free Navigation