RoboGrind: Intuitive and Interactive Surface Treatment with Industrial
Robots
- URL: http://arxiv.org/abs/2402.16542v2
- Date: Tue, 27 Feb 2024 08:57:43 GMT
- Title: RoboGrind: Intuitive and Interactive Surface Treatment with Industrial
Robots
- Authors: Benjamin Alt, Florian St\"ockl, Silvan M\"uller, Christopher Braun,
Julian Raible, Saad Alhasan, Oliver Rettig, Lukas Ringle, Darko Katic, Rainer
J\"akel, Michael Beetz, Marcus Strand and Marco F. Huber
- Abstract summary: RoboGrind is an integrated system for the intuitive, interactive automation of surface treatment tasks with industrial robots.
It combines a sophisticated 3D perception pipeline for surface scanning and automatic defect identification.
- Score: 7.7407798321584185
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Surface treatment tasks such as grinding, sanding or polishing are a vital
step of the value chain in many industries, but are notoriously challenging to
automate. We present RoboGrind, an integrated system for the intuitive,
interactive automation of surface treatment tasks with industrial robots. It
combines a sophisticated 3D perception pipeline for surface scanning and
automatic defect identification, an interactive voice-controlled wizard system
for the AI-assisted bootstrapping and parameterization of robot programs, and
an automatic planning and execution pipeline for force-controlled robotic
surface treatment. RoboGrind is evaluated both under laboratory and real-world
conditions in the context of refabricating fiberglass wind turbine blades.
Related papers
- RAMPA: Robotic Augmented Reality for Machine Programming and Automation [4.963604518596734]
This paper introduces Robotic Augmented Reality for Machine Programming (RAMPA)
RAMPA is a system that utilizes the capabilities of state-of-the-art and commercially available AR headsets, e.g., Meta Quest 3.
Our approach enables in-situ data recording, visualization, and fine-tuning of skill demonstrations directly within the user's physical environment.
arXiv Detail & Related papers (2024-10-17T10:21:28Z) - Physical Simulation for Multi-agent Multi-machine Tending [11.017120167486448]
Reinforcement learning (RL) offers a promising solution where robots can learn through interaction with the environment.
We leveraged a simplistic robotic system to work with RL with "real" data without having to deploy large expensive robots in a manufacturing setting.
arXiv Detail & Related papers (2024-10-11T17:57:44Z) - COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models [49.24666980374751]
COHERENT is a novel LLM-based task planning framework for collaboration of heterogeneous multi-robot systems.
A Proposal-Execution-Feedback-Adjustment mechanism is designed to decompose and assign actions for individual robots.
The experimental results show that our work surpasses the previous methods by a large margin in terms of success rate and execution efficiency.
arXiv Detail & Related papers (2024-09-23T15:53:41Z) - Polaris: Open-ended Interactive Robotic Manipulation via Syn2Real Visual Grounding and Large Language Models [53.22792173053473]
We introduce an interactive robotic manipulation framework called Polaris.
Polaris integrates perception and interaction by utilizing GPT-4 alongside grounded vision models.
We propose a novel Synthetic-to-Real (Syn2Real) pose estimation pipeline.
arXiv Detail & Related papers (2024-08-15T06:40:38Z) - RoboScript: Code Generation for Free-Form Manipulation Tasks across Real
and Simulation [77.41969287400977]
This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation.
We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language.
We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
arXiv Detail & Related papers (2024-02-22T15:12:00Z) - A ROS Architecture for Personalised HRI with a Bartender Social Robot [61.843727637976045]
BRILLO project has the overall goal of creating an autonomous robotic bartender that can interact with customers while accomplishing its bartending tasks.
We present the developed three-layers ROS architecture integrating a perception layer managing the processing of different social signals, a decision-making layer for handling multi-party interactions, and an execution layer controlling the behaviour of a complex robot composed of arms and a face.
arXiv Detail & Related papers (2022-03-13T11:33:06Z) - Autonomous Aerial Robot for High-Speed Search and Intercept Applications [86.72321289033562]
A fully-autonomous aerial robot for high-speed object grasping has been proposed.
As an additional sub-task, our system is able to autonomously pierce balloons located in poles close to the surface.
Our approach has been validated in a challenging international competition and has shown outstanding results.
arXiv Detail & Related papers (2021-12-10T11:49:51Z) - Predicting Sample Collision with Neural Networks [5.713670854553366]
We present a framework to address the cost of expensive primitive operations in sampling-based motion planning.
We evaluate our framework on multiple planning problems with a variety of robots in 2D and 3D workspaces.
arXiv Detail & Related papers (2020-06-30T14:56:14Z) - SAPIEN: A SimulAted Part-based Interactive ENvironment [77.4739790629284]
SAPIEN is a realistic and physics-rich simulated environment that hosts a large-scale set for articulated objects.
We evaluate state-of-the-art vision algorithms for part detection and motion attribute recognition as well as demonstrate robotic interaction tasks.
arXiv Detail & Related papers (2020-03-19T00:11:34Z) - Autonomous Planning Based on Spatial Concepts to Tidy Up Home
Environments with Service Robots [5.739787445246959]
We propose a novel planning method that can efficiently estimate the order and positions of the objects to be tidied up by learning the parameters of a probabilistic generative model.
The model allows a robot to learn the distributions of the co-occurrence probability of the objects and places to tidy up using the multimodal sensor information collected in a tidied environment.
We evaluate the effectiveness of the proposed method by an experimental simulation that reproduces the conditions of the Tidy Up Here task of the World Robot Summit 2018 international robotics competition.
arXiv Detail & Related papers (2020-02-10T11:49:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.