Automatic Onsite Grinding of Large Complex Surfaces

2019–2024 Advanced Robotics for Manufacturing Institute

Description of the image

Polishing and grinding of metallic parts is an important manufacturing operation in many industrial applications. It remains challenging to polish large complex surfaces. Polishing is predominantly done manually which is very time consuming, expensive and more importantly, can be a safety hazard for human operators using hand-held devices. We propose the assembly of a robot capable of polishing complex and relatively large surface areas of complex free form fabricated parts. By developing this robot for a complex and potentially un-safe operation, this application will lower the technical, operational, and economic barriers for companies to adopt robotic technologies.

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Research portfolio

2023–Now Prompt-to-Product: Unleashing Creativity with AI and Robots 2023–Now Humanoid Robots: Safety and Dexterity 2023–Now Neural-Symbolic Robotics: Learning and Reconfiguring Task Structure 2022–Now Toward Lifelong Safety of Autonomous Systems in UIE 2025–2026 PerSEVE (Perception by Segmentation, Extensible Visual Ecosystem) 2024–2026 Koopman Theory for Robot Learning and Control 2023–2025 State-wise Safe and Robust Reinforcement Learning for Continuous Control 2021–2025 Real-Time Systems for Human-Robot Interaction and Industrial Metaverse 2023–2024 Enhancing and Verifying the Robustness of Learning-based Systems 2019–2024 Automatic Onsite Grinding of Large Complex Surfaces Current project 2022–2023 Proactive Safe Human-Robot Interaction 2020–2022 Safe Uncaged Industrial Robots 2020–2021 Hierarchical Motion Planning for Efficient and Provably Safe HRI 2019–2021 6DoF Robot Assembly Station of Consumer Electronic Production 2019 Adaptable Behavior Prediction for Autonomous Driving 2012–2018 Past Projects

Abstract:

Applying intelligent robot arms in dynamic uncertain environments (i.e., flexible production lines) remains challenging, which requires efficient algorithms for real time trajectory generation. The motion planning problem for robot trajectory generation is highly nonlinear and nonconvex, which usually comes with collision avoidance constraints, robot kinematics and dynamics constraints, and task constraints (e.g., following a Cartesian trajectory defined on a surface and maintain the contact). The nonlinear and nonconvex planning problem is computationally expensive to solve, which limits the application of robot arms in the real world. In this paper, for redundant robot arm planning problems with complex constraints, we present a motion planning method using iterative convex optimization that can efficiently handle the constraints and generate optimal trajectories in real time. The proposed planner guarantees the satisfaction of the contact-rich task constraints and avoids collision in confined environments. Extensive experiments on trajectory generation for weld grinding are performed to demonstrate the effectiveness of the proposed method and its applicability in advanced robotic manufacturing.