Collaborative Robotic Path Planning for Industrial Spraying Operations on Complex Geometries

Implementation of automated robotic solutions for complex tasks currently faces a few major hurdles. For instance, lack of effective sensing and task variability – especially in high-mix/low-volume processes – creates too much uncertainty to reliably hard-code a robotic work cell. Current collaborative frameworks generally focus on integrating the sensing required for a physically collaborative implementation. While this paradigm has proven effective for mitigating uncertainty by mixing human cognitive function and fine motor skills with robotic strength and repeatability, there are many instances where physical interaction is impractical but human reasoning and task knowledge is still needed. The proposed framework consists of key modules such as a path planner, path simulator, and result simulator. An integrated user interface facilitates the operator to interact with these modules and edit the path plan before ultimately approving the task for automatic execution by a manipulator that need not be collaborative. Application of the collaborative framework is illustrated for a pressure washing task in a remanufacturing environment that requires one-off path planning for each part. The framework can also be applied to various other tasks, such as spray-painting, sandblasting, deburring, grinding, and shot peening. Specifically, automated path planning for industrial spraying operations offers the potential to automate surface preparation and coating in such environments. Autonomous spray path planners in the literature have been limited to generally continuous and convex surfaces, which is not true of most real parts. There is a need for planners that consistently handle concavities and discontinuities, such as sharp corners, holes, protrusions or other surface abnormalities when building a path. The path planner uses a slicing-based method to generate path trajectories. It identifies and quantifies the importance of concavities and surface abnormalities and whether they should be considered in the path plan by comparing the true part geometry to the convex hull path. If necessary, the path is then adapted by adjusting the movement speed or offset distance at individual points along the path. Which adaptive method is more effective and the trade-offs associated with adapting the path are also considered in the development of the path planner

Automatic Robot Path Planning for Visual Inspection from Object Shape

Visual inspection is a crucial yet time-consuming task across various industries. Numerous established methods employ machine learning in inspection tasks, necessitating specific training data that includes predefined inspection poses and training images essential for the training of models. The acquisition of such data and their integration into an inspection framework is challenging due to the variety in objects and scenes involved and due to additional bottlenecks caused by the manual collection of training data by humans, thereby hindering the automation of visual inspection across diverse domains. This work proposes a solution for automatic path planning using a single depth camera mounted on a robot manipulator. Point clouds obtained from the depth images are processed and filtered to extract object profiles and transformed to inspection target paths for the robot end-effector. The approach relies on the geometry of the object and generates an inspection path that follows the shape normal to the surface. Depending on the object size and shape, inspection paths can be defined as single or multi-path plans. Results are demonstrated in both simulated and real-world environments, yielding promising inspection paths for objects with varying sizes and shapes. Code and video are open-source available at: https://github.com/CuriousLad1000/Auto-Path-PlannerComment: 8 page

Optimisation of surface coverage paths used by a non-contact robot painting system

This thesis proposes an efficient path planning technique for a non-contact optical “painting” system that produces surface images by moving a robot mounted laser across objects covered in photographic emulsion. In comparison to traditional 3D planning approaches (e.g. laminar slicing) the proposed algorithm dramatically reduces the overall path length by optimizing (i.e. minimizing) the amounts of movement between robot configurations required to position and orientate the laser. To do this the pixels of the image (i.e. points on the surface of the object) are sequenced using configuration space rather than Cartesian space. This technique extracts data from a CAD model and then calculates the configuration that the five degrees of freedom system needs to assume to expose individual pixels on the surface. The system then uses a closest point analysis on all the major joints to sequence the points and create an efficient path plan for the component. The implementation and testing of the algorithm demonstrates that sequencing points using a configuration based method tends to produce significantly shorter paths than other approaches to the sequencing problem. The path planner was tested with components ranging from simple to complex and the paths generated demonstrated both the versatility and feasibility of the approach

PaintNet: Unstructured Multi-Path Learning from 3D Point Clouds for Robotic Spray Painting

Popular industrial robotic problems such as spray painting and welding require (i) conditioning on free-shape 3D objects and (ii) planning of multiple trajectories to solve the task. Yet, existing solutions make strong assumptions on the form of input surfaces and the nature of output paths, resulting in limited approaches unable to cope with real-data variability. By leveraging on recent advances in 3D deep learning, we introduce a novel framework capable of dealing with arbitrary 3D surfaces, and handling a variable number of unordered output paths (i.e. unstructured). Our approach focuses on predicting smaller path segments, which can be later concatenated to reconstruct long-horizon paths. We extensively validate the proposed method in the context of robotic spray painting by releasing PaintNet, the first public dataset of expert demonstrations on free-shape 3D objects collected in a real industrial scenario. A thorough experimental analysis demonstrates the capabilities of our model to promptly predict smooth output paths that cover up to 95% of the surface of previously unseen object instances. Furthermore, we show how models learned from PaintNet capture relevant features which serve as a reliable starting point to improve data and time efficiency when dealing with new object categories

PATH PLANNING FOR AUTOMATED ROBOT PAINTING

ABSTRACT This paper describes the analysis work underlying the pathplanning algorithm for a robotic painting system. The system requires no bespoke production tooling and fills an automation gap in rapid prototyping and manufacturing technology that is currently occupied by hand painting. The system creates images by exposing individual pixels of a photographic coating with a robot-mounted laser. The painting process requires no physical contact so potentially images could be developed on any shape regardless of its complexity: As objects can only be "painted" when their surface can be "hit" (i.e. exposed) by the light beam the system requires six degrees of freedom to ensure all overhanging or reentrant areas can be exposed. The accuracy of serial robots degrades with the length of the kinematic chain (in other words six axis robots cannot position themselves with the same accuracy as four axis ones). Consequently to ensure high precision in the location and orientation of the light source, the object being exposed is mounted on a rotary tilt table within the workspace of a fouraxis robot. This gives a six-degree of freedom positioning system composed of two separate kinematic chains. Although the resulting system is accurate the problems of constructing a coordinated path that allows the light beam to efficiently sweep (i.e. cover) the surface regardless of its geometry are challenging. This paper describes the difficulties and, after reviewing existing path planning algorithms, a new algorithm is introduced firstly by describing the nature of the system's configuration space and then further developing this concept as an alternative to a previously described planning algorithm. Having outlined the approach the paper presents a kinematic model for the system and compares the configuration space approach to a purely Cartesian planning approach

Automatic offline trajectory generation for surface coverage problem from a 3D drawing

Surface coverage with a robot manipulator is widely used in different industrial processes. For this reason having an automated way to program these robots is really important.In this work we implement an iterative algorithm to automate the process of trajectory generation for surface coverage, given the 3D model of the target object, with a particular focus on spray painting process

Integrated sliding-mode algorithms in robot tracking applications

An integrated solution based on sliding mode ideas is proposed for robotic trajectory tracking. The proposal includes three sliding-mode algorithms for speed auto-regulation, path conditioning and redundancy resolution in order to fulfill velocity, workspace and C-space constraints, respectively. The proposed method only requires a few program lines and simplifies the robot user interface since it directly deals with the fulfillment of the constraints to find a feasible solution for the robot trajectory tracking in a short computation time. The proposed approach is evaluated in simulation on the freely accessible 6R robot model PUMA-560, for which the main features of the method are illustrated.This research is partially supported by research project DPI2011-27845-C02-01 of the Spanish Government (Spain), research projects PAID-05-11-2640 and PAID-00-12-SP20120159 of the Universitat Polit'ecnica de Val'encia (Spain), and research projects ANPCyT PICT-2011-0888, CONICET PIP 112-2011-00361, and UNLP 1164 (Argentina).Gracia Calandin, LI.; Garelli, F.; Sala Piqueras, A. (2013). Integrated sliding-mode algorithms in robot tracking applications. Robotics and Computer-Integrated Manufacturing. 29(1):53-62. https://doi.org/10.1016/j.rcim.2012.07.007S536229

Coverage Path Planning with Real‐time Replanning and Surface Reconstruction for Inspection of Three‐dimensional Underwater Structures using Autonomous Underwater Vehicles

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/113717/1/rob21554.pd