RoboTSP - A Fast Solution to the Robotic Task Sequencing Problem

In many industrial robotics applications, such as spot-welding, spray-painting or drilling, the robot is required to visit successively multiple targets. The robot travel time among the targets is a significant component of the overall execution time. This travel time is in turn greatly affected by the order of visit of the targets, and by the robot configurations used to reach each target. Therefore, it is crucial to optimize these two elements, a problem known in the literature as the Robotic Task Sequencing Problem (RTSP). Our contribution in this paper is two-fold. First, we propose a fast, near-optimal, algorithm to solve RTSP. The key to our approach is to exploit the classical distinction between task space and configuration space, which, surprisingly, has been so far overlooked in the RTSP literature. Second, we provide an open-source implementation of the above algorithm, which has been carefully benchmarked to yield an efficient, ready-to-use, software solution. We discuss the relationship between RTSP and other Traveling Salesman Problem (TSP) variants, such as the Generalized Traveling Salesman Problem (GTSP), and show experimentally that our method finds motion sequences of the same quality but using several orders of magnitude less computation time than existing approaches.Comment: 6 pages, 7 figures, 1 tabl

Simulation and Planning of a 3D Spray Painting Robotic System

Nesta dissertação é proposto um sistema robótico 3D de pintura com spray. Este sistema inclui uma simulação realista do spray com precisão suficiente para imitar pintura com spray real. Também inclui um algoritmo otimizado para geração de caminhos que é capaz de pintar projetos 3D não triviais. A simulação parte de CAD 3D ou peças digitalizadas em 3D e produz um efeito visual realista que permite analisar qualitativamente o produto pintado. Também é apresentada uma métrica de avaliação que pontua trajetória de pintura baseada na espessura, uniformidade, tempo e desperdício de tinta.In this dissertation a 3D spray painting robotic system is proposed. This system has realistic spray simulation with sufficient accuracy to mimic real spray painting. It also includes an optimized algorithm for path generation that is capable of painting non trivial 3D designs. The simulation has 3D CAD or 3D scanned input pieces and produces a realistic visual effect that allows qualitative analyses of the painted product. It is also presented an evaluation metric that scores the painting trajectory based on thickness, uniformity, time and waste of paint

Development of a task-level robot programming and simulation system

An ongoing project in developing a Task-Level Robot Programming and Simulation System (TARPS) is discussed. The objective of this approach is to design a generic TARPS that can be used in a variety of applications. Many robotic applications require off-line programming, and a TARPS is very useful in such applications. Task level programming is object centered in that the user specifies tasks to be performed instead of robot paths. Graphics simulation provides greater flexibility and also avoids costly machine setup and possible damage. A TARPS has three major modules: world model, task planner and task simulator. The system architecture, design issues and some preliminary results are given

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

Trajectory and spray control planning on unknown 3D surfaces for industrial spray painting robot

Automated 3D path and spray control planning of industrial painting robots for unknown target surfaces is desired to meet demands on the production system. In this thesis, an image acquisition and laser range scanning based method has been developed. The system utilizes the XY projection of the boundaries of the target surface to generate the gun trajectory\u27s X and Y coordinates as well as the spray control. Z coordinates and gun direction, distance, and speed are generated based on the point cloud from the target that is acquired by the laser scanner. A simulation methodology was also developed which is capable of calculating the paint thickness across the target surface. Results have shown that the generated path could perform a full coverage on the target surface, while keeping the paint material waste at the minimum. Excellent paint thickness control could be achieved on 2D and straight line sweep surfaces, while a satisfactory thickness is obtained on other 3D arbitrary surfaces. Relationships among thickness, spray deposition profile, sampling roughness and geometric features of the target surfaces have been discussed to make this method more applicable in industry

Generating Optimized Trajectories for Robotic Spray Painting

In the manufacturing industry, spray painting is often an important part of the manufacturing process. Especially in the automotive industry, the perceived quality of the final product is closely linked to the exactness and smoothness of the painting process. For complex products or low batch size production, manual spray painting is often used. But in large scale production with a high degree of automation, the painting is usually performed by industrial robots. There is a need to improve and simplify the generation of robot trajectories used in industrial paint booths. A novel method for spray paint optimization is presented, which can be used to smooth out a generated initial trajectory and minimize paint thickness deviations from a target thickness. The smoothed out trajectory is found by solving, using an interior point solver, a continuous non-linear optimization problem. A two-dimensional reference function of the applied paint thickness is selected by fitting a spline function to experimental data. This applicator footprint profile is then projected to the geometry and used as a paint deposition model. After generating an initial trajectory, the position and duration of each trajectory segment are used as optimization variables. The primary goal of the optimization is to obtain a paint applicator trajectory, which would closely match a target paint thickness when executed. The algorithm has been shown to produce satisfactory results on both a simple 2-dimensional test example, and a non-trivial industrial case of painting a tractor fender. The resulting trajectory is also proven feasible to be executed by an industrial robot

Generation of effective sand-blasting trajectory for an autonomous robot in steel bridge maintenance

University of Technology, Sydney. Faculty of Engineering.Steel bridges are vulnerable to corrosions, which results in conditions demanding regular maintenance in terms of de-rusting and re-painting. Current practices mostly rely on human workers with manually operated sand-blasting equipment to remove the rust or paint. This approach is labour intensive, tedious and, most of all, causes health and safety hazards for the workers, due to toxic dust arising from the removed lead or asbestos-based paints. Thus, an autonomous steel bridge maintenance system is very desirable, and the motion control of a robotic arm is identified as a key system requirement. This thesis is concerned with studies on algorithms for generating an effective trajectory to be followed by an industrial robot arm used in sand-blasting. It is crucial in the context of productivity that the motion of the arm should follow a trajectory that aims to maximise the coverage of the blasted area and minimise the arm movements. The problem is challenging due to the changing environment underneath the bridge and the risk of colliding with obstacles. Furthermore, the trajectory generation process is complicated because of the many requirements imposed, such as minimum arm travel distance: minimum number of turns and minimum time to complete the blasting. The problem is tackled in this research by beginning with an assignment of the blasting area, where a hexagonal coverage pattern is adopted to allocate blasting targets. The sequencing of blasting spots on the blasting surface, constituting the path to be followed by the blasting nozzle, is determined through the use of a genetic algorithm as a sequence-finder for its applicability and flexibility in many engineering design problems. The order of blasting spots (that is, the path of nozzle) is then transformed to robot joint angles, that is, trajectory, by a genetic algorithm amended inverse kinematics approach. Furthermore, a method based on three-dimensional force-fields is used to safeguard the robot against collisions with obstacles. The resultant trajectory, in the form of a series of joint angles commands are fed to a Denso VM-6083D-W industrial robot for sand-blasting. The effectiveness of the generated trajectory is verified by simulations and experiments. It is shown that trajectories can be derived for blasting surfaces with satisfactory coverage. The developed method is further demonstrated in generating trajectories for a number of blasting surfaces of different sizes, to the extent of the work space, at various locations and orientations surrounding the robot arm. An experiment is conducted, on a mock-up robotic blasting system, by driving the robot arm in accordance with a generated trajectory