0000000000767515
AUTHOR
Knut Berg Kaldestad
Collision Avoidance with Potential Fields Based on Parallel Processing of 3D-Point Cloud Data on the GPU
In this paper we present an experimental study on real-time collision avoidance with potential fields that are based on 3D point cloud data and processed on the Graphics Processing Unit (GPU). The virtual forces from the potential fields serve two purposes. First, they are used for changing the reference trajectory. Second they are projected to and applied on torque control level for generating according nullspace behavior together with a Cartesian impedance main control loop. The GPU algorithm creates a map representation that is quickly accessible. In addition, outliers and the robot structure are efficiently removed from the data, and the resolution of the representation can be easily ad…
Robotic face milling path correction and vibration reduction
In this paper the developed method for off-line compensation of tool deflections and vibration reduction when milling aluminum with an industrial robot is presented. The efficiency of this approach is verified with high precision measurements of deflections using a laser tracker. The compensation method is based on the static milling process model which can predict the mean value components of the tool forces and the passive damping system mounted on the spindle to reduce vibrations. With a process model such as the one presented in this paper and estimates of the robot's joint stiffness values, the tool path can be adjusted to counteract deflections of the tool during milling operations. T…
Laser Triangulation 3D Point Cloud Sensor with Long Range and Large Field of View
This paper presents a point cloud sensor design based on laser triangulation. Both the camera axis and the laser axis are rotating, making it possible to scan on short and long range in high resolution. A third axis moves the laser and camera into a new plane. The design is tested on a working prototype. To the authors knowledge a similar design has not been presented before.
CAD-Based Training of an Expert System and a Hidden Markov Model for Obstacle Detection in an Industrial Robot Environment
Abstract Deploying industrial robots in harsh outdoor environments require additional functionalities not currently provided. For instance, movement of standard industrial robots are pre-programmed to avoid collision. In dynamic and less structured environments, however, the need for online detection and avoidance of unmodelled objects arises. This paper focus on online obstacle detection using a laser sensor by proposing three different approaches, namely a CAD-based Expert System (ES) and two probabilistic methods based on a Hidden Markov Model (HMM) which requires observation based training. In addition, this paper contributes by providing a comparison between the CAD-based ES and the tw…
Hard material small-batch industrial machining robot
Abstract Hard materials can be cost effectively machined with standard industrial robots by enhancing current state-of-the-art technologies. It is demonstrated that even hard metals with specific robotics-optimised novel hard-metal tools can be machined by standard industrial robots with an improved position-control approach and enhanced compliance-control functions. It also shows that the novel strategies to compensate for elastic robot errors, based on models and advanced control, as well as the utilisation of new affordable sensors and human-machine interfaces, can considerably improve the robot performance and applicability of robots in machining tasks. In conjunction with the developme…
Obstacle Detection in an Unstructured Industrial Robotic System: Comparison of Hidden Markov Model and Expert System
Abstract This paper presents a comparison of two approaches for detecting unknown obstacles inside the workspace of an industrial robot using a laser rangefinder for 2-D measurements. The two approaches are based on Expert System (ES) and Hidden Markov Model (HMM). The results presented in the paper demonstrate that both approaches are able to correctly detect and classify unknown objects. The ES is characterised by low computational requirements and an easy setup when relatively few known objects are to be included inside the workspace. HMMs are characterised by a higher flexibility and the ability to handle a larger amount of known objects inside the workspace. Another significant benefit…
Visual Marker Guided Point Cloud Registration in a Large Multi-Sensor Industrial Robot Cell
This paper presents a benchmark and accuracy analysis of 3D sensor calibration in a large industrial robot cell. The sensors used were the Kinect v2 which contains both an RGB and an IR camera measuring depth based on the time-of-flight principle. The approach taken was based on a novel procedure combining Aruco visual markers, methods using region of interest and iterative closest point. The calibration of sensors is performed pairwise, exploiting the fact that time-of-flight sensors can have some overlap in the generated point cloud data. For a volume measuring 10m × 14m × 5m a typical accuracy of the generated point cloud data of 5–10cm was achieved using six sensor nodes.
D Sensor-Based Obstacle Detection Comparing Octrees and Point clouds Using CUDA
This paper presents adaptable methods for achieving fast collision detection using the GPU and Nvidia CUDA together with Octrees. Earlier related work have focused on serial methods, while this paper presents a parallel solution which shows that there is a great increase in time if the number of operations is large. Two dierent models of the environment and the industrial robot are presented, the rst is Octrees at dierent resolutions, the second is a point cloud representation. The relative merits of the two dierent world model representations are shown. In particular, the experimental results show the potential of adapting the resolution of the robot and environment models to the task at h…
Off-line path correction of robotic face milling using static tool force and robot stiffness
In this paper the developed method for off-line compensation of tool deflections when milling aluminum with an industrial robot is presented. The efficiency of this approach is verified with high precision measurements of deflections using a laser tracker. The compensation method includes both the static milling process model which can predict the mean value components of the tool forces and a new combined local/global approach for estimating the combined stiffnesses of joints. With a process model such as the one presented in this paper and estimates of the robot's joint stiffness values, the tool path can be adjusted to counteract deflections of the tool during milling operations. The mod…
Industrial Robot Collision Handling in Harsh Environments
Doktorgradsavhandling i mekatronikk, Universitetet i Agder, 2014 The focus in this thesis is on robot collision handling systems, mainly collision detection and collision avoidance for industrial robots operating in harsh environments (e.g. potentially explosive atmospheres found in the oil and gas sector). Collision detection should prevent the robot from colliding and therefore avoid a potential accident. Collision avoidance builds on the concept of collision detection and aims at enabling the robot to find a collision free path circumventing the obstacle and leading to the goal position. The work has been done in collaboration with ABB Process Automation Division with focus on applicatio…