Nathan Larkin

Automated offline programming for robotic welding system with high degree of freedoms

Although robotics based flexible automation is an intriguing prospect for small to median enterprises in the era of the global competition, the complexity of programming remains one of the major hurdles limiting its applications. This paper presents an automated offline programming (AOLP) method to address this issue. AOLP is software that automatically plans and programs for a robotic welding system with high Degree of Freedoms (DOFs). It takes CAD model as input, and is able to generate the complete robotic welding code without any further programming effort.

Adaptive Partial Shortcuts: Path Optimization for Industrial Robotics

The quality of a path generated from an automated motion planning algorithm is of considerable importance, particularly when used in a real world robotic application. In this work a new path optimization algorithm, called the Adaptive Partial Shortcut algorithm, is presented. This algorithm optimizes paths as a post process to motion planning, and is designed specifically for use on industrial manipulators. The algorithm optimizes a robot’s degrees of freedom independently allowing it to produce manipulator paths of particularly high quality. This new algorithm utilizes an adaptive method of selecting the degree of freedom to optimize at each iteration, giving it a high level of efficiency. Tests conducted show the effectiveness of the algorithm; over a range of different test paths, the adaptive algorithm was able to generate solutions with a 60 % reduction in collision checks compared to the original partial shortcut approach.

3D mapping using a ToF camera for self programming an industrial robot

Automated Offline Programing (AOLP) is a cost effective robot programming method. However, it relies on accurate CAD information of the work environment to perform optimally. Incorrect CAD data is a known source of error for AOLP systems. This paper introduces a new sensor based method of programming that extends the concept of AOLP. Using a ToF camera to map the environment, there is no reliance on CAD data. The problem of motion planning to efficiently map the environment is examined and changes to the motion planning algorithm are proposed and tested.

Path Planning with a Lazy Significant Edge Algorithm (LSEA)

Probabilistic methods have been proven to be effective for robotic path planning in a geometrically complex environment. In this paper, we propose a novel approach, which utilizes a specialized roadmap expansion phase, to improve lazy probabilistic path planning. This expansion phase analyses roadmap connectivity information to bias sampling towards objects in the workspace that have not yet been navigated by the robot. A new method to reduce the number of samples required to navigate narrow passages is also proposed and tested. Experimental results show that the new algorithm is more efficient than the traditional path planning methodologies. It was able to generate solutions for a variety of path planning problems faster, using fewer samples to arrive at a valid solution.

Offline Programming for a Complex Welding System Using DELMIA Automation

This paper presents an offline programming (OLP) system for a complex robotic welding cell using DELMIA Automation. The goals of this research are aimed at investigating the feasibility of taking a commercially available robotic simulation package, DELMIA, and to use a Visual Basic Automation interface to reduce the programming time by creating automated ‘modules’ to carry out some of the tasks in the OLP process. The paper first investigates and presents the structure of OLP as a discreet method of individual steps. These steps are then evaluated for their potential as an automation candidate. The methods in which these steps are automated are then presented. A general analysis of the developed OLP system was carried out, providing a scope for future research and development.

Recent progress on sampling based dynamic motion planning algorithms

This paper reviews recent developments extending sampling based motion planning algorithms to operate in dynamic environments. Sampling based planners provide an effective approach for solving high degree of freedom robot motion planning problems. The two most common algorithms are the Probabilistic Roadmap Method and Rapidly Exploring Random Trees. These standard techniques are well established, however they assume a fully known environment and generate paths ahead of time. For realistic applications a robot may be required to update its path in real-time as information is gained or obstacles change position. Variants of these standard algorithms designed for dynamic environments are categorically presented and common implementation strategies are explored.

Path planning for industrial robots; Lazy Significant Edge Algorithm (LSEA)

This paper presents a new sampling based path planning algorithm, called the Lazy Significant Edge Algorithm (LSEA). LSEA utilises roadmap connectivity information to bias its sampling strategy towards objects in a robots workspace that have not yet been navigated by the robot. This allows LSEA to avoid redundant sampling of configuration space. The robotic system used in this paper to test LSEA consists of an articulated industrial manipulator mounted on a linear rail. LSEA was tested on this system with a series of different path planning problems in order to judge its overall effectiveness. When compared to a number of other popular sampling based path planning algorithms, it was concluded that LSEA had the best overall performance. It was observed to solve the various path planning problems more quickly than its counterparts, utilising fewer clash checks in order to reach the various solutions.

Tandem gas metal arc welding for low distortion butt welds

The feasibility of using Tandem Gas Metal Arc Welding (T-GMAW) to produce full penetration butt welds in 5mm ship panel steel plates has been assessed and compared to the current Submerged Arc Welding (SAW) process. Experiments conducted show that the T-GMAW process is feasible and demonstrated a significant improvement over the SAW process in several areas including higher travel speed, a reduction in filler material, significantly lower post weld distortion, and a smaller Heat Affected Zone (HAZ), while maintaining similar microstructure and mechanical properties in the weld metal and HAZ.

Automated Programming for Robotic Welding

Robotic welding automation allows manufacturers to increase quality, flexibility and reduce costs. However, the costs involved in programming welding robots for small production runs limits viability for Small and Medium Enterprises to employ arc welding automation. This paper outlines an Automated Offline Programming framework which can be used to generate robot programs directly from Computer Aided Design models with minimal human input, allowing programming costs to be drastically reduced or even eliminated. The key stages of our approach are presented and a specific implementation for welding of complex pipe structures is shown. The results demonstrate the feasibility of our method to enable truly flexible robotic welding automation.

Automatic program generation for welding robots from CAD

Industrial robotic automation is a key tool for manufacturing companies to achieve flexibility and low production costs. However, the cost associated with re-programming limits the economic viability for low production volumes. Automated Offline Programming is an approach that uses software algorithms to generate robot programs with little or no human effort. This contrasts with typical programming methods that require considerable human effort from highly skilled operators. This paper presents an Automated Offline Programming solution developed for a steel fabrication company and details the motion planning algorithms. It also describes a novel technique to decompose the welding path motion planning problem into sequential sub-problems such that greedy search techniques can be employed. The results show that this programming approach is effective for robot welding applications and reduces programming effort to effectively no cost.