Joseph Polden

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.

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.

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.

Bringing path planning and lean automation together

In order to achieve higher productivity and flexibility, manufacturing industry is turning increasingly to robotics based lean automation systems. This lean approach presents a series of new challenges for the control, operation and programming of robotic hardware implemented to carry out a range of manufacturing processes. This paper reviews relevant path planning methodologies alongside a specific set of requirements for a manipulator operating in a lean automation workcell. Then, new challenges to path planning for a lean automation system are presented. Finally, a framework for a new path planner is developed and its performance is compared to existing methods.