Thesis Abstract: Path-planning for Industrial Robots Among Multiple Under-specified Tasks

Nowadays industrialized countries with their high labor costs have to rely on production automation to keep their competitive advantage. One of the most flexible and powerful automation technology available today is industrial robotics. Equipped with the right tool, standardized industrial robots can perform numerous production tasks. Since acquisition and programming of an industrial robot are very expensive, the feasibility of using robots in production facilities depends on the efficiency with which the robot can perform its task: the more production steps a robot can perform in a given time interval, the higher the production rates, as a consequence the faster the robot can compensate for its initial acquisition and programming costs, and the higher is the competitive advantage it provides to the company. Virtually all robotics scenarios consist of two different types of robotic movements. The first category includes movements that are specifically required for the job. For example welding a seam, deburring some sharp edge or cutting a shape. These movements are typically the movements, during which the tools (e.g., a welding torch) are switched on. We call this category effective movements or effective tasks. In between two effective movements are supporting movements. Supporting movements are not directly needed for a given job. However, they are necessary to sequence one effective movement after the other. In the example of seam welding, these movements would be to move the robot from welding seam to welding seam. We call these movements supporting movements or supporting tasks. Fig. 1 illustrates this concept on a simple welding example. The two welding seams – (2) and (4) – are effective movements, while (1), (3) and (5) are supporting movements, which are only necessary to execute the effective movements. The major characteristics that affect the efficiency of a given robot are how fast the robot can perform its tasks (effective movements), and how long it takes the robot to move into position to perform a task after having completed the previous one (support movements). While effective movements are usually understood as rigid paths (defined by the task/application), supporting movements are open to computational optimization.