Abstract
Industrial robot applications should be designed to allow the robot to provide the best performance for increasing throughput. In this regard, both trajectory and task order optimization are crucial, since they can heavily impact cycle time. Moreover, it is very common for a robotic application to be kinematically or functionally redundant so that multiple arm configurations may fulfill the same task at the working points. In this context, even if the working cycle is composed of a small number of points, the number of possible sequences can be very high, so that the robot programmer usually cannot evaluate them all to obtain the shortest possible cycle time. One of the most well-known problems used to define the optimal task order is the Travelling Salesman Problem (TSP), but in its original formulation, it does not allow to consider different robot configurations at the same working point. This paper aims at overcoming TSP limitations by adding some mathematical and conceptual constraints to the problem. With such improvements, TSP can be used successfully to optimize the cycle time of industrial robotic tasks where multiple configurations are allowed at the working points. Simulation and experimental results are presented to assess how cost (cycle time) and computational time are influenced by the proposed implementation.
Highlights
The most common industrial robot applications are related to the handling of products, the welding, and the assembly of parts [1]
The performance of the robotic workcell is usually limited by the performance of the robot, i.e., the time required by the robot to move between the points
This paper proposed an improvement to the Traveling Salesman Problem that allows to optimize throughput of a robotic workcell in multi-configuration scenarios, widespread in common industrial robot applications
Summary
The most common industrial robot applications are related to the handling of products, the welding, and the assembly of parts [1]. If a redundant application is considered, the robot can reach a point (i.e., a city) with multiple configurations, requiring different movement times. A designer could be interested in optimizing the performance of the robot by choosing the appropriate configurations in the case of a fixed robot task order, i.e., when the industrial process is composed of a fixed sequence of subtasks In this case, Equation (2) can be simplified as follows:. Increasing the number of possible configurations with the same number of points increases the number of combinations and, is likely to find a path that requires a lower cost to be followed To verify this reasoning, an industrial 6-axis robot (Adept Viper s650) has been exploited, and 20 simulations have been performed.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
