Evaluating robot workspaces with performance maps

Evaluating robot workspaces with performance maps Aline Kluge-Wilkes, Presley Demuner Reverdito The following data set was created while validating a proposed method to evaluate robot workspaces with different performance metrics. The underlying paper will be presented at the CIRP CATS 2024, the 10th Conference on Assembly Technology and Systems, hosted in Karlsruhe from 24-26.April.2024 and published thereafter. Deploying mobile robots in line-less and mobile assembly systems without predefined formations necessitates understanding the robot's capabilities and determining the tasks it can perform. The data set concerns performance maps to assess the feasibility of assembly tasks within the robot's workspace. These maps offer quantifiable metrics to compare the suitability of base placements for mobile robots concerning the feasibility of specific assembly tasks. Performance maps are a discretised representation of a particular robot's distribution of selected performance metrics. The current implementation focuses on calculating manipulability, dexterity, and condition number. Using a URDF robot model and task poses as input, the metrics are computed across distributed poses within the robot's workspace at a specified resolution, forming the performance map. The data set contains the application of the performance map to exemplary tasks and robots. A Design of Experiments (DoE) was conducted to investigate the influence of three predictor variables on robot manipulation performance. The predictor variables considered in the study were as follows: 1. Resolution (R): Two levels of resolution, 0.08m and 0.12m, were examined to assess their impact on robot performance representation. 2. Number of poses (N): Two different quantities of poses, 25 and 50, were studied to understand how they affect the representation of the robot's capabilities. 3. Robot Model: Two distinct robot models, the ABB IRB1600-1.2 and the ABB IRB120-0.58, were chosen for the experiments. Eight experiments were designed and executed, each with a unique combination of the predictor variables. These experiments are summarised in the Excel sheet "DOE-tests-and-results". The first table overviews the eight experiments. The following eight tables indicate the positions and the results: the respective calculated robot's performance metrics. The .json files specify the poses for which the respective performance metrics (dexterity index, reciprocal condition number, and manipulability index) per robot were calculated. The included .png files visualise the distribution of each index for each conducted experiment in a performance map. The maps were created with Matplotlib. As a comparison, the "capability maps" of both robot models with both resolutions --- generated with the ROS (Robot Operating System) library Reuleaux (http://wiki.ros.org/reuleaux) --- are provided. The generated files are given in .h5 format. The respective files are named as "CM_...". ------------------------------------------------------------------------------------------------------------------------------------------------------- Acknowledgement: Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy --- EXC-2023 Internet of Production --- 390621612.