{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T18:41:50Z","timestamp":1775155310547,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>Robot acceptance is rapidly increasing in many different industrial applications. The advancement of production systems and machines requires addressing the productivity complexity and flexibility of current manufacturing processes in quasi-real time. Nowadays, robot placement is still achieved via industrial practices based on the expertise of the workers and technicians, with the adoption of offline expensive software that demands time-consuming simulations, detailed time-and-motion mapping activities, and high competencies. Current challenges have been addressed mainly via path planning or robot-to-workpiece location optimization. Numerous solutions, from analytical to physical-based and data-driven formulation, have been discussed in the literature to solve these challenges. In this context, the machine learning approach has proven its superior performance. Nevertheless, the industrial environment is complex to model, generating extra training effort and making the learning procedure, in some cases, inefficient. The industrial problems concern workstation productivity; path-constrained minimal-time motions, considering the actuator\u2019s torque limits; followed by robot vibration and the reduction in its accuracy and lifetime. This paper presents a procedure to find the robot base location for a prescribed task within the robot\u2019s workspace, complying with multiple criteria. The proposed hybrid procedure includes analytical, physical-based, and data-driven modeling to solve the optimization problem. The contribution of the algorithm, for a given user-defined task, is the search for the best robot base location that enables the target points, maximizing the manipulability, avoiding singularities, and minimizing energy consumption. Firstly, the established method was verified using an anthropomorphic robot that considers different levels of a priori kinematics and system dynamics knowledge. The feasibility of the proposed method was evaluated through various simulations for small- and medium-sized robots. Then, a commercial offline program was compared, considering three scenarios and fourteen robots demonstrating an energy reduction in the 7.6\u201313.2% range. Moreover, the unknown joint dependency in real robot applications was investigated. From 11 robot positions for each active joint, a direct kinematic was appraised with an automatic DH scheme that generates the 3D workspace with an RMSE lower than 65.0 \u00b5m. Then, the inverse kinematic was computed using an ANN technique tuned with a genetic algorithm showing an RMSE in an S-shape task close to 702.0 \u00b5m. Finally, three experimental campaigns were performed with a set of tasks, repetitions, end-effector velocity, and payloads. The energy consumption reduction was observed in the 12.7\u201322.9% range. Consequently, the proposed procedure supports the reduction in workstation setup time and energy saving during industrial operations.<\/jats:p>","DOI":"10.3390\/robotics13100153","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T07:58:32Z","timestamp":1729065512000},"page":"153","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["6-DOFs Robot Placement Based on the Multi-Criteria Procedure for Industrial Applications"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9414-3763","authenticated-orcid":false,"given":"Francesco","family":"Aggogeri","sequence":"first","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze, 38, 25123 Brescia, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6108-3941","authenticated-orcid":false,"given":"Nicola","family":"Pellegrini","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze, 38, 25123 Brescia, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102002","DOI":"10.1016\/j.rcim.2020.102002","article-title":"A method for robot placement optimization based on two-dimensional manifold in joint space","volume":"67","author":"Li","year":"2021","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1117\/1.OE.56.3.034111","article-title":"Online absolute pose compensation and steering control of industrial robot based on six degrees of freedom laser measurement","volume":"56","author":"Yang","year":"2017","journal-title":"Opt. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.precisioneng.2009.01.002","article-title":"Calibration-based thermal error model for articulated arm coordinate measuring machines","volume":"33","author":"Santolaria","year":"2009","journal-title":"Precis. Eng."},{"key":"ref_4","first-page":"2180","article-title":"Review on Research Status of Positioning Accuracy Reliability of Industrial Robots","volume":"31","author":"Wu","year":"2020","journal-title":"China Mech. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.jmapro.2020.04.085","article-title":"A survey of welding robot intelligent path optimization","volume":"63","author":"Wang","year":"2021","journal-title":"J. Manuf. Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6117","DOI":"10.1109\/TIE.2018.2874587","article-title":"Diversity-Based Cooperative Multivehicle Path Planning for Risk Management in Costmap Environments","volume":"66","author":"Votion","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"81","DOI":"10.5772\/60715","article-title":"Pseudo-bacterial Potential Field Based Path Planner for Autonomous Mobile Robot Navigation","volume":"12","author":"Montiel","year":"2015","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6932","DOI":"10.1109\/LRA.2020.3026638","article-title":"Mobile Robot Path Planning in Dynamic Environments Through Globally Guided Reinforcement Learning","volume":"5","author":"Wang","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2378","DOI":"10.1109\/LRA.2019.2903261","article-title":"Primal: Pathfinding via reinforcement and imitation multiagent learning","volume":"4","author":"Sartoretti","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, Q., Gama, F., Ribeiro, A., and Prorok, A. (2020, January 25\u201329). Graph neural networks for decentralized multi-robot path planning. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341668"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kaczmarek, W., Borys, S., Panasiuk, J., Siwek, M., and Prusaczyk, P. (2022). Experimental Study of the Vibrations of a Roller Shutter Gripper. Appl. Sci., 12.","DOI":"10.3390\/app12199996"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bucinskas, V., Dzedzickis, A., Sumanas, M., Sutinys, E., Petkevicius, S., Butkiene, J., Virzonis, D., and Morkvenaite-Vilkonciene, I. (2022). Improving Industrial Robot Positioning Accuracy to the Microscale Using Machine Learning Method. Machines, 10.","DOI":"10.3390\/machines10100940"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"ref_14","unstructured":"Sutton, R.S., and Barto, A.G. (2018). Introduction to Reinforcement Learning, MIT Press."},{"key":"ref_15","unstructured":"Pan, X., Wang, W., Zhang, X., Li, B., Yi, J., and Song, D. (2019, January 13\u201317). How you act tells a lot: Privacy-leaking attack on deep reinforcement learning. Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems, Montreal, QC, Canada."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1017\/S0263574701003708","article-title":"Optimal location of a robot path when considering velocity performance","volume":"20","author":"Aspragathos","year":"2002","journal-title":"Robotica"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1108\/IR-08-2021-0167","article-title":"Error compensation based on surface reconstruction for industrial robot on two-dimensional manifold","volume":"49","author":"Li","year":"2022","journal-title":"Ind. Robot"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1016\/j.mechmachtheory.2010.03.008","article-title":"Multi-objective path placement optimization of parallel kinematics machines based on energy consumption, shaking forces and maximum actuator torques: Application to the Orthoglide","volume":"45","author":"Caro","year":"2010","journal-title":"Mech. Mach. Theory"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.jmsy.2023.01.011","article-title":"Scheduling a dual gripper material handling robot with energy considerations","volume":"67","author":"Gultekin","year":"2023","journal-title":"J. Manuf. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.jmsy.2023.05.019","article-title":"Energy-efficient and quality-aware part placement in robotic additive manufacturing","volume":"68","author":"Ghungrad","year":"2023","journal-title":"J. Manuf. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.rcim.2017.04.007","article-title":"Optimal robot placement with consideration of redundancy problem for wrist-partitioned 6R articulated robots","volume":"48","author":"Doan","year":"2017","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3135","DOI":"10.1007\/s00170-019-03391-0","article-title":"A convex programming approach to the base placement of a 6-DOF articulated robot with a spherical wrist","volume":"102","author":"Son","year":"2019","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1109\/TASE.2016.2612694","article-title":"A method for optimizing the base position of mobile painting manipulators","volume":"14","author":"Ren","year":"2017","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.rcim.2018.05.007","article-title":"Base position optimization for mobile painting robot manipulators with multiple constraints","volume":"54","author":"Yu","year":"2018","journal-title":"Robot Comput. Integr. Manuf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.jmsy.2015.03.006","article-title":"Novel integrated offline trajectory generation approach for robot assisted spray painting operation","volume":"37","author":"Andulkar","year":"2015","journal-title":"J. Manuf. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/j.jmsy.2023.08.017","article-title":"Towards region-based robotic machining system from perspective of intelligent manufacturing: A technology framework with case study","volume":"70","author":"Wang","year":"2023","journal-title":"J. Manuf. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.procir.2016.02.105","article-title":"Optimal robot placement for tasks execution","volume":"44","author":"Spensieri","year":"2016","journal-title":"Procedia CIRP"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Malhan, R.K., Kabir, A.M., Shah, B., and Gupta, S.K. (2019, January 20\u201324). Identifying feasible workpiece placement with respect to redundant manipulator for complex manufacturing tasks. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8794353"},{"key":"ref_29","unstructured":"(2024, October 10). Available online: https:\/\/www.iso.org\/committee\/5915511.html."},{"key":"ref_30","unstructured":"(2024, October 10). Available online: https:\/\/www.iso.org\/standard\/51330.html."},{"key":"ref_31","unstructured":"(2024, October 10). Available online: https:\/\/www.iso.org\/standard\/41571.html."},{"key":"ref_32","unstructured":"(2024, October 10). Available online: https:\/\/webstore.ansi.org\/standards\/ria\/riatrr155062014."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tagliani, F.L., Pellegrini, N., and Aggogeri, F. (2022). Machine Learning Sequential Methodology for Robot Inverse Kinematic Modeling. Appl. Sci., 12.","DOI":"10.3390\/app12199417"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Faria, C., Vila\u00e7a, J.L., Monteiro, S., Erlhagen, W., and Bicho, E. (2019, January 14\u201317). Automatic Denavit-Hartenberg Parameter Identification for Serial Manipulators. Proceedings of the IECON 2019\u201445th Annual Conference of the IEEE Industrial Electronics Society, Lisbon, Portugal.","DOI":"10.1109\/IECON.2019.8927455"},{"key":"ref_35","unstructured":"Aggogeri, F., Pellegrini, N., Taesi, C., and Tagliani, F.L. (2022, January 7\u20139). Inverse kinematic solver based on machine learning sequential procedure for robotic applications. Proceedings of the 2021 International Symposium on Intelligent Robotics and Systems (ISoIRS 2021), Online."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Liu, Y., He, Y., Wu, W., Wang, H., Huang, C., and Ye, B. (2022). Fuzzy PID Control of the Three-Degree-of-Freedom Parallel Mechanism Based on Genetic Algorithm. Appl. Sci., 12.","DOI":"10.3390\/app122111128"},{"key":"ref_37","unstructured":"Teodoro, I.-P. (2021). A Novel Optimization Robust Design of Artificial Neural Networks to Solve the Inverse Kinematics of a Manipulator of 6 DOF, IEEE."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"106682","DOI":"10.1016\/j.cie.2020.106682","article-title":"An analytical and a deep learning model for solving the inverse kinematic problem of an industrial parallel robot","volume":"151","author":"Toquica","year":"2020","journal-title":"Comput. Ind. Eng."},{"key":"ref_39","first-page":"51","article-title":"A comparison of optimization algorithms for deep learning","volume":"34","author":"Derya","year":"2020","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.rcim.2009.07.003","article-title":"Optimal location of a general position and orientation end-effector\u2019s path relative to manipulator\u2019s base, considering velocity performance, Robot","volume":"26","author":"Nektarios","year":"2010","journal-title":"Comput.-Integr. Manuf."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1177\/0954408915608755","article-title":"A simulation-based method using artificial neural networks for solving the inverse kinematic problem of articulated robots","volume":"231","author":"Soylak","year":"2017","journal-title":"Proc. Inst. Mech. Eng. Part E J. Process Mech. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.jmsy.2013.09.006","article-title":"Dual hierarchical genetic-optimal control: A new global optimal path planning method for robots","volume":"33","author":"Azimirad","year":"2014","journal-title":"J. Manuf. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.jmsy.2011.08.003","article-title":"Evolutionary optimization of robotic assembly operation sequencing with collision-free paths","volume":"30","author":"Givehchi","year":"2011","journal-title":"J. Manuf. Syst."}],"container-title":["Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-6581\/13\/10\/153\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:14:21Z","timestamp":1760112861000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-6581\/13\/10\/153"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"references-count":43,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["robotics13100153"],"URL":"https:\/\/doi.org\/10.3390\/robotics13100153","relation":{},"ISSN":["2218-6581"],"issn-type":[{"value":"2218-6581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}