{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T16:26:19Z","timestamp":1771863979502,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T00:00:00Z","timestamp":1752192000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T00:00:00Z","timestamp":1752192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001509","name":"Royal Society Te Ap\u0101rangi","doi-asserted-by":"publisher","award":["23-VUW-019-JVH"],"award-info":[{"award-number":["23-VUW-019-JVH"]}],"id":[{"id":"10.13039\/501100001509","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["OP der Zukunft"],"award-info":[{"award-number":["OP der Zukunft"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["OP der Zukunft"],"award-info":[{"award-number":["OP der Zukunft"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["OP der Zukunft"],"award-info":[{"award-number":["OP der Zukunft"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["OP der Zukunft"],"award-info":[{"award-number":["OP der Zukunft"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002869","name":"Christian-Albrechts-Universit\u00e4t zu Kiel","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002869","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Monitoring robotic manipulators is essential in highly automated environments, where optical cameras can provide precise and dense information while suffering from line-of-sight occlusions. This paper introduces a lightweight, learning-based solution to address these occlusions in robot-camera systems. We employ a small MLP (Multilayer Perceptron) to learn occluded spaces and use its gradients for active occlusion avoidance. To generate training data, we use smooth random robot trajectories that uniformly sample the robot\u2019s configuration space. Additionally, we reduce data acquisition time by modifying the state-of-the-art 3D Gaussian Splatting (3DGS) method to create a near-photorealistic model of the manipulator for generating extensive training datasets. Our experiments show that the proposed approach achieves a balanced accuracy of 94.7 %, which improves upon a previous method by 20 % while reducing the sampling time from 4.5 h to 5 min. We demonstrate the application of the proposed method in two real-world test cases and achieve continuous visibility. Through the use of rigid 3DGS, we significantly improved upon previous results. By reducing the data sampling duration from hours to minutes, the practicality and applicability of our approach in real-world scenarios have been substantially enhanced.<\/jats:p>","DOI":"10.1007\/s42979-025-04151-6","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T10:49:29Z","timestamp":1752230969000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Occlusion Avoidance for Robotic Manipulators Using Rigid Gaussian Splatting"],"prefix":"10.1007","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6800-2462","authenticated-orcid":false,"given":"Jakob","family":"Nazarenus","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8951-8962","authenticated-orcid":false,"given":"Simon","family":"Reichhuber","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4398-1569","authenticated-orcid":false,"given":"Reinhard","family":"Koch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5825-8915","authenticated-orcid":false,"given":"Sven","family":"Tomforde","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7222-2214","authenticated-orcid":false,"given":"Simin","family":"Kou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8728-8726","authenticated-orcid":false,"given":"Fang-Lue","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,11]]},"reference":[{"issue":"12","key":"4151_CR1","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/MSPEC.2019.8913831","volume":"56","author":"E Guizzo","year":"2019","unstructured":"Guizzo E. By leaps and bounds: An exclusive look at how boston dynamics is redefining robot agility. IEEE Spectr. 2019;56(12):34\u20139.","journal-title":"IEEE Spectr"},{"key":"4151_CR2","doi-asserted-by":"crossref","unstructured":"Fan X, Simmons-Edler R, Lee D, Jackel L, Howard R, Lee D. Aurasense: Robot collision avoidance by full surface proximity detection. In: 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021. p. 1763\u20131770. IEEE.","DOI":"10.1109\/IROS51168.2021.9635919"},{"issue":"4-5","key":"4151_CR3","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1177\/0278364917710318","volume":"37","author":"S Levine","year":"2018","unstructured":"Levine S, Pastor P, Krizhevsky A, Ibarz J, Quillen D. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. Int J Robot Res. 2018;37(4\u20135):421\u201336.","journal-title":"Int J Robot Res"},{"key":"4151_CR4","doi-asserted-by":"crossref","unstructured":"Tremblay J, Tyree S, Mosier T, Birchfield S. Indirect object-to-robot pose estimation from an external monocular rgb camera. in 2020 ieee. In: RSJ International Conference on Intelligent Robots and Systems (IROS), 2020. p. 4227\u201334.","DOI":"10.1109\/IROS45743.2020.9341163"},{"key":"4151_CR5","unstructured":"Yu J, Ren X, Gu Y, Lin H, Wang T, Zhu Y, Xu H, Jiang Y-G, Xue X, Fu Y. Sparsegrasp: Robotic grasping via 3d semantic gaussian splatting from sparse multi-view rgb images. 2024. arXiv preprint arXiv:2412.02140."},{"key":"4151_CR6","doi-asserted-by":"crossref","unstructured":"Nazarenus J, Reichhuber S, Amersdorfer M, Elsner L, Koch R, Tomforde S, Abbas H. Learning occlusions in robotic systems: How to prevent robots from hiding themselves. In: Proceedings of the 16th International Conference on Agents and Artificial Intelligence, 2024. vol. 2, pp. 482\u201392.","DOI":"10.5220\/0012431000003636"},{"issue":"4","key":"4151_CR7","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3592433","volume":"42","author":"B Kerbl","year":"2023","unstructured":"Kerbl B, Kopanas G, Leimk\u00fchler T, Drettakis G. 3d gaussian splatting for real-time radiance field rendering. ACM Trans Graph. 2023;42(4):139\u20131.","journal-title":"ACM Trans Graph"},{"issue":"3-4","key":"4151_CR8","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/S0921-8890(01)00119-1","volume":"35","author":"C Silva","year":"2001","unstructured":"Silva C, Santos-Victor J. Motion from occlusions. Robot Auton Syst. 2001;35(3\u20134):153\u201362.","journal-title":"Robot Auton Syst"},{"key":"4151_CR9","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1007\/s11263-011-0490-7","volume":"97","author":"A Ayvaci","year":"2012","unstructured":"Ayvaci A, Raptis M, Soatto S. Sparse occlusion detection with optical flow. Int J Comput Vision. 2012;97:322\u201338.","journal-title":"Int J Comput Vision"},{"key":"4151_CR10","doi-asserted-by":"crossref","unstructured":"Anuj L, Krishna MG. Multiple camera based multiple object tracking under occlusion: A survey. In: 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), 2017. p. 432\u201337. IEEE.","DOI":"10.1109\/ICIMIA.2017.7975652"},{"key":"4151_CR11","doi-asserted-by":"crossref","unstructured":"Boroushaki T, Leng J, Clester I, Rodriguez A, Adib F. Robotic grasping of fully-occluded objects using rf perception. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021. p. 923\u201329. IEEE.","DOI":"10.1109\/ICRA48506.2021.9560956"},{"issue":"11","key":"4151_CR12","doi-asserted-by":"publisher","first-page":"3082","DOI":"10.3390\/s20113082","volume":"20","author":"A Shintemirov","year":"2020","unstructured":"Shintemirov A, Taunyazov T, Omarali B, Nurbayeva A, Kim A, Bukeyev A, Rubagotti M. An open-source 7-dof wireless human arm motion-tracking system for use in robotics research. Sensors. 2020;20(11):3082.","journal-title":"Sensors"},{"issue":"1","key":"4151_CR13","doi-asserted-by":"publisher","first-page":"11204","DOI":"10.1038\/s41598-021-90523-w","volume":"11","author":"SS Vedaei","year":"2021","unstructured":"Vedaei SS, Wahid KA. A localization method for wireless capsule endoscopy using side wall cameras and imu sensor. Sci Rep. 2021;11(1):11204.","journal-title":"Sci Rep"},{"key":"4151_CR14","doi-asserted-by":"crossref","unstructured":"Gandhi D, Cervera E. Sensor covering of a robot arm for collision avoidance. In: SMC\u201903 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme-System Security and Assurance (Cat. No. 03CH37483), 2003. vol. 5, p. 4951\u201355. IEEE.","DOI":"10.1109\/ICSMC.2003.1245767"},{"key":"4151_CR15","doi-asserted-by":"publisher","DOI":"10.1108\/ir-06-2018-0128","author":"Z Zheng","year":"2018","unstructured":"Zheng Z, Ma Y, Zheng H, Gu Y, Lin M. Industrial part localization and grasping using a robotic arm guided by 2d monocular vision. Industrial Robot: Int J. 2018. https:\/\/doi.org\/10.1108\/ir-06-2018-0128.","journal-title":"Industrial Robot: Int J"},{"key":"4151_CR16","doi-asserted-by":"crossref","unstructured":"Danielczuk M, Angelova A, Vanhoucke V, Goldberg K. X-ray: Mechanical search for an occluded object by minimizing support of learned occupancy distributions. In: 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020. p. 9577\u201384. IEEE.","DOI":"10.1109\/IROS45743.2020.9340984"},{"key":"4151_CR17","doi-asserted-by":"crossref","unstructured":"Schonberger JL, Frahm J-M. Structure-from-motion revisited. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. p. 4104\u201313.","DOI":"10.1109\/CVPR.2016.445"},{"issue":"1","key":"4151_CR18","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1145\/3503250","volume":"65","author":"B Mildenhall","year":"2021","unstructured":"Mildenhall B, Srinivasan PP, Tancik M, Barron JT, Ramamoorthi R, Ng R. Nerf: Representing scenes as neural radiance fields for view synthesis. Commun ACM. 2021;65(1):99\u2013106.","journal-title":"Commun ACM"},{"key":"4151_CR19","doi-asserted-by":"crossref","unstructured":"Reiser C, Peng S, Liao Y, Geiger A. Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021. p. 14335\u201345.","DOI":"10.1109\/ICCV48922.2021.01407"},{"key":"4151_CR20","doi-asserted-by":"crossref","unstructured":"Yu A, Li R, Tancik M, Li H, Ng R, Kanazawa A. Plenoctrees for real-time rendering of neural radiance fields. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021. p. 5752\u201361.","DOI":"10.1109\/ICCV48922.2021.00570"},{"key":"4151_CR21","doi-asserted-by":"crossref","unstructured":"Fridovich-Keil S, Yu A, Tancik M, Chen Q, Recht B, Kanazawa A. Plenoxels: Radiance fields without neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022. p. 5501\u201310.","DOI":"10.1109\/CVPR52688.2022.00542"},{"issue":"4","key":"4151_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3528223.3530127","volume":"41","author":"T M\u00c3\u00bcller","year":"2022","unstructured":"M\u00c3\u00bcller T, Evans A, Schied C, Keller A. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans Graph (TOG). 2022;41(4):1\u201315.","journal-title":"ACM Trans Graph (TOG)"},{"key":"4151_CR23","doi-asserted-by":"crossref","unstructured":"Luiten J, Kopanas G, Leibe B, Ramanan D. Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis. In: 3DV. 2024.","DOI":"10.1109\/3DV62453.2024.00044"},{"key":"4151_CR24","doi-asserted-by":"crossref","unstructured":"Wu G, Yi T, Fang J, Xie L, Zhang X, Wei W, Liu W, Tian Q, Wang X. 4d gaussian splatting for real-time dynamic scene rendering. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. p. 20310\u201320.","DOI":"10.1109\/CVPR52733.2024.01920"},{"key":"4151_CR25","doi-asserted-by":"crossref","unstructured":"Brodersen KH, Ong CS, Stephan KE, Buhmann JM. The balanced accuracy and its posterior distribution. In: 2010 20th International Conference on Pattern Recognition, 2010. p. 3121\u20134. IEEE.","DOI":"10.1109\/ICPR.2010.764"},{"key":"4151_CR26","unstructured":"Rijsbergen Cv. Information retrieval 2nd ed buttersworth. London [Google Scholar] 1979;115."},{"issue":"5","key":"4151_CR27","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1109\/70.631234","volume":"13","author":"J Swevers","year":"1997","unstructured":"Swevers J, Ganseman C, Tukel DB, Schutter J, Van Brussel H. Optimal robot excitation and identification. IEEE Trans Robot Autom. 1997;13(5):730\u201340. https:\/\/doi.org\/10.1109\/70.631234.","journal-title":"IEEE Trans Robot Autom"},{"issue":"4A","key":"4151_CR28","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1115\/1.2831194","volume":"119","author":"J Swevers","year":"1997","unstructured":"Swevers J, Ganseman C, De Schutter J, Van Brussel H. Generation of periodic trajectories for optimal robot excitation. J Manuf Sci Eng. 1997;119(4A):611\u201315. https:\/\/doi.org\/10.1115\/1.2831194.","journal-title":"J Manuf Sci Eng"},{"issue":"5","key":"4151_CR29","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1017\/S0263574706002712","volume":"24","author":"K-J Park","year":"2006","unstructured":"Park K-J. Fourier-based optimal excitation trajectories for the dynamic identification of robots. Robotica. 2006;24(5):625\u201333. https:\/\/doi.org\/10.1017\/S0263574706002712.","journal-title":"Robotica"},{"issue":"1","key":"4151_CR30","doi-asserted-by":"publisher","first-page":"6863","DOI":"10.1016\/j.ifacol.2017.08.1208","volume":"50","author":"YR St\u00fcrz","year":"2017","unstructured":"St\u00fcrz YR, Affolter LM, Smith RS. Parameter identification of the kuka lbr iiwa robot including constraints on physical feasibility. IFAC-PapersOnLine. 2017;50(1):6863\u201368. https:\/\/doi.org\/10.1016\/j.ifacol.2017.08.1208. (20th IFAC World Congress).","journal-title":"IFAC-PapersOnLine"},{"issue":"1","key":"4151_CR31","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1137\/17M1161853","volume":"61","author":"S Filip","year":"2019","unstructured":"Filip S, Javeed A, Trefethen LN. Smooth random functions, random odes, and gaussian processes. SIAM Rev. 2019;61(1):185\u2013205.","journal-title":"SIAM Rev"},{"key":"4151_CR32","doi-asserted-by":"crossref","unstructured":"Bousmalis K, Irpan A, Wohlhart P, Bai Y, Kelcey M, Kalakrishnan M, Downs L, Ibarz J, Pastor P, Konolige K. Using simulation and domain adaptation to improve efficiency of deep robotic grasping. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018. p. 4243\u201350. IEEE.","DOI":"10.1109\/ICRA.2018.8460875"},{"key":"4151_CR33","volume-title":"Pattern recognition and machine learning","author":"CM Bishop","year":"2006","unstructured":"Bishop CM, Nasrabadi NM. Pattern recognition and machine learning, vol. 4. New York, NY: Springer; 2006."},{"issue":"1","key":"4151_CR34","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover T, Hart P. Nearest neighbor pattern classification. IEEE Trans Inf Theory. 1967;13(1):21\u20137.","journal-title":"IEEE Trans Inf Theory"},{"key":"4151_CR35","doi-asserted-by":"publisher","DOI":"10.1201\/9781315373577","volume-title":"Mixture model-based classification","author":"PD McNicholas","year":"2016","unstructured":"McNicholas PD. Mixture model-based classification. Boca Raton, FL: Chapman and Hall\/CRC; 2016."},{"key":"4151_CR36","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1023\/A:1022627411411","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20:273\u201397.","journal-title":"Mach Learn"},{"key":"4151_CR37","doi-asserted-by":"publisher","DOI":"10.1002\/0470011815.b2a14038","volume-title":"Support vector machines","author":"B Sch\u00f6lkopf","year":"2005","unstructured":"Sch\u00f6lkopf B, Smola A. Support vector machines. Chichester, UK: John Wiley & Sons, Ltd; 2005. https:\/\/doi.org\/10.1002\/0470011815.b2a14038."},{"issue":"2","key":"4151_CR38","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/0893-6080(91)90009-T","volume":"4","author":"K Hornik","year":"1991","unstructured":"Hornik K. Approximation capabilities of multilayer feedforward networks. Neural Netw. 1991;4(2):251\u201357.","journal-title":"Neural Netw"},{"key":"4151_CR39","unstructured":"Kingma DP, Ba J. Adam: A method for stochastic optimization. 2014. arXiv preprint arXiv:1412.6980."},{"key":"4151_CR40","first-page":"230","volume":"2","author":"L Li","year":"2020","unstructured":"Li L, Jamieson K, Rostamizadeh A, Gonina E, Ben-Tzur J, Hardt M, Recht B, Talwalkar A. A system for massively parallel hyperparameter tuning. Proceed Mach Learn Syst. 2020;2:230\u201346.","journal-title":"Proceed Mach Learn Syst"},{"key":"4151_CR41","doi-asserted-by":"crossref","unstructured":"Akiba T, Sano S, Yanase T, Ohta T, Koyama M. Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2019.","DOI":"10.1145\/3292500.3330701"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04151-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-025-04151-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04151-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T05:23:49Z","timestamp":1757222629000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-025-04151-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,11]]},"references-count":41,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["4151"],"URL":"https:\/\/doi.org\/10.1007\/s42979-025-04151-6","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,11]]},"assertion":[{"value":"21 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Jakob Nazarenus, Simon Reichhuber, Reinhard Koch, and Sven Tomforde are employed at Kiel University\u00a0(,\u00a0accessed June 24, 2025). Fang-Lue Zhang is employed at Victoria University of Wellington\u00a0(,\u00a0accessed June 24, 2025). Simin Kou has been studying at Victoria University of Wellington and Hangzhou Dianzi University\u00a0(,\u00a0accessed June 24, 2025).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"628"}}