{"title":"A probabilistic neural network-based bimanual control method with multimodal haptic perception fusion","authors":"Xinrui Chi, Zhanbin Guo, Fu Cheng","doi":"10.1016/j.aej.2025.06.024","DOIUrl":null,"url":null,"abstract":"<div><div>In the master-slave robot system, single-modal tactile perception has problems such as collision detection delay (>120 ms), force estimation error (>2.3 N), and sensor conflicts, resulting in a 37 % failure rate of robot operations in nuclear decommissioning scenarios and a 19.2 % risk of excessive tissue compression in laparoscopic surgery. To address this, this paper proposes a multimodal tactile perception fusion control method based on a probabilistic neural network (PNN). Pressure, vibration, and temperature signals are synchronously collected through bionic artificial skin. A hierarchical heterogeneous feature alignment (HHFA) module is designed to solve the spatio-temporal asynchrony of multi-source signals (root mean square error <0.8 ms), and a dynamic Bayesian fusion layer (DBFL) is developed to achieve adaptive weighting based on the entropy-variance coupling index, suppressing noise interference and modal conflicts. The dual-channel PNN encodes the fused sensory information into a Gaussian mixture model (8 components) and generates high-precision control instructions by maximizing the posterior probability. Experiments show that in grasping and fine operation tasks, the positioning error of this method is reduced to 0.15 mm, the operation success rate is increased by 19.6 % (reaching 96.4 %), and the signal-to-noise ratio remains stable at <span><math><mrow><mn>40.2</mn><mo>±</mo><mn>1.5</mn><mi>dB</mi></mrow></math></span> under humidity changes (30–90 %RH) and mechanical strain (15 %).</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"127 ","pages":"Pages 892-919"},"PeriodicalIF":6.2000,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"alexandria engineering journal","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1110016825007653","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
In the master-slave robot system, single-modal tactile perception has problems such as collision detection delay (>120 ms), force estimation error (>2.3 N), and sensor conflicts, resulting in a 37 % failure rate of robot operations in nuclear decommissioning scenarios and a 19.2 % risk of excessive tissue compression in laparoscopic surgery. To address this, this paper proposes a multimodal tactile perception fusion control method based on a probabilistic neural network (PNN). Pressure, vibration, and temperature signals are synchronously collected through bionic artificial skin. A hierarchical heterogeneous feature alignment (HHFA) module is designed to solve the spatio-temporal asynchrony of multi-source signals (root mean square error <0.8 ms), and a dynamic Bayesian fusion layer (DBFL) is developed to achieve adaptive weighting based on the entropy-variance coupling index, suppressing noise interference and modal conflicts. The dual-channel PNN encodes the fused sensory information into a Gaussian mixture model (8 components) and generates high-precision control instructions by maximizing the posterior probability. Experiments show that in grasping and fine operation tasks, the positioning error of this method is reduced to 0.15 mm, the operation success rate is increased by 19.6 % (reaching 96.4 %), and the signal-to-noise ratio remains stable at under humidity changes (30–90 %RH) and mechanical strain (15 %).
期刊介绍:
Alexandria Engineering Journal is an international journal devoted to publishing high quality papers in the field of engineering and applied science. Alexandria Engineering Journal is cited in the Engineering Information Services (EIS) and the Chemical Abstracts (CA). The papers published in Alexandria Engineering Journal are grouped into five sections, according to the following classification:
• Mechanical, Production, Marine and Textile Engineering
• Electrical Engineering, Computer Science and Nuclear Engineering
• Civil and Architecture Engineering
• Chemical Engineering and Applied Sciences
• Environmental Engineering