Pub Date : 2024-11-14DOI: 10.1016/j.birob.2024.100193
Ziwu Ren, Zhongyuan Wang, Xiaohan Liu, Rui Lin
A physically feasible, reliable, and safe motion is essential for robot operation. A parameterization-based trajectory planning approach is proposed for an 8-DOF manipulator with multiple constraints. The inverse kinematic solution is obtained through an analytical method, and the trajectory is planned in joint space. As such, the trajectory planning of the 8-DOF manipulator is transformed into a parameterization-based trajectory optimization problem within its physical, obstacle and task constraints, and the optimization variables are significantly reduced. Then teaching–learning-based optimization (TLBO) algorithm is employed to search for the redundant parameters to generate an optimal trajectory. Simulation and physical experiment results demonstrate that this approach can effectively solve the trajectory planning problem of the manipulator. Moreover, the planned trajectory has no theoretical end-effector deviation for the task constraint. This approach can provide a reference for the motion planning of other redundant manipulators.
{"title":"Parameterization-based trajectory planning for an 8-DOF manipulator with multiple constraints","authors":"Ziwu Ren, Zhongyuan Wang, Xiaohan Liu, Rui Lin","doi":"10.1016/j.birob.2024.100193","DOIUrl":"10.1016/j.birob.2024.100193","url":null,"abstract":"<div><div>A physically feasible, reliable, and safe motion is essential for robot operation. A parameterization-based trajectory planning approach is proposed for an 8-DOF manipulator with multiple constraints. The inverse kinematic solution is obtained through an analytical method, and the trajectory is planned in joint space. As such, the trajectory planning of the 8-DOF manipulator is transformed into a parameterization-based trajectory optimization problem within its physical, obstacle and task constraints, and the optimization variables are significantly reduced. Then teaching–learning-based optimization (TLBO) algorithm is employed to search for the redundant parameters to generate an optimal trajectory. Simulation and physical experiment results demonstrate that this approach can effectively solve the trajectory planning problem of the manipulator. Moreover, the planned trajectory has no theoretical end-effector deviation for the task constraint. This approach can provide a reference for the motion planning of other redundant manipulators.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"5 1","pages":"Article 100193"},"PeriodicalIF":0.0,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-04DOI: 10.1016/j.birob.2024.100192
Yize Ma, Qingxiang Wu, Zehao Qiu, Yongchun Fang, Ning Sun
In recent years, a variety of pneumatic soft actuators (PSAs) have been proposed due to the development of soft robots in biomimetic robots, medical devices, etc. At the same time, the modeling and control of PSAs remains an open question. In this paper, a spatial bending pneumatic soft actuator (SBPSA) modeling method based on the Prandtl–Ishlinskii (PI) model is proposed, and the inverse model is designed to compensate for hysteresis nonlinearity. Furthermore, an adaptive feedback controller combined with a hysteresis compensator is proposed for the precise control and tracking of SBPSAs. Finally, an experimental platform is built, and experimental results demonstrate the effectiveness of the proposed method for precise tracking.
{"title":"Modeling and precise tracking control of spatial bending pneumatic soft actuators","authors":"Yize Ma, Qingxiang Wu, Zehao Qiu, Yongchun Fang, Ning Sun","doi":"10.1016/j.birob.2024.100192","DOIUrl":"10.1016/j.birob.2024.100192","url":null,"abstract":"<div><div>In recent years, a variety of pneumatic soft actuators (PSAs) have been proposed due to the development of soft robots in biomimetic robots, medical devices, etc. At the same time, the modeling and control of PSAs remains an open question. In this paper, a spatial bending pneumatic soft actuator (SBPSA) modeling method based on the Prandtl–Ishlinskii (PI) model is proposed, and the inverse model is designed to compensate for hysteresis nonlinearity. Furthermore, an adaptive feedback controller combined with a hysteresis compensator is proposed for the precise control and tracking of SBPSAs. Finally, an experimental platform is built, and experimental results demonstrate the effectiveness of the proposed method for precise tracking.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"4 4","pages":"Article 100192"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-28DOI: 10.1016/j.birob.2024.100191
Yanqiang Lei , Fuxin Du , Huajian Song , Liping Zhang
The friction between the joints of the continuum manipulator with discrete joints brings great difficulties to kinematic modeling. The traditional driving wire arrangement limits the load capacity of the manipulator. A cable-stayed notch manipulator for transluminal endoscopic surgery is proposed, and a driving force coupling kinematic mode is established. The manipulator is fabricated from a superelastic Nitinol tube with bilaterally cut rectangular notches and is actuated by a stay cable. By applying the comprehensive elliptic integral solution (CEIS) for large deformation beams, the bending angle of each elastic beam is obtained, and the kinematics from the driving space to the joint space is formed. According to the bending angle of each elastic beam, the expression of the manipulator in Cartesian space can be obtained by geometric analysis. The kinematics from the joint space to the Cartesian space is established. The outer diameter of the manipulator is only 3.5 mm, and the inner diameter can reach 2 mm, allowing instruments to pass through. The maximum error of the manipulator movement is less than 5%. The load capacity of the manipulator has been verified through the stiffness experiments, and the maximum load of the manipulator can reach 400 g. The cable-stayed notch manipulator can be accurately modeled on the base of CEIS, and its motion accuracy can meet the needs of engineering applications. The compact size and excellent load capacity of the manipulator make it potential for application in transluminal endoscopic surgical robots.
{"title":"Design and kinematics analysis of a cable-stayed notch manipulator for transluminal endoscopic surgery","authors":"Yanqiang Lei , Fuxin Du , Huajian Song , Liping Zhang","doi":"10.1016/j.birob.2024.100191","DOIUrl":"10.1016/j.birob.2024.100191","url":null,"abstract":"<div><div>The friction between the joints of the continuum manipulator with discrete joints brings great difficulties to kinematic modeling. The traditional driving wire arrangement limits the load capacity of the manipulator. A cable-stayed notch manipulator for transluminal endoscopic surgery is proposed, and a driving force coupling kinematic mode is established. The manipulator is fabricated from a superelastic Nitinol tube with bilaterally cut rectangular notches and is actuated by a stay cable. By applying the comprehensive elliptic integral solution (CEIS) for large deformation beams, the bending angle of each elastic beam is obtained, and the kinematics from the driving space to the joint space is formed. According to the bending angle of each elastic beam, the expression of the manipulator in Cartesian space can be obtained by geometric analysis. The kinematics from the joint space to the Cartesian space is established. The outer diameter of the manipulator is only 3.5 mm, and the inner diameter can reach 2 mm, allowing instruments to pass through. The maximum error of the manipulator movement is less than 5%. The load capacity of the manipulator has been verified through the stiffness experiments, and the maximum load of the manipulator can reach 400 g. The cable-stayed notch manipulator can be accurately modeled on the base of CEIS, and its motion accuracy can meet the needs of engineering applications. The compact size and excellent load capacity of the manipulator make it potential for application in transluminal endoscopic surgical robots.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"4 4","pages":"Article 100191"},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-26DOI: 10.1016/j.birob.2024.100190
Hongyu Zhang , Guoliang Li , Dapeng Wan , Ziyue Wang , Jinshun Dong , Shoujun Lin , Lixia Deng , Haiying Liu
In the field of security, intelligent surveillance tasks often involve a large number of dense and small objects, with severe occlusion between them, making detection particularly challenging. To address this significant challenge, Dense and Small YOLO (DS-YOLO), a dense small object detection algorithm based on YOLOv8s, is proposed in this paper. Firstly, to enhance the dense small objects’ feature extraction capability of backbone network, the paper proposes a lightweight backbone. The improved C2fUIB is employed to create a lightweight model and expand the receptive field, enabling the capture of richer contextual information and reducing the impact of occlusion on detection accuracy. Secondly, to enhance the feature fusion capability of model, a multi-scale feature fusion network, Light-weight Full Scale PAFPN (LFS-PAFPN), combined with the DO-C2f module, is introduced. The new module successfully reduces the miss rate of dense small objects while ensuring the accuracy of detecting large objects. Finally, to minimize feature loss of dense objects during network transmission, a dynamic upsampling module, DySample, is implemented. DS-YOLO was trained and tested on the CrowdHuman and VisDrone2019 datasets, which contain a large number of densely populated pedestrians, vehicles and other objects. Experimental evaluations demonstrated that DS-YOLO has advantages in dense small object detection tasks. Compared with YOLOv8s, the Recall and [email protected] are increased by 4.9% and 4.2% on CrowdHuman dataset, 4.6% and 5% on VisDrone2019, respectively. Simultaneously, DS-YOLO does not introduce a substantial amount of computing overhead, maintaining low hardware requirements.
{"title":"DS-YOLO: A dense small object detection algorithm based on inverted bottleneck and multi-scale fusion network","authors":"Hongyu Zhang , Guoliang Li , Dapeng Wan , Ziyue Wang , Jinshun Dong , Shoujun Lin , Lixia Deng , Haiying Liu","doi":"10.1016/j.birob.2024.100190","DOIUrl":"10.1016/j.birob.2024.100190","url":null,"abstract":"<div><div>In the field of security, intelligent surveillance tasks often involve a large number of dense and small objects, with severe occlusion between them, making detection particularly challenging. To address this significant challenge, Dense and Small YOLO (DS-YOLO), a dense small object detection algorithm based on YOLOv8s, is proposed in this paper. Firstly, to enhance the dense small objects’ feature extraction capability of backbone network, the paper proposes a lightweight backbone. The improved C2fUIB is employed to create a lightweight model and expand the receptive field, enabling the capture of richer contextual information and reducing the impact of occlusion on detection accuracy. Secondly, to enhance the feature fusion capability of model, a multi-scale feature fusion network, Light-weight Full Scale PAFPN (LFS-PAFPN), combined with the DO-C2f module, is introduced. The new module successfully reduces the miss rate of dense small objects while ensuring the accuracy of detecting large objects. Finally, to minimize feature loss of dense objects during network transmission, a dynamic upsampling module, DySample, is implemented. DS-YOLO was trained and tested on the CrowdHuman and VisDrone2019 datasets, which contain a large number of densely populated pedestrians, vehicles and other objects. Experimental evaluations demonstrated that DS-YOLO has advantages in dense small object detection tasks. Compared with YOLOv8s, the Recall and [email protected] are increased by 4.9% and 4.2% on CrowdHuman dataset, 4.6% and 5% on VisDrone2019, respectively. Simultaneously, DS-YOLO does not introduce a substantial amount of computing overhead, maintaining low hardware requirements.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"4 4","pages":"Article 100190"},"PeriodicalIF":0.0,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-18DOI: 10.1016/j.birob.2024.100189
Timothy Sellers , Tingjun Lei , Chaomin Luo , Zhuming Bi , Gene Eu Jan
In the field of autonomous robots, achieving complete precision is challenging, underscoring the need for human intervention, particularly in ensuring safety. Human Autonomy Teaming (HAT) is crucial for promoting safe and efficient human–robot collaboration in dynamic indoor environments. This paper introduces a framework designed to address these precision gaps, enhancing safety and robotic interactions within such settings. Central to our approach is a hybrid graph system that integrates the Generalized Voronoi Diagram (GVD) with spatio-temporal graphs, effectively combining human feedback, environmental factors, and key waypoints. An integral component of this system is the improved Node Selection Algorithm (iNSA), which utilizes the revised Grey Wolf Optimization (rGWO) for better adaptability and performance. Furthermore, an obstacle tracking model is employed to provide predictive data, enhancing the efficiency of the system. Human insights play a critical role, from supplying initial environmental data and determining key waypoints to intervening during unexpected challenges or dynamic environmental changes. Extensive simulation and comparison tests confirm the reliability and effectiveness of our proposed model, highlighting its unique advantages in the domain of HAT. This comprehensive approach ensures that the system remains robust and responsive to the complexities of real-world applications.
在自主机器人领域，实现完全精确具有挑战性，这就强调了人类干预的必要性，尤其是在确保安全方面。人类自主团队（HAT）对于促进动态室内环境中安全高效的人机协作至关重要。本文介绍了一个旨在解决这些精度差距的框架，以增强此类环境中的安全性和机器人互动。我们方法的核心是一个混合图系统，它将广义伏罗诺图（GVD）与时空图整合在一起，有效地结合了人类反馈、环境因素和关键航点。该系统的一个组成部分是改进的节点选择算法（iNSA），该算法采用了经修订的灰狼优化算法（rGWO），具有更好的适应性和性能。此外，还采用了障碍物跟踪模型来提供预测数据，从而提高了系统的效率。从提供初始环境数据和确定关键航点，到在意外挑战或动态环境变化时进行干预，人类的洞察力发挥着至关重要的作用。广泛的模拟和对比测试证实了我们所建议的模型的可靠性和有效性，凸显了其在 HAT 领域的独特优势。这种全面的方法确保了系统在现实世界的复杂应用中始终保持稳健性和响应性。
{"title":"Human autonomy teaming-based safety-aware navigation through bio-inspired and graph-based algorithms","authors":"Timothy Sellers , Tingjun Lei , Chaomin Luo , Zhuming Bi , Gene Eu Jan","doi":"10.1016/j.birob.2024.100189","DOIUrl":"10.1016/j.birob.2024.100189","url":null,"abstract":"<div><div>In the field of autonomous robots, achieving complete precision is challenging, underscoring the need for human intervention, particularly in ensuring safety. Human Autonomy Teaming (HAT) is crucial for promoting safe and efficient human–robot collaboration in dynamic indoor environments. This paper introduces a framework designed to address these precision gaps, enhancing safety and robotic interactions within such settings. Central to our approach is a hybrid graph system that integrates the Generalized Voronoi Diagram (GVD) with spatio-temporal graphs, effectively combining human feedback, environmental factors, and key waypoints. An integral component of this system is the improved Node Selection Algorithm (iNSA), which utilizes the revised Grey Wolf Optimization (rGWO) for better adaptability and performance. Furthermore, an obstacle tracking model is employed to provide predictive data, enhancing the efficiency of the system. Human insights play a critical role, from supplying initial environmental data and determining key waypoints to intervening during unexpected challenges or dynamic environmental changes. Extensive simulation and comparison tests confirm the reliability and effectiveness of our proposed model, highlighting its unique advantages in the domain of HAT. This comprehensive approach ensures that the system remains robust and responsive to the complexities of real-world applications.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"4 4","pages":"Article 100189"},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-18DOI: 10.1016/j.birob.2024.100188
Kaidi Zhu, Tim C. Lueth, Yilun Sun
Tendon-driven continuum robots (TDCR) are widely used in various engineering disciplines due to their exceptional flexibility and dexterity. However, their complex structure often leads to significant manufacturing costs and lengthy prototyping cycles. To cope with this problem, we propose a fused-deposition-modeling-printable (FDM-printable) TDCR structure design using a serial S-shaped backbone, which enables planar bending motion with minimized plastic deformation. A kinematic model for the proposed TDCR structure based on the pseudo-rigid-body model (PRBM) approach is developed. Experimental results have revealed that the proposed kinematic model can effectively predict the bending motion under certain tendon forces. In addition, analyses of mechanical hysteresis and factors influencing bending stiffness are conducted. Finally, A three-finger gripper is fabricated to demonstrate a possible application of the proposed TDCR structure.
{"title":"Design of FDM-printable tendon-driven continuum robots using a serial S-shaped backbone structure","authors":"Kaidi Zhu, Tim C. Lueth, Yilun Sun","doi":"10.1016/j.birob.2024.100188","DOIUrl":"10.1016/j.birob.2024.100188","url":null,"abstract":"<div><div>Tendon-driven continuum robots (TDCR) are widely used in various engineering disciplines due to their exceptional flexibility and dexterity. However, their complex structure often leads to significant manufacturing costs and lengthy prototyping cycles. To cope with this problem, we propose a fused-deposition-modeling-printable (FDM-printable) TDCR structure design using a serial S-shaped backbone, which enables planar bending motion with minimized plastic deformation. A kinematic model for the proposed TDCR structure based on the pseudo-rigid-body model (PRBM) approach is developed. Experimental results have revealed that the proposed kinematic model can effectively predict the bending motion under certain tendon forces. In addition, analyses of mechanical hysteresis and factors influencing bending stiffness are conducted. Finally, A three-finger gripper is fabricated to demonstrate a possible application of the proposed TDCR structure.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"5 1","pages":"Article 100188"},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1016/j.birob.2024.100186
Qiang Li, Shuo Wang, Cong Wang, Jihong Zhu
{"title":"Editorial for the special issue on bio-inspired robotic dexterity intelligence","authors":"Qiang Li, Shuo Wang, Cong Wang, Jihong Zhu","doi":"10.1016/j.birob.2024.100186","DOIUrl":"10.1016/j.birob.2024.100186","url":null,"abstract":"","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"4 4","pages":"Article 100186"},"PeriodicalIF":0.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1016/j.birob.2024.100187
Shilong Sun , Chiyao Li , Zida Zhao , Haodong Huang , Wenfu Xu
This paper investigates the utilization of large language models (LLMs) for the comprehensive control of humanoid robot locomotion. Traditional reinforcement learning (RL) approaches for robot locomotion are resource-intensive and rely heavily on manually designed reward functions. To address these challenges, we propose a method that employs LLMs as the primary designer to handle key aspects of locomotion control, such as trajectory planning, inverse kinematics solving, and reward function design. By using user-provided prompts, LLMs generate and optimize code, reducing the need for manual intervention. Our approach was validated through simulations in Unity, demonstrating that LLMs can achieve human-level performance in humanoid robot control. The results indicate that LLMs can simplify and enhance the development of advanced locomotion control systems for humanoid robots.
{"title":"Leveraging large language models for comprehensive locomotion control in humanoid robots design","authors":"Shilong Sun , Chiyao Li , Zida Zhao , Haodong Huang , Wenfu Xu","doi":"10.1016/j.birob.2024.100187","DOIUrl":"10.1016/j.birob.2024.100187","url":null,"abstract":"<div><div>This paper investigates the utilization of large language models (LLMs) for the comprehensive control of humanoid robot locomotion. Traditional reinforcement learning (RL) approaches for robot locomotion are resource-intensive and rely heavily on manually designed reward functions. To address these challenges, we propose a method that employs LLMs as the primary designer to handle key aspects of locomotion control, such as trajectory planning, inverse kinematics solving, and reward function design. By using user-provided prompts, LLMs generate and optimize code, reducing the need for manual intervention. Our approach was validated through simulations in Unity, demonstrating that LLMs can achieve human-level performance in humanoid robot control. The results indicate that LLMs can simplify and enhance the development of advanced locomotion control systems for humanoid robots.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"4 4","pages":"Article 100187"},"PeriodicalIF":0.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-04DOI: 10.1016/j.birob.2024.100185
Zefan Su , Hanchen Yao , Jianwei Peng , Zhelin Liao , Zengwei Wang , Hui Yu , Houde Dai , Tim C. Lueth
In the dynamic and unstructured environment of human–robot symbiosis, companion robots require natural human–robot interaction and autonomous intelligence through multimodal information fusion to achieve effective collaboration. Nevertheless, the control precision and coordination of the accompanying actions are not satisfactory in practical applications. This is primarily attributed to the difficulties in the motion coordination between the accompanying target and the mobile robot. This paper proposes a companion control strategy based on the Linear Quadratic Regulator (LQR) to enhance the coordination and precision of robot companion tasks. This method enables the robot to adapt to sudden changes in the companion target’s motion. Besides, the robot could smoothly avoid obstacles during the companion process. Firstly, a human–robot companion interaction model based on nonholonomic constraints is developed to determine the relative position and orientation between the robot and the companion target. Then, an LQR-based companion controller incorporating behavioral dynamics is introduced to simultaneously avoid obstacles and track the companion target’s direction and velocity. Finally, various simulations and real-world human–robot companion experiments are conducted to regulate the relative position, orientation, and velocity between the target object and the robot platform. Experimental results demonstrate the superiority of this approach over conventional control algorithms in terms of control distance and directional errors throughout system operation. The proposed LQR-based control strategy ensures coordinated and consistent motion with target persons in social companion scenarios.
{"title":"LQR-based control strategy for improving human–robot companionship and natural obstacle avoidance","authors":"Zefan Su , Hanchen Yao , Jianwei Peng , Zhelin Liao , Zengwei Wang , Hui Yu , Houde Dai , Tim C. Lueth","doi":"10.1016/j.birob.2024.100185","DOIUrl":"10.1016/j.birob.2024.100185","url":null,"abstract":"<div><div>In the dynamic and unstructured environment of human–robot symbiosis, companion robots require natural human–robot interaction and autonomous intelligence through multimodal information fusion to achieve effective collaboration. Nevertheless, the control precision and coordination of the accompanying actions are not satisfactory in practical applications. This is primarily attributed to the difficulties in the motion coordination between the accompanying target and the mobile robot. This paper proposes a companion control strategy based on the Linear Quadratic Regulator (LQR) to enhance the coordination and precision of robot companion tasks. This method enables the robot to adapt to sudden changes in the companion target’s motion. Besides, the robot could smoothly avoid obstacles during the companion process. Firstly, a human–robot companion interaction model based on nonholonomic constraints is developed to determine the relative position and orientation between the robot and the companion target. Then, an LQR-based companion controller incorporating behavioral dynamics is introduced to simultaneously avoid obstacles and track the companion target’s direction and velocity. Finally, various simulations and real-world human–robot companion experiments are conducted to regulate the relative position, orientation, and velocity between the target object and the robot platform. Experimental results demonstrate the superiority of this approach over conventional control algorithms in terms of control distance and directional errors throughout system operation. The proposed LQR-based control strategy ensures coordinated and consistent motion with target persons in social companion scenarios.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"4 4","pages":"Article 100185"},"PeriodicalIF":0.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-17DOI: 10.1016/j.birob.2024.100184
Yanhong Peng , Yuxin Wang , Fangchao Hu , Miao He , Zebing Mao , Xia Huang , Jun Ding
We present a novel approach to predicting the pressure and flow rate of flexible electrohydrodynamic pumps using the Kolmogorov–Arnold Network. Inspired by the Kolmogorov–Arnold representation theorem, KAN replaces fixed activation functions with learnable spline-based activation functions, enabling it to approximate complex nonlinear functions more effectively than traditional models like Multi-Layer Perceptron and Random Forest. We evaluated KAN on a dataset of flexible EHD pump parameters and compared its performance against RF, and MLP models. KAN achieved superior predictive accuracy, with Mean Squared Errors of 12.186 and 0.012 for pressure and flow rate predictions, respectively. The symbolic formulas extracted from KAN provided insights into the nonlinear relationships between input parameters and pump performance. These findings demonstrate that KAN offers exceptional accuracy and interpretability, making it a promising alternative for predictive modeling in electrohydrodynamic pumping.
我们提出了一种利用 Kolmogorov-Arnold 网络预测柔性电动流体动力泵压力和流量的新方法。受科尔莫哥洛夫-阿诺德表示定理的启发，KAN 用可学习的基于样条的激活函数取代了固定的激活函数，使其能够比多层感知器和随机森林等传统模型更有效地逼近复杂的非线性函数。我们在灵活的 EHD 泵参数数据集上对 KAN 进行了评估，并将其性能与 RF 和 MLP 模型进行了比较。KAN 的预测准确度更高，压力和流量预测的平均平方误差分别为 12.186 和 0.012。从 KAN 中提取的符号公式有助于深入了解输入参数与泵性能之间的非线性关系。这些研究结果表明，KAN 具有卓越的准确性和可解释性，是电液动力泵预测建模的理想选择。
{"title":"Predictive modeling of flexible EHD pumps using Kolmogorov–Arnold Networks","authors":"Yanhong Peng , Yuxin Wang , Fangchao Hu , Miao He , Zebing Mao , Xia Huang , Jun Ding","doi":"10.1016/j.birob.2024.100184","DOIUrl":"10.1016/j.birob.2024.100184","url":null,"abstract":"<div><div>We present a novel approach to predicting the pressure and flow rate of flexible electrohydrodynamic pumps using the Kolmogorov–Arnold Network. Inspired by the Kolmogorov–Arnold representation theorem, KAN replaces fixed activation functions with learnable spline-based activation functions, enabling it to approximate complex nonlinear functions more effectively than traditional models like Multi-Layer Perceptron and Random Forest. We evaluated KAN on a dataset of flexible EHD pump parameters and compared its performance against RF, and MLP models. KAN achieved superior predictive accuracy, with Mean Squared Errors of 12.186 and 0.012 for pressure and flow rate predictions, respectively. The symbolic formulas extracted from KAN provided insights into the nonlinear relationships between input parameters and pump performance. These findings demonstrate that KAN offers exceptional accuracy and interpretability, making it a promising alternative for predictive modeling in electrohydrodynamic pumping.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"4 4","pages":"Article 100184"},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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