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A Passive Recursive Newton Euler Algorithm and Its Application to Inverse Dynamics under Passivity-based Controller

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A Passive Recursive Newton Euler Algorithm and Its Applicati...

IEEE International Conference on Simulation, Modeling, and programming for Autonomous Robots (SIMPAR)

作者： Jonghyeok Kim Youngjin Choi Jonghoon Park Wan Kyun Chung Department of Mechanical Engineering POSTECH Pohang South Korea Department of Robotics Hanyang University Ansan South Korea Neuromeka Seoul South Korea

ISBN: (数字)9798331516857

ISBN: (纸本)9798331516864

This paper proposes a passivity-based Recursive Newton-Euler algorithm, abbreviated pRNE. The proposed pRNE algorithm solves the inverse dynamics under a passivity-based controller, which requires calculating the product term of the Coriolis matrix $C(q,\dot q)$ and the reference velocity ${\dot q^{{\text{ref}}}}$, that is, $C(q,\dot q){\dot q^{{\text{ref}}}}$. Two major parts are modified compared to the standard RNE algorithm: 1) matrix-vector factorization in single rigid body dynamics satisfying the skew-symmetric property, and 2) propagation for reference velocity and acceleration in recursive computation. Through simulation, the proposed pRNE algorithm combined with the passivity-based inverse dynamic controller (P-IDC) validates an accurate tracking performance under model uncertainty and disturbance compared to the standard inverse dynamic controller (IDC).

关键词： Uncertainty Accuracy Trajectory tracking Heuristic algorithms Computational modeling Lie groups programming Computational efficiency Standards Autonomous robots

A Unified Framework for Robots that Influence Humans over Long-Term Interaction

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arXiv 2025年

作者： Sagheb, Shahabedin Parekh, Sagar Pandya, Ravi Mun, Ye-Ji Driggs-Campbell, Katherine Bajcsy, Andrea Losey, Dylan P. Virginia Tech Department of Mechanical Engineering United States Carnegie Melon University Robotics Institute United States University of Illinois Urbana-Champaign Department of Electrical and Computer Engineering United States

Robot actions influence the decisions of nearby humans. Here influence refers to intentional change: robots influence humans when they shift the human’s behavior in a way that helps the robot complete its task. Imagine an autonomous car trying to merge;by proactively nudging into the human’s lane, the robot causes human drivers to yield and provide space. Influence is often necessary for seamless interaction. However, if influence is left unregulated and uncontrolled, robots will negatively impact the humans around them (e.g., autonomous cars that repeatedly merge in front of humans may cause human drivers to become more aggressive). Prior works have begun to address this problem by creating a variety of control algorithms that seek to influence humans. Although these methods are effective in the short-term, they fail to maintain influence over time as the human adapts to the robot’s behaviors. In this paper we therefore present an optimization framework that enables robots to purposely regulate their influence over humans across both short-term and long-term interactions. Here the robot maintains its influence by reasoning over a dynamic human model which captures how the robot’s current choices will impact the human’s future behavior. Our resulting framework serves to unify current approaches: we demonstrate that state-of-the-art methods are simplifications of our underlying formalism. Our framework also provides a principled way to generate influential policies: in the best case the robot exactly solves our framework to find optimal, influential behavior. But when solving this optimization problem becomes impractical, designers can introduce their own simplifications to reach tractable approximations. We experimentally compare our unified framework to state-of-the-art baselines and ablations, and demonstrate across simulations and user studies that this framework is able to successfully influence humans over repeated interactions. See videos of our experiments he

关键词： Human robot interaction

Muscle Activation Estimation by Optimizing the Musculoskeletal Model for Personalized Strength and Conditioning Training

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arXiv 2025年

作者： Wu, Xi Li, Chenzui Zou, Kehan Xi, Ning Chen, Fei Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Department of Industrial and Manufacturing System Engineering The University of Hong Kong Hong Kong

Musculoskeletal models are pivotal in the domains of rehabilitation and resistance training to analyze muscle conditions. However, individual variability in musculoskeletal parameters and the immeasurability of some internal biomechanical variables pose significant obstacles to accurate personalized modelling. Furthermore, muscle activation estimation can be challenging due to the inherent redundancy of the musculoskeletal system, where multiple muscles drive a single joint. This study develops a whole-body musculoskeletal model for strength and conditioning training and calibrates relevant muscle parameters with an electromyography-based optimization method. By utilizing the personalized musculoskeletal model, muscle activation can be subsequently estimated to analyze the performance of exercises. Bench press and deadlift are chosen for experimental verification to affirm the efficacy of this approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Electromyography

Design and Performance Comparison of Low Dropout Regulators for a 32-bit Microcontroller Using 22nm Technology

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Design and Performance Comparison of Low Dropout Regulators ...

IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Emmanuel K. M. Johnson Luis Victor N. Lima Pamela. R. A. N. Campagnucci Sandro A. P. Haddad Wellington A. Amaral Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program University of Brasilia Brasilia DF Brazil Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

This paper presents a comparison between two LDOs (Low Dropout Regulators) developed for a PMIC (Power Management Integrated Circuit) responsible for powering a 32-bit microcontroller based on the RISC-V microarchitecture, designed in 22 nm technology. The microcontroller will be integrated with a radio frequency interface for IoT (Internet of Things) applications. Two LDOs were designed: one with external compensation and the other a capacitor-less LDO. Both operate with an input voltage of 2.5V, an output voltage of 0.8V, and a maximum current of 150mA. The externally compensated LDO exhibited greater stability in response to load variations and a higher PSRR (Power Supply Rejection Ratio), while the capacitor-less LDO demonstrated faster regulation speed and occupied a chip area similar to the externally compensated LDO.

关键词： Regulators Microcontrollers Power supplies Power system management Simulation Stability analysis Regulation Circuit stability Internet of Things Voltage control

Efficient Swept Volume-Based Trajectory Generation for Arbitrary-Shaped Ground Robot Navigation

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arXiv 2025年

作者： Li, Yisheng Yin, Longji Cai, Yixi Liu, Jianheng Li, Haotian Zhang, Fu Department of Mechanical Engineering University of Hong Kong Hong Kong Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden

Navigating an arbitrary-shaped ground robot safely in cluttered environments remains a challenging problem. The existing trajectory planners that account for the robot’s physical geometry severely suffer from the intractable runtime. To achieve both computational efficiency and Continuous Collision Avoidance (CCA) of arbitrary-shaped ground robot planning, we proposed a novel coarse-to-fine navigation framework that significantly accelerates planning. In the first stage, a sampling-based method selectively generates distinct topological paths that guarantee a minimum inflated margin. In the second stage, a geometry-aware front-end strategy is designed to discretize these topologies into full-state robot motion sequences while concurrently partitioning the paths into SE(2) sub-problems and simpler R2 sub-problems for back-end optimization. In the final stage, an SVSDF-based optimizer generates trajectories tailored to these sub-problems and seamlessly splices them into a continuous final motion plan. Extensive benchmark comparisons show that the proposed method is one to several orders of magnitude faster than the cutting-edge methods in runtime while maintaining a high planning success rate and ensuring CCA. Copyright © 2025, The Authors. All rights reserved.

关键词： Computational geometry

Anomalous properties of spark plasma sintered boron nitride solids

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Materials Today 2025年 86卷 42-51页

作者： Biswas, Abhijit Serles, Peter Alvarez, Gustavo A. Schimpf, Jesse Hache, Michel Kong, Jonathan Demingos, Pedro Guerra Yuan, Bo Pieshkov, Tymofii S. Li, Chenxi Puthirath, Anand B. Gao, Bin Gray, Tia Zhang, Xiang Murukeshan, Jishnu Vajtai, Robert Dai, Pengcheng Singh, Chandra Veer Howe, Jane Zou, Yu Martin, Lane W. Clancy, James Patrick Tian, Zhiting Filleter, Tobin Ajayan, Pulickel M. Department of Materials Science and Nanoengineering Rice University HoustonTX77005 United States Department of Mechanical & Industrial Engineering University of Toronto 5 King's College Road TorontoM5S 3G8 Canada Sibley School of Mechanical and Aerospace Engineering Cornell University IthacaNY14853 United States Department of Materials Science and Engineering University of California Berkeley94720 United States Department of Materials Science and Engineering University of Toronto 184 College St TorontoM5S 3E4 Canada Department of Physics and Astronomy McMaster University HamiltonL8S 4M1 Canada Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley94720 United States

Hexagonal boron nitride (h-BN) is a brittle ceramic with a layered structure, however, recent experiments have suggested that inter-layer structural engineering could be key to new structural and functional properties. Here we report the scalable bulk synthesis of high-density crystalline h-BN solids, by using high-temperature spark plasma sintering (SPS) of h-BN powders, which show high values of mechanical strength, ductility, dielectric constant, thermal conductivity, and exceptional neutron radiation shielding capability. Through exhaustive characterizations we reveal that SPS induces non-basal plane crystallinity, twisting of layers, and facilitates inter-grain fusion with a high degree of in-plane alignment across macroscale dimensions, resulting in near-theoretical density and improved properties. Our findings highlight the importance of material design, via new approaches such as layer twisting and interlayer interconnections, to create novel ceramics with properties that could go beyond their intrinsic limits. © 2025

关键词： Ductility

On the Benefits of Torque Vectoring for Automated Collision Avoidance at the Limits of Handling

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IEEE Transactions on Vehicular Technology 2025年

作者： Bertipaglia, Alberto Tavernini, Davide Montanaro, Umberto Alirezaei, Mohsen Happee, Riender Sorniotti, Aldo Shyrokau, Barys Department of Cognitive Robotics Delft University of Technology Delft2628 CD Netherlands Centre for Automotive Engineering University of Surrey GuildfordGU2 7XH United Kingdom Department of Mechanical Engineering University of Eindhoven Eindhoven5612 AZ Netherlands Departement of Mechanical and Aerospace Engineering Politecnico di Torino Torino10129 Italy

This paper presents a novel approach integrating motion replanning, path tracking and vehicle stability for collision avoidance using nonlinear Model Predictive Contouring Control. Employing torque vectoring capabilities, the proposed controller is able to stabilise the vehicle in evasive manoeuvres at the limit of handling. A nonlinear double-track vehicle model, together with an extended Fiala tyre model, is used to capture the nonlinear coupled longitudinal and lateral dynamics. The optimised control inputs are the steering angle and the four longitudinal wheel forces to minimise the tracking error in safe situations and maximise the vehicle-to-obstacle distance in emergency manoeuvres. These optimised longitudinal forces generate an additional direct yaw moment, enhancing the vehicle’s lateral agility and aiding in obstacle avoidance and stability maintenance. The longitudinal tyre forces are constrained using the tyre friction cycle. The proposed controller has been tested on rapid prototyping hardware to prove real-time capability. In a high-fidelity simulation environment validated with experimental data, our proposed approach successfully avoids obstacles and maintains vehicle stability. It outperforms two baseline controllers: one without torque vectoring and another one without collision avoidance prioritisation. Furthermore, we demonstrate the robustness of the proposed approach to vehicle parameter variations, road friction, perception, and localisation errors. The influence of each variation is statistically assessed to evaluate its impact on the performance, providing guidelines for future controller design. © 1967-2012 IEEE.

关键词： Friction

Development of a Multi-Fingered Soft Gripper Digital Twin for Machine Learning-based Underactuated Control

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arXiv 2025年

作者： Yang, Wu-Te Lin, Pei-Chun University of California Berkeley United States Department of Mechanical Engineering National Taiwan University Bio-inspired Robotics Lab Taipei Taiwan

Soft robots, made from compliant materials, exhibit complex dynamics due to their flexibility and high degrees of freedom. Controlling soft robots presents significant challenges, particularly underactuation, where the number of inputs is fewer than the degrees of freedom. This research aims to develop a digital twin for multi-fingered soft grippers to advance the development of underactuation algorithms. The digital twin is designed to capture key effects observed in soft robots, such as nonlinearity, hysteresis, uncertainty, and time-varying phenomena, ensuring it closely replicates the behavior of a real-world soft gripper. Uncertainty is simulated using the Monte Carlo method. With the digital twin, a Q-learning algorithm is preliminarily applied to identify the optimal motion speed that minimizes uncertainty caused by the soft robots. Underactuated motions are successfully simulated within this environment. This digital twin paves the way for advanced machine learning algorithm training. © 2025, CC BY-NC-ND.

关键词： Degrees of freedom (mechanics)

Decentralized Navigation of a Cable-Towed Load using Quadrupedal Robot Team via MARL

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arXiv 2025年

作者： Chen, Wen-Tse Nguyen, Minh Li, Zhongyu Sue, Guo Ning Sreenath, Koushil Robotics Institute Carnegie Mellon University Pittsburgh United States Department of Mechanical Engineering University of California Berkeley Berkeley United States

This work addresses the challenge of enabling a team of quadrupedal robots to collaboratively tow a cable-connected load through cluttered and unstructured environments while avoiding obstacles. Leveraging cables allows the multi-robot system to navigate narrow spaces by maintaining slack when necessary. However, this introduces hybrid physical interactions due to alternating taut and slack states, with computational complexity that scales exponentially as the number of agents increases. To tackle these challenges, we developed a scalable and decentralized system capable of dynamically coordinating a variable number of quadrupedal robots while managing the hybrid physical interactions inherent in the load-towing task. At the core of this system is a novel multi-agent reinforcement learning (MARL)-based planner, designed for decentralized coordination. The MARL-based planner is trained using a centralized training with decentralized execution (CTDE) framework, enabling each robot to make decisions autonomously using only local (ego) observations. To accelerate learning and ensure effective collaboration across varying team sizes, we introduce a tailored training curriculum for MARL. Experimental results highlight the flexibility and scalability of the framework, demonstrating successful deployment with one to four robots in real-world scenarios and up to twelve robots in simulation. The decentralized planner maintains consistent inference times, regardless of the team size. Additionally, the proposed system demonstrates robustness to environment perturbations and adaptability to varying load weights. This work represents a step forward in achieving flexible and efficient multi-legged robotic collaboration in complex and real-world environments. © 2025, CC BY.

关键词： Multipurpose robots