Abstract

In recent years, interest in autonomous driving vehicles has grown, both for industrial use and for public and personal transportation. As is well known, although LiDAR sensors and cameras are required to detect obstacles and pedestrians; neural networks and AI to interpret the environment and make decisions; and/or GPS and real-time maps for precise navigation; etc., these vehicles require wheels for movement. This work presents an electromechanical differential that allows controlling the accelerated differential movement required when an autonomous vehicle turns sideways. The name electromechanical differential refers to an arrangement of three accelerators: one to control the vehicle’s acceleration; this accelerator is connected in series with the accelerator that independently controls each drive wheel. Control of the differential acceleration of the rear wheel hub motors, in the case of four-wheel vehicles, is provided by the vehicle’s front-wheel steering system mechanism, considering the Ackerman differential geometry, allowing for minimizing the number of complex automatic control systems, such as sensors and encoders. The front steering pivot, controlled by the steering wheel to indicate the vehicle’s turn, has a square-shaped structure, where the pivot rotates on a hub fixed to the chassis, while the ends of the pivot terminate one in the wheel and the other in the steering rod. This turning motion is associated with the butterfly mechanism of an electric pedal accelerator, which opens and closes with the rotation of the wheel.These ideas can be extrapolated to an electromechanical differential for two- or four-legged robots, either with pivoting on one foot or with differential movement)
Biography

Dr. Abisai Reséndiz work in Dep. Mechanical Engineering, TecNM- Querétaro México, has completed his PhD from Chile University and postdoctoral studies from UNICAMP Brasil.

Abstract

Human-machine interfaces (HMIs) have not only become popular technologies but have become the hope of many individuals for restoring their lost limb function. Any HMI has two important intrinsic design components—(i) decode the human commands and (ii) controlling the machine to convert that command into action. Decades of research went into making the interface between the human and the machine seamless but were unable to effectively address the inherent challenges, namely, complexity, adaptability and variability. To overcome the above challenges, it is critical to computationally understand and quantitatively characterize the human sensorimotor control. Emerging areas in HMIs critically depend on the ability to build bioinspired models, experimentally validate them and utilize them in adaptive and intuitive control. The human hand with high dimensionality encompasses the three inherent challenges and may serve as an ideal validation paradigm. How central nervous system (CNS) controls this high dimensional human hand effortlessly is still an unsolved mystery. To address this high dimensional control problem, many bioinspired motor control models have been proposed, one of which is based on synergies. According to this model, instead of controlling individual motor units, CNS simplifies the control using coordinated control of groups of motor units called synergies. However, there are several unanswered questions today— Where are synergies present in CNS? What is their role in motor control and motor learning? By combining the concepts of human motor control, computational neuroscience, machine learning and validation with noninvasive human experiments, can we answer these fundamental questions? The goal of this research is to develop efficient, seamless and near-natural human-machine interfaces based on biomimetically inspired models.

Biography

Ramana Vinjamuri is an Associate Professor in Computer Science and Electrical Engineering at UMBC. He also serves as the Director of a neurotech center named BRAIN, an NSF IUCRC. He serves as a visiting scientist at NIDA of NIH. He recently has chaired IEEE Brain workshop of SfN 2023 and chaired BMES 2024. His notable research awards are from NSF CAREER, NSF I-corps, NIDILRR, USISTEF and New Jersey Health Foundation. He holds Adjunct Faculty positions in IIT-Hyderabad and MAHE in India. His primary research interests are in brain-computer interfaces, robotics, human-robot interaction, AI/ML, and neurotech for mental health.

Precision agriculture has revolutionized farm management by integrating automation and geospatial technologies to enhance efficiency and sustainability. One of the most impactful innovations in this field is the combination of remote sensing, high-precision positioning, and autonomous guidance systems, enabling accurate field planning and execution with minimal environmental impact. This study explores the application of these technologies in agricultural field design. A Matrice 300 RTK drone equipped with an LiDAR L1 sensor was used to model a 1.5-ha plot with slopes ranging from 1% to 10%, generating a digital elevation model with a 5 cm resolution. A GNSS CHNAV i50 receiver was then used to validate the obtained metrics and ensure model accuracy. Based on the elevation model, natural runoff lines were identified, and keyline infiltration ditches were designed to optimize water capture and distribution in the soil. These lines were replotted and marked in the field using an autonomous guidance system, specifically a Sveaverken system mounted on a Case Farmall 75 tractor, achieving an average deviation of ±3 cm from the designed lines and curves. The autonomous guidance system not only improved marking accuracy but also reduced operational time, fuel consumption, and reliance on human operators, making the process more efficient, scalable, and cost-effective. The use of LiDAR enabled a detailed representation of the terrain, facilitating precise planning and reducing uncertainty in the design of water management structures. GNSS validation ensured model accuracy and allowed for corrections before field execution, enhancing overall reliability and effectiveness in large-scale applications. The results demonstrate that the integration of LiDAR, GNSS, and autonomous guidance significantly improves accuracy in planning and executing water management strategies in agricultural fields. These advancements represent a key step toward the full automation of agricultural tasks and the adoption of robotic technologies in precision agriculture.

Biography

Kerin Romero Calvo is an Associate Professor School of Agricultural Engineering, Technological Institute of Costa Rica Rica. Advanced Student Master’s Degree in Agricultural Mechanization, National University of La Plata Argentina. His teaching areas are Machinery and Agricultural Mechanization and Research areas are Agricultural Mechanization and Digital Agriculture. He has published 4 papers. His work as a researcher has resulted in 4 scientific articles published in renowned journals in the scientific community. In addition, he works as an independent consultant in field surveys and design.

Abstract

Recent advancements in skin-integrated vibrotactile haptic devices are extending the sense of touch beyond the fingertips to the entire body in virtual environments. However, achieving realistic tactile interactions remains challenging due to the complexity of the somatosensory system and the limited understanding of skin-actuator interactions. This study experimentally investigates a wide range of commercially available vibrotactile actuators, including eccentric rotating mass (ERM), linear resonant actuators (LRA), tactors, and piezoelectric actuators, in direct contact with both human skin and skin-like materials. To analyze these interactions, non-intrusive optical techniques such as 3D Digital Image Correlation (DIC), Particle Tracking Velocimetry (PTV), and Eulerian Video Magnification (EVM) are employed. These methods enable the assessment of key continuum mechanical properties relevant to mechanoreception, including strain and acceleration at varying depths within a skin phantom. Additionally, critical parameters such as contact area, power input, frequency response, and viscoelastic properties are examined, while customized perception tests help establish correlations between actuator performance and tactile perception. The insights gained from this study serve as reference data for model validation and contribute to the development of next-generation haptic technologies.
Biography
Dr. Jin-Tae “Jimmy” Kim is an assistant professor in the Mechanical Engineering department at Pohang University of Science and Technology. He obtained his B.S. degree in Mechanical Engineering, with minor in mathematics, from Oklahoma State University in 2013 and his MS and PhD degrees in Theoretical and Applied Mechanics from the University of Illinois at Urban-Champaign in 2015 and 2020, respectively. He worked as a postdoctoral researcher in the Querrey Simpson Institute for Bioelectronics at Northwestern University until 2023. His areas of research are experimental/theoretical fluid mechanics with photogrammetric approaches and soft electronics. He has published 47 papers in peer-reviewed journals including 3 Nature, 1 Nature Electronics, 3 Nature Communications, 3 Science Advances, 5 PNAS, 6 Journal of Fluid Mechanics, 5 Physical Review Fluids, 2 Physics of Fluid, among others. He received the Hassan Aref memorial award for Theoretical and Applied Mechanics in 2020.