This technology relates to a hip-joint elastic suit and its operating method for physical movement assistance, specifically a non-powered wearable suit that uses elastic elements to assist with hip flexion and extension.
Older adults with reduced muscle mass often experience slower walking speeds and decreased stability, leading to a higher risk of falls. However, existing powered assistive devices are heavy and expensive, making them difficult to wear for daily use.
By connecting the main belt to the thigh-worn components using elastic elements and adjusting the assistive force via length-adjustment members, this technology provides a lightweight and convenient way to improve walking speed and stability.
This invention was developed with support from the Ministry of Science and ICT for "Machine Learning and Extended Reality for High-Speed Mutual Adaptation between Users and Wearable Robots," and the Ministry of Trade, Industry and Energy for the "Development of Human-Augmented Hybrid Robot Suits Capable of Safe 100m Sprints in 7 Seconds and Comfortable 12-Hour Wear."
This technology is a method and device that collects muscle activation data for various gait environments using surface electromyography (sEMG) sensors attached to multiple lower limb muscles, such as the rectus femoris, vastus medialis, and tibialis anterior, and uses this data as input for an artificial neural network to estimate and classify the user's gait environment.
Surface electromyography signals are difficult to classify accurately due to their complex patterns and non-linear characteristics, making it challenging to detect transitions in gait environments early enough to control assistive robots effectively.
This technology proposes a method for estimating gait environments by utilizing electromyography profiles from 11 lower limb muscle sites as inputs for an artificial neural network. It can be applied to exoskeleton gait assistive robots and rehabilitation equipment, enabling rapid recognition of changes in the user's gait environment to provide natural and safe assistance.
This invention was developed through the Ministry of Science and ICT's project on early detection algorithms for gait environment transitions based on biosignals using deep learning techniques and support for the unaffected side.
This technology corrects accumulated localization errors along a robot's path using loop closure. It works by having an underwater robot perform an initial 3D scan of an object at a starting position, traverse multiple locations, and then return to the starting position to perform a second scan of the same object.
In underwater environments, localization errors accumulate as the robot moves, which degrades the consistency of 3D scan data. Previously, it was difficult to ensure accurate localization without relying on expensive, high-precision sensors.
This technology proposes a method to correct both yaw sensor errors and path-based localization data by comparing the first and second scans of the same object. It is applicable to underwater tunnel inspections and marine structure surveys, providing an economical solution for obtaining precise 3D data without the need for expensive navigation equipment.
This invention was developed with support from the Smart Underwater Tunnel System Research Center, funded by the Ministry of Science and ICT.
This technology is a joint positioning device that maintains the position of a multi-joint system by redirecting a unidirectional force from a spring balancer via a tension wire to deliver gravity compensation to linear and rotary joints.
In multi-joint robots, installing individual gravity compensation devices for each joint increases the number of components, adds to the overall mass and volume of the system, and leads to structural complexity and inefficiency.
This technology proposes a method where force applied from a single source is transmitted via a tension wire to the connection points of the linear guide rolling unit and the first and second rotary joints. By winding the wire multiple times around the connection and auxiliary connection parts, the required force is efficiently amplified and transmitted. Applicable to industrial robot arms and medical stands, this system supports the entire multi-joint structure with a single compensation device, achieving both weight reduction and structural simplification.
This invention was developed with support from the Ministry of Trade, Industry and Energy for the development of human-centered smart dual-arm transfer assistance robots.
This technology is a robot system and learning data generation method that determines collisions in real-time by generating training data from the variance between control target values and actual measured values of robot joints during non-collision states, and predicting dynamic normal operating ranges using an AI learning model.
Conventional torque sensor-based collision detection involves high hardware costs, motor current-based methods are prone to false positives due to friction, and existing AI approaches often suffer from reduced robot durability during the collection of actual collision data.
This technology proposes a method that calculates time-series maximum and minimum measured values from normal, non-collision operation data using sliding window and moving average techniques, utilizing them as training data to predict dynamic collision ranges. This enables accurate collision detection without the need for additional sensors. It can be applied to safety certification for collaborative robots and industrial manipulators, replacing expensive torque sensors while ensuring both safety and cost-efficiency.
This invention was developed with support from the Ministry of Trade, Industry and Energy for the development of deep reinforcement learning-based collaborative task technology capable of intelligently responding to unstructured work environments, such as assembly tasks.
This technology is an upper-body assistive device that utilizes a multi-joint link structure and elastic elements positioned between frames secured to the user's upper and lower body. It passively provides traction to reduce disc pressure and generates a moment to assist in straightening the back through mechanical elastic deformation that occurs when the user bends forward.
Existing actuator-based active assistive devices face issues with battery and power supply, and can hinder movement or cause unintended muscle activation due to the application of excessive force.
This technology proposes a design where the upper frame, lower frame, and first and second connecting frames are sequentially connected to rotate, providing mechanical restorative force based on the user's movement through elastic elements, all without the need for an external power source. It is lightweight, maintenance-free, and easy to implement in fields involving repetitive lumbar strain, such as logistics, construction, and elderly care, effectively reducing the risk of injury.
This technology is a knee joint guide device that features a planar rigid 6-bar linkage structure—consisting of three revolute joints, three prismatic joints, and four links—positioned between proximal and distal body links to provide a self-aligning function that accommodates the shifting instantaneous center of rotation during knee flexion.
Because it is difficult to replicate the unique instantaneous center of rotation path of an individual's knee joint using only simple revolute joints, conventional devices often cause physical discomfort. Furthermore, existing multi-degree-of-freedom mechanisms designed to address this issue are structurally complex, leading to reduced efficiency in force and speed transmission.
This technology proposes a 1-degree-of-freedom closed-loop system using a planar 6-bar linkage structure that combines three revolute joints and three prismatic joints. The structure, including the actuator, optimizes its mechanical position according to the knee flexion angle, allowing it to adapt to various body types without complex customization while efficiently delivering the torque required for walking. It is applicable to various fields, including rehabilitation, strength assistance, and industrial wearable robots, enhancing both walking convenience and quality of life while reducing physical strain on the wearer.
This technology maps FMCW radar target detection data into a 2D angle-velocity domain and calculates a robot's movement speed and rotation angle without external sensors by analyzing data correlations between consecutive scans and applying linear regression to trend lines, enabling simultaneous localization and mapping (SLAM) through ego-motion estimation.
Conventional SLAM systems require additional hardware such as motor encoders or gyro sensors to estimate a robot's ego-motion, which increases system complexity and cost. Furthermore, laser and camera-based sensors often suffer from performance degradation in low-light or adverse weather conditions.
This technology proposes a method that converts the relative velocity and angle information of targets acquired from radar sensors into a binary matrix, derives the rotation angle through forward and backward cross-correlation operations, and calculates movement speed from the velocity-axis intercept by performing linear regression on the trend line of detection points in the angle-velocity domain. This allows for robust localization in adverse conditions without the need for additional sensors. Because it functions even in environments where cameras and LiDAR are ineffective—such as smoke, dust, or low-light conditions—it significantly enhances the reliability of disaster response robots and industrial autonomous equipment.
This invention was developed with the support of the Ministry of Science and ICT's "Research on Next-Generation Radar Systems Robust to Interference: Deep Learning-Based Data Augmentation Core Technology and Adaptive Target Tracking Technology."
This technology is a marine life monitoring system that uses a structure combining an upper frame equipped with a camera and an open lower frame. It can be attached to underwater drones or installed in a fixed position to collect ecological data in a non-invasive manner.
Conventional ecological survey methods using divers or underwater robots often disturb the target species and face challenges in maintaining stable, long-term monitoring from a fixed location.
This technology proposes a design that houses an underwater camera and battery within an aluminum or STS316 pressure-resistant enclosure, mounted inside a fixed frame with an open structure that allows marine life to move in and out freely. This enables long-term monitoring without disturbing the organisms, while simultaneously performing underwater noise measurement, sonar detection, and water quality analysis. It is applicable to aquaculture management, marine ecological surveys, and fishery resource monitoring, providing a practical solution for research and industrial sites that require long-term data collection without disturbing marine life.
This invention was developed with support from the Ministry of Oceans and Fisheries for the development of science and technology-based marine area utilization impact assessment.
This technology features a mechanical design that allows the gripper to grip and rotate objects independently, without requiring additional robot arm joints. It utilizes a dual-motor mechanism: a first motor drives a gripping shaft to move links linearly for grasping, while a second motor drives a rotation shaft to rotate the gripped object.
Conventional robots require the use of robot arm joints to rotate an object after gripping it, which limits the workspace, complicates the robot arm structure, and increases costs.
This technology arranges the gripping drive (first motor/shaft) and the rotation drive (second motor/shaft) side-by-side, using a ball spline and link structure to convert the motor's rotational motion into linear gripping movement and full-body rotation. By employing a gear transmission system, it minimizes slippage and enhances control precision. Applicable to manufacturing automation, assembly processes, and service robots, it enables object rotation without the need for robot arm joints, thereby reducing workspace requirements and structural complexity.
This invention was developed with support from the Ministry of Trade, Industry and Energy for the development of robot task control technology capable of gripping, manipulating, and using various objects in daily living environments based on multimodal perception.
This technology is a system that autonomously serves and collects in-flight meals by integrating a robotic arm and support structure onto a meal cart. It includes GPU-based autonomous driving and stop control, voice recognition-based meal selection, meal tray transport using primary and secondary rails, and a precision delivery process via a robotic arm gripper.
In-flight meal service is currently performed manually by flight attendants, which poses risks of injury and health issues due to the handling of heavy carts, creating a continuous demand for automated serving systems.
This technology implements autonomous meal service through a serving robot equipped with a robotic arm (including a gripper) and a rail-based tray transport structure. It automates the process using object recognition technology linked to voice and seat databases, along with verification and error-checking algorithms utilizing the robotic arm's camera and object recognition sensors. Applicable to in-flight services, food and beverage serving robots, and meal distribution automation, it reduces the physical burden and accident risks for flight attendants while increasing service efficiency.
This technology distinguishes between intentional and unintentional human movements through frequency analysis of multi-degree-of-freedom force signals and dynamically adjusts admittance model parameters, such as inertia and damping, based on the resulting collaborative state.
Fixed admittance gains create a trade-off between sensitive control and safety, while existing DFT-based observers suffer from slow real-time response speeds, making immediate adjustments difficult.
By using low-pass and high-pass filters for frequency analysis, this technology reduces computational load to increase response speed and enables real-time variable control of parameters based on the collaborative state. It can be applied to collaborative robots and physical human-robot interaction systems, providing control performance that ensures both sensitivity and safety tailored to the work environment.
This technology utilizes a fuzzy Q-learning algorithm to adaptively adjust the admittance parameters of wearable robots. It monitors human-robot collaboration status in real-time using low-pass and high-pass filters, using this data along with interaction forces and load weight as inputs for a fuzzy control model to calculate optimal control values.
Setting fixed admittance gains created a trade-off between sensitivity and safety, while existing DFT-based frequency analysis methods struggled with observation delays, making it difficult to recognize collaboration status or respond to impacts within 0.5 seconds.
This technology achieves low-latency collaboration recognition using a second-order IIR Butterworth filter and optimizes parameters in real-time by applying fuzzy Q-learning to expert knowledge-based fuzzy rule initial values. It serves as an innovative solution for power-assist suits and logistics wearable robots by automatically balancing sensitivity and safety based on the situation.
This technology relates to an actuation system and operating method capable of implementing various profiles for two cables using a single actuator.
Previously, each cable required a separate motor, which increased system weight and power consumption, and made it difficult to implement overlapping drive profiles for the two cables.
This technology uses a moving gear and pulley units to distribute and switch the output of a single motor between two cables, simplifying control while reducing both the number of actuators and battery consumption.
This invention was developed with support from the Zero-Power Physical Augmentation Basic Research Laboratory of the Ministry of Science and ICT, and the Korea Forest Service's project for developing deep learning-integrated smart wearable suits to assist muscle strength, prevent injuries, and improve work efficiency for forestry workers.
This technology relates to a pulley-integrated cable actuator and its operating method, featuring a drive module optimized for cable-driven systems by combining a ring gear-integrated pulley with a planetary gear set.
In conventional wearable robots, the cable drive unit consists of a motor, a reducer, and a pulley arranged separately, resulting in a bulky, protruding structure. This has been a primary factor in reducing comfort and mobility.
This technology integrates a ring gear into the pulley and incorporates a planetary gear set to reduce the protrusion of the drive unit and optimize the design volume. It enhances the usability and comfort of wearable robots through a more compact and efficient cable drive system.
This invention was developed through the "Development of Deep Learning-Integrated Smart Wearable Suits for Muscle Assistance, Injury Prevention, and Work Efficiency Improvement for Forestry Workers" project by the Korea Forest Service, and the "Machine Learning and Extended Reality Support for High-Speed Mutual Adaptation between Users and Wearable Robots" project by the Ministry of Science and ICT.
