This technology relates to a biomimetic lightweight wearable suit and its design method, featuring an assistive suit that optimizes force transmission paths by mimicking human anatomical structures and physical properties.
Conventional exoskeleton devices are often heavy and bulky, making them uncomfortable for daily use, while their rigid frames restrict joint movement and reduce overall comfort.
By aligning force transmission patterns and anchor points with human muscle and tendon structures, this technology achieves a lightweight, flexible design that enhances walking and mobility efficiency. It is applicable to various assistive devices, including ankle exoskeletons.
This invention was developed with support from the Ministry of Trade, Industry and Energy for the development of a human-augmentation hybrid robot suit capable of a 100m sprint in 7 seconds and 12 hours of comfortable wear; the Ministry of Agriculture, Food and Rural Affairs for the development of a deep-learning-integrated smart wearable suit to assist forest workers with muscle strength, injury prevention, and work efficiency; and for the development of soft wearable robot suits to assist the gait of the elderly and Parkinson's patients.
This technology is a system that calculates the current electrical phase difference between rotors in a coaxial magnetic gear drive module equipped with a motor and multiple rotors, and controls the motor to a target torque by adjusting the rotation angle of one of the rotors to converge on a target phase difference.
Conventional reduction mechanisms suffer from wear due to mechanical contact, leading to high maintenance costs, while existing non-contact power transmission mechanisms have limitations in terms of torque control performance and stability.
This technology proposes a method that linearizes the non-linearity of magnetic gears by combining a disturbance observer with a non-contact power transmission structure that utilizes the magnet arrangement of inner and outer rotors. It can be applied to small robots and collaborative robot drive systems, enabling precise torque control while reducing maintenance costs through wear-free, sealed power transmission.
This invention was developed with support from the Ministry of Science and ICT for research on the design and control of non-contact active small continuous variable transmission mechanism modules for ideal robot operation.
본 기술은 모바일 로봇 하단의 전·후·좌·우 4면에 매립 배치된 광시야각 라이다 센서로부터 깊이 영상을 획득하고, 센서별 캘리브레이션 파라미터를 이용해 단일 World 좌표계로 정합하여 사각지대 없는 통합 3D 포인트 클라우드를 생성하는 감지 시스템입니다.
기존 로봇 상단 탑재형 라이다는 고가이면서 부피가 크고, 센서 사각지대로 인해 로봇 하부와 구동부 근처의 장애물을 감지하지 못해 별도의 보조 센서가 필수적이라는 비효율이 있었습니다.
본 기술은 4면에 매립된 라이다로 수평 전방향 서라운드 뷰와 수직 30도 이상의 화각을 확보하고 회전변환 행렬과 원점 좌표를 이용해 개별 센서 데이터를 병합하는 방식을 제안합니다. 라스트 마일 배송 로봇과 실내 서비스 로봇에 적용될 수 있어 보조 센서 없이 음영 지역을 제거하여 주행 안전성과 원가 경쟁력을 동시에 확보합니다.
본 발명은 과학기술정보통신부의 광시야 고해상도 라이다 기반 라스트 마일 자율주행 로봇 플랫폼 지원을 통해 개발되었습니다.
This technology is a reinforcement learning-based gait control method that enables robots to quickly resume adaptive walking when hardware failures, such as leg damage, occur. It achieves this by distilling knowledge from an agent trained in a normal state and utilizing it as a refined joint trajectory space through an encoder-decoder neural network.
Existing gait control technologies can adapt to terrain or environmental changes, but they face inefficiencies when hardware failures occur, often leading to a loss of control or requiring the agent to be retrained from scratch.
This technology proposes a method that uses a conditional variational autoencoder to set the joint trajectory space as the action space, narrowing the search space during failures based on knowledge learned in a normal state. It generates anchor points and paths based on conditional vectors to derive optimal joint trajectories in real time. This ensures robust autonomy, allowing robots to autonomously reconfigure their gait even when legs are damaged, making it ideal for environments where mission interruption is critical, such as disaster site exploration, defense, and industrial patrolling.
This invention was developed with support from the Artificial Intelligence Graduate School Program (Korea University) funded by the Ministry of Science and ICT.
This technology is an AR-based robot arm control system that detects objects in real-time from video captured via an AR device and calculates their 3D coordinates using ray casting and mesh generation to precisely control the target position of a robot arm.
Conventional controller or eye-tracking methods are difficult for individuals with physical disabilities, such as quadriplegia, to operate directly. Furthermore, these methods present inconveniences and collision risks, as users must simultaneously monitor the screen and the robot arm's position.
This technology proposes a method where an AR device recognizes the user's gaze to select a specific object, calculates the relative distance and 3D coordinates between the object and the robot arm, and enables the robot arm to automatically move and perform grasping tasks, ensuring intuitive and safe operation. It can be utilized for rehabilitation assistance, support for daily living for people with disabilities, and remote operations, significantly improving the independence and quality of life for users with physical limitations.
This invention was developed with support from the Ministry of Science and ICT for the "Development of Customized Brain-Robot Interface for the Physically Disabled with Improved Accuracy, Speed, and Convenience" and the "Development of Non-invasive BCI Integrated Brain-Cognitive Computing SW Platform Technology for Controlling Real-life Appliances and AR/VR Devices via Thought" (BCI-General/Sub-project 1) projects.
This technology is an AI-based approximate path planning device and method that identifies substitute objects or approximate spaces when a target object is not detected by utilizing distances in an embedding vector space, and re-plans the robot's destination and movement path accordingly.
Previously, if a target object commanded by a user was not detected in the surrounding environment, it was impossible to set an endpoint, causing the robot's path planning to be interrupted and the movement task to fail.
This technology proposes a method that uses an embedding algorithm to extract a substitute object with an embedding value closest to the target object, or identifies an approximate space where the target object is highly likely to exist, and generates a path to that point. This allows tasks to continue without interruption even when recognition fails. It is applied to home service robots and indoor delivery robots to ensure autonomy that flexibly responds to the uncertainties of real-world environments.
This invention was developed with support from the Artificial Intelligence Graduate School Program (Korea University) funded by the Ministry of Science and ICT.
This technology is a multifunctional soft robot mechanism that combines four pneumatically driven modules with two snap-through joint sections, allowing the robot to change and maintain its geometric shape using only a single pneumatic control.
Conventional pneumatic network soft robots require continuous air supply to maintain their shape and demand complex inputs, which increases the overall volume and weight of the device.
This technology introduces bistable shell-structured snap joints that use snap-through and snap-back behaviors triggered by critical pressure to lock the robot's physical shape. This enables the implementation of a soft robot capable of dynamic mode switching, such as aligning the four drive modules in a line or deploying them horizontally depending on the control mode. Because it can switch between various movement modes with a single pneumatic input, it offers exceptional competitiveness in environments requiring multifunctionality with limited resources, such as exploration and disaster response robotics.
This invention was developed with support from the Metamorphic Mechanical Systems Research Group of the Ministry of Science and ICT.
This technology is a variable stiffness joint that drives mechanical structures by utilizing pneumatic snap-through buckling. It features a snap joint module positioned between two pneumatically pressurized members to deform the shape of a soft robot.
Conventional pneumatic network-based soft robots require complex, separate input controls for every movement and continuous pressure supply to maintain their deformed state.
This technology proposes a design that combines an elastic shell capable of pneumatic snap-through behavior with a tendon structure, enabling shape deformation and state retention with a single input control. This allows the deformed shape to be stably maintained without continuous pressure supply. It significantly reduces the burden on pressure supply systems in applications requiring lightweight and low-power operation, such as medical assistive devices, wearable devices, and grippers, thereby expanding the practical range of soft robots.
This invention was developed with support from the Metamorphic Mechanical System Research Center of the Ministry of Science and ICT.
This technology is a quadrotor-based tilt-rotor aircraft. It features a mechanical mechanism that connects rotor shafts at the front and rear of the body to a single servo motor using a belt-pulley or gear transmission structure, allowing for synchronized tilting of all rotors, along with a 5-degree-of-freedom control method utilizing this mechanism.
Conventional multi-rotors are limited to 4 degrees of freedom because their thrust direction is fixed relative to the airframe. This makes translational movement impossible without tilting the entire aircraft and restricts stable hovering while in a tilted state.
This technology uses a mechanical tilting system with a single servo motor to tilt the rotation axes of all rotors simultaneously, enabling thrust direction control independent of the airframe's attitude. It proposes a controller that calculates optimal control inputs based on a dynamic model decomposed into underactuated and fully actuated subsystems, allowing for 5-degree-of-freedom flight with minimal actuators. It is highly applicable to missions where tilting the airframe is not feasible, such as precision photography, facility inspection, and close-proximity flight in confined spaces, meeting the demand for high-performance aircraft with minimal hardware.
This invention was developed with support from the Ministry of Science and ICT's development of image-based detection and avoidance technology, and the Ministry of Education's development of tilt-rotor control techniques based on coupling/uncoupling mechanisms for autonomous cooperative transport.
This technology analyzes gesture information by measuring electrical signal changes (resistance and capacitance) resulting from joint bending in the finger and palm areas of a glove woven with conductive fibers.
Conventional data gloves require complex manufacturing processes and incur high cutting and sewing costs due to the integration of wires, electronic sensors, and circuits.
This technology integrates the sensor area by weaving conductive fibers into the same layer as non-conductive fibers, configured to detect changes in finger joint contact points and palm capacitance. Applicable to rehabilitation training, wearable interfaces, and remote robot operation, it enhances gesture detection performance with a simple structure that eliminates the need for embedded circuits.
This invention was developed with support from the Ministry of Science and ICT for the development of biomimetic bionic arm mechanisms.
This technology is a robot control mechanism that autonomously navigates indoor spaces to detect sound information. When it determines an alarm situation requires the user's attention, it sends information to the user's device or, if the user does not respond, moves directly to the user to provide an alert through physical contact.
The hearing impaired often face risks or daily inconveniences due to an inability to perceive indoor sound information (such as fire alarms, doorbells, or household appliance sounds) in a timely manner, while existing attachable devices are cumbersome to install and often have blind spots.
This technology features a wheeled body equipped with sound, location, and object detection sensors, utilizing AI algorithms to analyze the user's location and sound data. Upon detecting an alarm, it sends a notification to the user's device; if unconfirmed, the robot navigates to the user's location and induces physical contact (tactile stimulation) by repeatedly moving forward and backward to ensure the alarm is perceived. Applicable to home service robots, indoor safety, and accessibility support, it enhances daily safety by delivering sound information to the hearing impaired immediately.
This technology is a modular actuator featuring a worm and worm gear reduction system. By positioning the motor and motor driver both inside and on the exterior of the case and separating them from the rotating assembly, it ensures ease of assembly and flexibility in capacity scaling. The module allows for the construction of multi-jointed robot arms by interconnecting multiple units via integrated coupling interfaces and connectors.
Previously, designing individual robots required custom manufacturing of components and frames, leading to high production costs. Furthermore, a lack of modularity in robot drive units made maintenance and performance upgrades difficult.
This technology adopts a structure where the motor is fixed to the exterior side of the case, driving the worm gear of the rotating assembly via a worm shaft. It enables the direct mechanical and electrical connection of multiple drive modules using coupling interfaces and pin connectors. Applicable to collaborative robots, logistics manipulators, and educational robot platforms, it allows for the configuration of various robot arm specifications simply by combining modules, significantly reducing development time and costs.
This invention was developed with the support of the Ministry of Science and ICT for the development of a modular manipulator based on spherical parallel complex joints for item delivery and collection.
This technology is a neuromorphic control system that receives EMG data from sensors and visual motion information from DVS cameras, converts them into spike signals via adaptive filtering and delta-sigma modulation, and inputs them into a multi-spiking neural network to classify and replicate hand and arm movements in real time.
Conventional control methods based on EMG and acceleration sensors have limitations in precisely mimicking hand and arm movements, suffer from low real-time responsiveness, and consume high power, which hinders the performance advancement of medical robotic systems.
By converting EMG data into spike signals and processing DVS camera data through cropping and down-sampling for parallel input into an SNN model, this technology enhances computational efficiency and enables high-speed, precise motion imitation with low power consumption. It can be applied to prosthetic limbs, rehabilitation robots, and remote-controlled manipulators, offering new possibilities for reducing battery load while instantly reflecting user intent.
This invention was developed with support from the Ministry of Science and ICT for research and development on neuro-chip design technology and neuro-computing platforms that mimic the human nervous system.
This technology relates to a gait state prediction system and method using domain adaptation techniques and flexible time windows, enabling highly accurate estimation of gait state variables despite individual differences in walking patterns.
Existing gait state prediction models suffer from a sharp decline in accuracy when users or environments change, and their fixed time windows limit their ability to adapt flexibly to variations in walking speed.
By integrating domain adaptation algorithms with flexible time window techniques, this technology ensures robust predictive performance against individual differences and speed variations. It can be applied to gait rehabilitation, wearable robot control, and healthcare monitoring.
This invention was developed with support from 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; the Ministry of Science and ICT’s Zero-Power Human Augmentation Basic Research Laboratory; and the development of deep learning-based tactile/texture analysis and tactile-feedback augmented prosthetic hands using flexible artificial neural patches.
This technology relates to a liquid motion detection system for delivery robots and a liquid delivery robot equipped with said system. More specifically, it involves technology that analyzes the sloshing of liquid on a tray in real-time using a camera to control the robot's driving speed and tray angle.
Service robots transporting food and beverages in restaurants frequently face issues with liquid spilling due to sudden speed changes or uneven floor surfaces. This leads to reduced service quality, the need for rework, and potential safety hazards.
By actively controlling the tray angle and driving speed through a liquid state analysis unit and a reinforcement learning model, this technology ensures stable delivery without liquid spills. It can be applied to various fields, including serving robots, unmanned cafes, and hospital transport robots.
This invention was developed with support from the Ministry of Science and ICT’s Zero-Power Body Enhancement Basic Research Laboratory and the development of deep learning-based tactile/texture interpretation and tactile-enhanced prosthetic hands using flexible artificial neural patches.
