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.
본 기술은 EMG 센서의 근전도 데이터와 DVS 카메라의 시각적 움직임 정보를 입력받아 적응형 필터와 델타-시그마 변조를 통해 스파이크 신호로 변환하고, 이를 멀티 스파이킹 뉴럴 네트워크에 입력하여 실시간으로 손과 팔의 동작을 분류하고 재현하는 뉴로모픽 제어 기술입니다.
종래의 근전도 및 가속도 센서 기반 제어 방식은 손과 팔의 동작을 정밀하게 모방하는 데 한계가 있었고, 실시간 반응성이 낮으며 로봇 제어 시 소비 전력이 높아 의료용 로봇 시스템의 성능 발전에 제약이 있었습니다.
본 기술은 근전도 데이터를 스파이크 신호로 변환하고 DVS 카메라 데이터를 크로핑과 다운-샘플링을 거쳐 SNN 모델에 병렬 입력함으로써 연산 효율을 높이고 저전력으로 고속의 정밀한 동작 모방 제어를 수행합니다. 의수와 재활 로봇, 원격 조작 매니퓰레이터에 적용될 수 있어 배터리 부담을 낮추면서 사용자의 의도를 즉각 반영하는 새로운 가능성을 제시합니다.
본 발명은 과학기술정보통신부의 인간의 신경계를 모사한 뉴로 칩 설계 기술 및 뉴로 컴퓨팅 플랫폼 연구개발 지원을 통해 개발되었습니다.
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.
This technology involves a biohybrid robot and a method for manufacturing muscle bundles. The robot’s drive unit consists of muscle bundles cultured with gold nanoparticles immobilized with hyaluronic acid, while its propulsion unit is composed of a plate-shaped support and an oar.
Conventional industrial robots, due to their rigid structures, have limited adaptability to their surroundings. Soft robots, intended as alternatives, have struggled to achieve the driving force and cellular bioactivity levels comparable to living muscle tissue.
By incorporating gold-hyaluronic acid nanoparticles, this technology enhances the electrical conductivity and cellular bioactivity of muscle bundles and proposes a method for connecting multiple mobile units. It can be utilized for drug screening for muscle disease prevention and treatment, as well as for the development of bio-actuators, significantly advancing the driving force and practicality of tissue-based robots.
This invention was developed with support from the Ministry of Science and ICT’s project for brain-assembloid-based biomimetic sensing biohybrid robots, the Ministry of Science and ICT’s project for organoid-based nanobiohybrid actuator chips for drug screening, and the Ministry of Education’s project for developing nanobiochips for brain disease drug evaluation.
This technology is a compliant control system that operates a target robot in parallel with a corresponding virtual model. It identifies differences between the two state signals as operational errors and limits the compensation range based on the magnitude of disturbances and the robot's sliding speed, thereby suppressing excessive driving torque.
Conventional industrial robots often misinterpret external collisions or disturbances during operation as simple tracking errors, leading the control system to apply excessive torque, which can result in equipment damage or safety accidents.
This technology utilizes an operational error observer to calculate the state difference between the physical robot and the virtual model, and a compensation output unit to separately output virtual and actual compensation signals, enabling the robot to adapt to external forces. It can be applied to precision assembly and human-robot collaboration environments, ensuring safety for both the robot and the operator during collisions without the need for additional force sensors.
This invention was developed with support from the Ministry of Trade, Industry and Energy for the development of a universal multi-mode robot teaching device for high-difficulty assembly tasks requiring 0.1mm precision in position, velocity, and contact force teaching.
This technology features a soft finger unit and gripper that selectively implements shape-adaptive grasping and vacuum suction grasping through a single pneumatic control. It utilizes a soft body made of stretchable material with internal pneumatic channels and an opening/closing module that operates under positive and negative pressure.
Existing soft grippers suffer from low payload capacity and difficulty in grasping specific shapes, such as thin sheets. This has historically necessitated the inefficient addition of separate suction-type grippers to overcome these limitations.
This technology introduces a check-valve-based opening/closing module at the tip of the soft body. It performs shape-adaptive grasping by expanding the bending chamber under positive pressure and enables suction grasping by opening the module to deliver vacuum pressure under negative pressure. This allows a single gripper to perform both grasping methods. It significantly improves facility efficiency by handling various object shapes in fields such as food packaging, logistics picking, and electronic component handling without the need for gripper changes.
This invention was developed with support from the Ministry of Trade, Industry and Energy for recognition technology and grippers capable of multi-product random piece picking.
This technology is an underactuated robot gripper that utilizes a single motor and a magnetic-based non-contact power transmission mechanism. It drives multiple fingers with a single actuator and performs adaptive grasping tailored to an object's shape through a complex kinematic structure incorporating worm gears and magnetic gears.
Conventional robot hands have been difficult to apply to service robots due to complex control requirements and high costs, while simple industrial grippers have limitations in flexibly grasping objects of various shapes.
This technology proposes a method that transmits motor power to the output shaft of each finger via a set of magnetic and worm gears, utilizing torsion springs and multi-stage link structures to allow finger joints to bend according to the object's shape upon contact. This enables adaptive grasping with only a single actuator. It can be applied to service robots, logistics picking, and daily assistance robots, significantly reducing the production cost of robot hands while maintaining high grasping performance.
This invention was developed with support from the Ministry of Trade, Industry and Energy for recognition technology and grippers capable of high-mix random piece picking.
This technology is a tactile sensing system that uses thermoelectric elements and temperature sensors attached to a robot gripper's fingertips to acquire time-series data on thermal conductivity changes during object contact, which is then processed by a 1D-CNN deep learning model to identify and classify objects.
Existing robot recognition systems based on pressure or force sensors often struggle with limited classification accuracy, as they fail to provide sufficient information regarding the unique physical properties of an object's texture or material.
This technology proposes a method where the thermoelectric element heats the fingertip above room temperature before contact; the temperature sensor then measures the temperature changes caused by the object's thermal conductivity, and the deep learning model classifies the data. This allows for precise object recognition that incorporates material properties. It can be applied to logistics sorting, recycling, and service robot object handling, providing a new means of perception that can distinguish objects that are difficult to identify using visual information alone.
This invention was developed with support from the Ministry of Science and ICT for the development of electro-hydraulic actuator-based soft robot modules.
This technology is an elbow rehabilitation device that measures the stiffness of a patient's elbow joint and performs rehabilitation training by controlling the speed of the drive motor based on torque values.
Conventional rehabilitation robots struggle with precise operational control based on a patient's stiffness and lack adequate response to sudden spasms, posing a risk of injury to the patient.
This technology uses a torque sensor to measure the load applied to the elbow and its instantaneous changes in real-time, while a rehabilitation control unit variably adjusts the motor's rotation angle and speed according to the patient's condition. Applicable to rehabilitation training, gait assistance, and medical/welfare services, it provides a robot capable of adjusting treatment based on the patient's condition and mobility, thereby improving the effectiveness of rehabilitation therapy for stroke patients with elbow stiffness.
This invention was developed with support from the Ministry of Science, ICT and Future Planning for brain mapping-based robot rehabilitation.
This technology corrects sensor and control errors that occur during autonomous robot localization and mapping. It generates a noise-minimized map by inputting real-time robot-view maps and global maps into a style transfer learning model, which is then used to calibrate the robot's position.
Existing autonomous robots often suffer from degraded localization performance in real-world operation due to discrepancies between simulated and actual environments, as well as errors in odometry sensors and motor control.
This technology utilizes an operation control program to generate robot-view and global maps. By applying a style transfer learning model between ground-truth image sets and real-world image sets, it produces transformed map data to calibrate the navigation agent's position estimates, enabling precise localization and mapping in real-world environments. Since it bridges the gap between simulation and reality using only a learning model—without the need for additional sensors—it significantly reduces development costs and trial-and-error during the commercialization of logistics and service robots.
This invention was developed with support from the Ministry of Science and ICT for learning to establish mid-to-long-term task plans for service robots through hierarchical understanding of 3D information.
