Lower-limb exoskeleton analysis device that predicts gait state and terrain using CNN analysis of IMU signals

This technology is an AI-based analysis system that preprocesses IMU sensor data from a lower-limb exoskeleton robot into n-channel images. It analyzes gait states using a CNN-based feature network while simultaneously transmitting feature values from intermediate convolutional blocks to a head network to predict the terrain environment (uphill/downhill/flat).

Conventional technologies require separate training for gait state determination and terrain recognition algorithms, which is time-consuming and inefficient. Furthermore, they face limitations in integrated analysis due to the difficulty of securing large-scale data samples.

This technology constructs input data by converting and stacking IMU measurements into 2D channel images and utilizes a multi-output structure based on a common feature network (convolutional blocks) to perform gait state analysis and terrain classification in parallel within a single model. Applicable to rehabilitation training, gait assistance, and medical/welfare services, it integrates gait state and terrain recognition into one model to improve analysis accuracy.

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Key Features:

• A gait analysis device for a lower-limb exoskeleton robot, comprising a head network connected to one of the convolutional blocks, which calculates the terrain environment the user is walking on based on data processed by the connected convolutional block.
• A data receiver that receives measurement data from an IMU (Inertial Measurement Unit) sensor while a user wearing a lower-limb exoskeleton robot equipped with the sensor is walking.
• A data preprocessor that generates input data by converting n features included in the measurement data into an n-channel image.
• A feature network with convolutional blocks for calculating the gait state of the exoskeleton robot from the input data.

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This invention was developed with the support of the Ministry of Science and ICT's project for developing AI/big data-based integrated gait control solutions for personalized gait support and evaluation for lower-limb exoskeleton robots.

Shoulder Rehabilitation Robot

This technology is a link-based rehabilitation exercise assistance device that converts the rotational force of a drive shaft into a linkage and crank-connecting rod structure to implement shoulder flexion/extension and abduction/adduction movements.

Existing rehabilitation devices are often limited to specific movements or fail to account for scapular plane motion, making natural shoulder rehabilitation difficult and complicating setup due to the use of multiple drive units.

This technology transmits power from a single motor through a four-bar linkage and crank-connecting rod mechanism to selectively implement flexion/extension and abduction/adduction exercises centered on the scapular plane. It can be applied to rehabilitation training, gait assistance, and medical/welfare services, allowing for selective shoulder flexion/extension and abduction/adduction exercises based on the shoulder plane, as well as the scapular movement known to be necessary prior to shoulder rehabilitation in clinical settings.

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Key Features:

• A forearm support unit provided on the slider, where the user's forearm is positioned.
• A drive unit that rotates the drive shaft using externally supplied power.
• A slider configured to move linearly along the longitudinal direction of a guide bar, with its rear end rotatably connected to the front end of a connecting rod, moving linearly along the guide bar through the reciprocating motion of the connecting rod.
• A third link bar whose lower front end is rotatably connected to the lower front end of a second link bar, and whose upper rear end is rotatably connected to the rear side of the drive unit, rotating in conjunction with the second link bar.

End-Effector for Robots

This technology is a paddle-type end-effector designed to lift injured persons or objects from the ground. An elastic element mounted at the end of the paddle member physically deforms upon contact with the object; this deformation toggles a switch to detect the insertion state. Additionally, a hinged bracket provides a compliance function to prevent collisions with the object.

Conventional robotic end-effectors are often too thick, making it difficult to insert them between an injured person and the ground. This poses a risk of secondary injury during insertion, while exposed cables lead to durability issues and a lack of reliability in autonomous robotic rescue operations.

This technology features an insertion-sensing unit composed of an elastic element and a switch at the tip of a thin paddle member, which deforms under external force from the object. It also utilizes a compliance structure that connects the paddle to the robot body via a hinged bracket, allowing for angular adjustments that mitigate impact upon contact. Applicable to logistics picking, service robots, and manufacturing automation, it prevents damage and ensures safe insertion of the paddle, thereby enhancing the safety and effectiveness of rescue operations.

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Key Features:

• An end-effector for robots where a switch element turns on when the elastic element is deformed and turns off when the elastic element returns to its original state.
• A sensing unit provided on the paddle member to detect whether the paddle member is fully inserted.
• A paddle member mounted to the robot body, designed to be inserted between the object to be transported and the surface.
• The sensing unit is positioned adjacent to the end opposite to where the paddle member is attached to the robot body, and the sensing unit includes an elastic element that deforms under external force applied by the object being transported.

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This invention was developed with support from the Ministry of Trade, Industry and Energy for the development of core technologies for rescue robot end-effectors.

Robot Gripper for Inspection

This technology is a gripper module mounted on the end of a multi-degree-of-freedom robot manipulator. It integrates an air chuck, Remote Center Compliance (RCC), a force-torque sensor, and a laser sensor to perform a real-time impedance control mechanism based on the contact force between components and inspection jigs.

Conventional position-based control methods for manufacturing robots struggle to handle minor jamming or alignment errors between components and inspection jigs, often leading to a reliance on operator skill and reduced accuracy in defect detection.

This technology utilizes a position-based impedance control algorithm that calculates component movement paths in real time. It forms a system that performs precise insertion and position/orientation correction by controlling the RCC through feedback from force-torque and laser sensors. Applicable to logistics, service robots, and autonomous platforms, it improves the accuracy and speed of inserting components into test jigs, while reducing damage and increasing operational efficiency compared to manual methods.

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Key Features:

• A laser sensor mounted on a laser sensor bracket attached to one side of the main bracket, which senses the movement distance, speed, and insertion depth of the component into the jig.
• A force-torque (F/T) sensor coupled with the RCC via an RCC bracket, which senses the impact applied when the positioning pin is inserted into the jig.
• A robot gripper for inspection featuring a pair of gripper fingers, each equipped with a pad to enhance gripping force.
• An air chuck with its upper section mounted on one side of the bracket, which grips and releases components using a pair of gripper fingers in response to control signals.

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This invention was developed with support from the Ministry of Science and ICT’s AI-based Anti-Drone Active Control Technology Development project.

Grasping Learning System for Irregular Objects Using Offline Reinforcement Learning and Distribution Shift Penalties

This technology utilizes an offline reinforcement learning model to derive grasping poses for irregular objects. It extracts the workspace from offline data collected within the robot's operating environment and applies a penalty to actions with high Q-values that are not present in the valid offline dataset, thereby preventing excessive Q-value predictions outside the offline data distribution and optimizing the grasping success rate.

Real-time online reinforcement learning methods carry a high risk of robot damage during data collection, are time-consuming and costly, and are difficult to implement in real-world field applications due to environmental constraints.

This technology employs an offline reinforcement learning-based model to perform training without real-time interaction. By inferring pixel-wise Q-values and applying a penalty to actions with the maximum Q-value that are not included in the existing dataset (valid offline data), the model is prevented from selecting incorrect optimal actions outside the training data distribution, allowing for the precise derivation of 6-DOF grasping poses. It can be applied to logistics picking, service robots, and manufacturing automation, optimizing grasping success rates without the risk of robot damage during data collection.

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Key Features:

• Offline reinforcement learning model training step, where the training unit of the learning server trains an offline reinforcement learning model using valid offline data and infers pixel-wise Q-values
• Penalty application step, where the action with the highest Q-value among those not present in the valid offline data is selected as a representative action, and a penalty is applied only to that representative action
• Maximum Q-value action determination step, where it is verified whether an action is the one with the highest Q-value among those determined to be excluded from the valid offline data
• Valid offline data extraction step, where valid offline data corresponding to the workspace is extracted

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This invention was developed with the support of the Artificial Intelligence Convergence Innovation Talent Cultivation program by the Ministry of Science and ICT.

6-DOF Object Grasping Pose Learning System Combining Deep Reinforcement Learning and Gram-Schmidt Orthogonalization

This technology converts spatial information acquired through an input device into point cloud data and applies a deep reinforcement learning model to infer the Q-value (success rate) and rotation vector for each point. Based on the inferred values, it uses Gram-Schmidt orthonormalization to determine the 6-DOF grasping pose.

Conventional supervised learning-based grasping techniques require sophisticated dynamic models, limiting their use for grasping unknown objects without CAD data. Furthermore, the presence of multiple objects often leads to occlusion, which reduces grasping success rates.

This technology uses a deep reinforcement learning model to infer grasping positions and angles directly from point clouds without the need for label generation. In the event of a learning failure, it calibrates the reward function model through inverse reinforcement learning and optimizes the 6-DOF grasping pose using the Gram-Schmidt orthogonalization technique. Applicable to logistics picking, service robots, and manufacturing automation, it significantly increases the grasping success rate for unknown objects even without CAD data.

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Key Features:

• Deep reinforcement learning model training phase, which learns from point cloud data and infers the Q-value and rotation vector for each point.
• Work area spatial information extraction phase, where the data generation unit extracts the portion corresponding to the robot device's workspace from the first spatial information to create second spatial information.
• Point cloud data generation phase, which includes a spatial information reception phase where the learning server's data generation unit receives the first spatial information.
• Spatial information acquisition phase, which acquires the first spatial information containing the target object for grasping through an input device.

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This invention was developed with the support of the Artificial Intelligence Convergence Innovation Talent Cultivation program by the Ministry of Science and ICT.

Method for Determining the Position and Angle of a Microrobot

This technology is an image processing method that calculates the 3D position and angle of a microrobot by performing thresholding and noise removal on top-view and side-view images of the microrobot collected within a simulation environment, followed by setting a Region of Interest (ROI) and applying edge detection and line detection algorithms.

Challenges include reduced mobility efficiency due to the miniaturization of microrobots, increased control difficulty for real-world human applications, and a lack of precise state recognition technology for pre-testing and simulation.

This technology utilizes camera images to identify the initial position of the microrobot, performs ROI setting and length-based noise filtering, and then extracts the slope and length of line segments through edge and line detection to ultimately derive the microrobot's rotation angles (Yaw, Roll, Pitch) and position. It can be applied to industrial robots and automation systems, improving the control of medical microrobots by providing a method to clearly recognize their position and angle within a simulation environment.

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Key Features:

• Includes the step of determining the position and angle of the microrobot in 3D space from an image, where the step of determining the position and angle includes identifying the initial position of the microrobot from the image.
• A step of removing noise that is longer or shorter than the length of the microrobot, based on the length of the microrobot displayed in the image, from the noise generated during the process of isolating the Region of Interest (ROI) from the image.
• A step of performing edge detection at the final position displayed in the image to identify points of discontinuity based on image brightness.
• A step of setting the initial position in the image as a Region of Interest (ROI) for the microrobot.

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This invention was developed with support from the Ministry of Trade, Industry and Energy for the development of a microrobotic system for the treatment of chronic total occlusion in myocardial infarction.

Electro-adhesive Pneumatic/Hydraulic Gripper

This technology is a soft robotic gripper mechanism that combines a flexible gripping unit, which expands and contracts via a pneumatic/hydraulic chamber, with an electro-adhesive film that utilizes electrostatic attraction. This increases static friction with objects, allowing for the secure gripping of irregularly shaped items.

Conventional motor-driven robotic hands are limited in their ability to grip irregular objects of various shapes and materials due to their rigid construction, while pneumatic/hydraulic soft robotic grippers often suffer from weak gripping force due to the nature of their flexible materials.

This technology places an electro-adhesive film containing a first electrode in the contact area of the flexible gripping unit to generate electrostatic adhesion. By covering the electrode with a thin film that has lower elongation and higher stiffness than the gripper body, it maintains flexibility while locally enhancing gripping force. Applicable to logistics picking, service robots, and manufacturing automation, it improves both clamping force and durability compared to traditional pneumatic grippers.

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Key Features:

• First and second gripping units connected to the main body and driven by the internal pneumatic/hydraulic chamber
• The first gripping unit includes a first electro-adhesive film with a first electrode in the area that contacts the object
• The second gripping unit includes a second electro-adhesive film with a second electrode in the area that contacts the object
• Each electro-adhesive film features a structure where the electrode is encased in a thin film with a lower elongation rate than the gripping unit itself

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This invention was developed with support from the Ministry of Trade, Industry and Energy for the development of shape-adaptive electro-adhesive grippers capable of picking irregular multi-objects.

Linkage for Equilibrium Manipulators

This technology features a rod-shaped linkage made of elastic material (rubber or polypropylene) with annular hinge grooves that form joints. It enables 6-DOF position and orientation control through the elastic deformation of the material, eliminating the need for separate joint assemblies.

Conventional manipulator linkages consist of multiple joint assemblies (translational, universal, spherical), which lead to positional errors due to friction and vibration at each joint, as well as structural complexity that hinders precise miniaturization.

By molding the linkage itself from elastic materials like rubber or polypropylene and carving semi-circular hinge grooves at specific locations to induce bending and twisting, this technology eliminates physical friction and enables an ultra-precise, ultra-compact structure. It can be applied to industrial robots and automation systems, improving the precision and control of manipulators by reducing friction and structural limitations.

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Key Features:

• Rod-shaped elastic linkage connecting the end effector to the actuator
• One or more joints formed to rotatably support the linkage around its central axis
• Joints provide rotational support in response to actuator movement to change the position and orientation of the end effector
• Structure where the linkage bends and flexes via semi-circular hinge grooves embedded in the joints

Curling Robot

This technology utilizes a three-wheeled robot structure, featuring a pair of front wheels and a single rear wheel, to perform precise turns and changes in direction by calculating the steering angle of the rear wheel based on the kinematic geometry between the front and rear wheels.

It overcomes the limitations of conventional methods that rely on external guide lines or environmental data, enabling active and independent path control using the robot's own kinematic dimensions.

This technology establishes a kinematic triangular model that accounts for the distance between the center of the front wheels and the rear wheel, as well as the distance between the front wheels. By determining the rear wheel steering angle through mathematical formulas, it controls turning maneuvers by fixing the body's center of rotation to one of the front wheels. Applicable to industrial robots and automated systems, it improves the precision and efficiency of a curling robot's movement on ice by controlling its travel direction without external guidance.

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Key Features:

• A curling robot for throwing a stone, comprising a body
• A controller that determines the rotation angle of the body, using one of the pair of front wheels as the center of rotation, based on the rotation angle of the rear wheel relative to a virtual line connecting the center of the pair of front wheels and the rear wheel
• A curling robot that rotates the body by the determined rotation angle around the center of rotation while the body is in a stationary state.
• A pair of front wheels installed at the front of the body

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This invention was developed with support from the Ministry of Science and ICT for the development of AI curling robot technology capable of establishing game strategies and performing in matches.

Tracking Device

This technology is a mechanism for a mobile tracking device equipped with a positioning module containing multiple positioning nodes, an imaging module, and a sensor module. The device uses a controller to measure the distance to a target node, set a path, and sense obstacles and moving objects to perform path correction, stopping, and re-tracking. In particular, the positioning module calculates the target position through a node configuration of isosceles right triangles and squares, and uses a verification and correction algorithm based on combinations of three nodes.

Existing GPS-based indoor positioning suffers from low accuracy and signal distortion, as well as the inconvenience of having to pre-install positioning nodes throughout the entire area. Furthermore, there is a lack of safety due to the inability to respond to fixed obstacles or sudden moving objects encountered during real-time movement.

This technology utilizes four positioning nodes installed inside the mobile unit to dynamically calculate the distance to a target node. Based on data input from the imaging module and sensor module, the controller controls the real-time path (avoidance, stopping, and restarting). Specifically, it adopts a computational structure that combines three out of the multiple nodes to measure distance, while using the remaining nodes to verify and correct the data. It can be applied to rehabilitation training, gait assistance, and medical/welfare services, improving the accuracy of positioning the target object and enhancing environmental management capabilities by modifying the path based on obstacle detection.

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Key Features:

• A tracking device that verifies and corrects the measured distance at least once using three positioning nodes, including the remaining positioning nodes.
• A controller that receives data from the positioning module, imaging module, and sensor module to set the path and control the operation of the drive module.
• A sensor module that senses the front of the main body to detect moving objects between the target object and the main body.
• A tracking device for following a moving target object, comprising a drive module that moves the main body.

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This invention was developed with the support of the Ministry of Science and ICT's Real-time Indoor Wide-area Positioning Technology Development project.

Ultra-Precision Compact Parallel Robot Manipulator

This technology features a parallel robot manipulator structure that performs multi-degree-of-freedom position and orientation control. It consists of a frame with top and bottom plates and guide pins, housing piezoelectric motor-based actuators and slide plates that transmit translational motion to an end-effector via linkage units.

Conventional manipulators are difficult to miniaturize due to complex structures where actuators move along axes, and their structural complexity and large volume limit their commercial application in areas such as ultra-precision tasks or medical use.

This technology adopts a parallel kinematic structure where the rotational motion of a screw driven by an actuator (piezoelectric motor) fixed to the bottom plate is converted into translational motion via ball bearings to move the slide plate vertically. The linkage unit connected to the slide plate then drives the end-effector, resulting in a more compact device with improved precision. Applicable to industrial robots and automation systems, it enhances the miniaturization, precision, portability, and control capabilities of parallel robot manipulators.

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Key Features:

• Frame unit forming the exterior with top plate, bottom plate, and guide pins
• Multiple independently operated drive units coupled to the frame
• End-effector provided inside the frame unit, with a device mounted on its base
• Linkage units connecting the end-effector to the multiple drive units to control position and orientation

Multi-jointed Robot

This technology features multiple multi-jointed legs coupled to a main body, with friction pads at the base of each leg featuring grooves that provide anisotropic friction. Each leg consists of alternating joint units with a first joint axis and a second joint axis perpendicular to it. By aligning the grooves of the friction pads parallel to the connection direction, the robot mimics snake-like movement and enables multi-jointed walking functionality.

Wheeled robots are limited by road surface conditions, while conventional legged robots often struggle with mobility or maneuverability on rough terrain and suffer from reduced efficiency when utilizing multi-jointed structures.

This technology utilizes a hyper-redundant leg structure that mimics the biological movement of a snake, combined with anisotropic friction pads that induce varying levels of friction based on the direction of ground contact. By forming longitudinal grooves in the friction pads, the design minimizes interference during walking motions and enables terrain-adaptive maneuvering. Applicable to industrial robots and automation systems, this technology enhances robot mobility and adaptability in challenging terrains and environments.

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Key Features:

• A leg unit connected to a main body, comprising a first joint unit with a first joint axis and a second joint unit with a second joint axis perpendicular to the first joint axis.
• A leg unit comprising a first joint unit with a first joint axis and a second joint unit with a second joint axis perpendicular to the first joint axis.
• A friction pad unit that provides anisotropic friction to the leg unit.
• A multi-jointed robot where the first direction is parallel to the connection direction in which the first joint unit and the second joint unit are connected.

Collision Detection Sensor

This technology is a collision detection system that utilizes multiple capacitive sensors with varying measurement areas placed on a robot's surface to simultaneously perform wide-range proximity detection and precise position/gesture recognition.

Existing collaborative robots use high-output actuators, which pose a risk of collision accidents when working in close proximity to human operators. Consequently, there is a need for reliable and precise non-contact proximity sensing solutions to prevent such incidents.

This technology features a hybrid array of capacitive sensor groups with different measurement areas (first and second measurement areas) on the robot's link surface, with additional third-area sensors placed at the joints. By leveraging the differences in sensing range and resolution proportional to these areas, the system detects collisions and recognizes user gestures to trigger control actions. Applicable to robotic gripping, precision measurement, and automated equipment, it enhances the safety and reliability of collaborative robots by enabling rapid collision detection and gesture recognition for improved human-robot interaction.

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Key Features:

• A collision detection sensor for a robot, comprising a plurality of first capacitive measurement units arranged at regular intervals, and a plurality of second capacitive measurement units, with two or more of the second units disposed between the first units.
• A plurality of second capacitive measurement units disposed on the robot's surface, spaced apart from the first capacitive measurement units, and having a second measurement area different from the first measurement area of the first capacitive measurement units.
• A plurality of second capacitive measurement units having a second measurement area different from the first measurement area of the first capacitive measurement units.
• A plurality of first capacitive measurement units disposed on the surface of the robot.

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This invention was developed with support from the Ministry of Trade, Industry and Energy for the development of functional safety implementation technology based on international standards for robots operating in human-contact environments, as well as risk assessment and mitigation technology.

Gripper System

This technology is a gripper system mechanism that physically controls the coefficient of friction between the gripping surface and an object by forming a plurality of micro-channels on the gripping surface and supplying liquid through a fluid pump.

Conventional gripper systems rely solely on normal force control, which can cause deformation when gripping flexible objects. Furthermore, because the coefficient of friction is fixed as a constant value, there are limitations in precisely gripping objects of various materials and shapes.

This technology actively controls the contact area and frictional force by discharging liquid through a plurality of micro-channels formed on the gripping part, and adjusts the supply volume of the fluid pump and the gripping force of the drive unit in real time based on the object's state measured by force sensors. Applicable to logistics picking, service robots, and manufacturing automation, it improves precision control for gripping various materials and objects of different shapes by controlling friction through fluid and deformation control of the gripping part.

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Key Features:

• A gripper system for gripping an object, comprising a pair of gripping parts having gripping surfaces with a plurality of micro-channels formed therein, which come into contact with the object through the gripping surfaces.
• A control unit that controls a drive unit to have the pair of gripping parts grip an object, or controls a fluid pump to adjust the amount of liquid supplied to the gripping surfaces.
• A drive unit that operates the pair of gripping parts.
• A fluid pump connected to the plurality of micro-channels to supply liquid.