Multi-degree-of-freedom intelligent transfer device for patient transfer and cooperative control method

Abstract

The invention discloses a multi-degree-of-freedom intelligent transfer device for patient transfer and a cooperative control method, and belongs to the technical field of medical instruments and rehabilitation robots. The device comprises a movable chassis, a lifting stand column, a multi-degree-of-freedom mechanical arm system, a multi-mode sensing system and a controller. The mechanical arm system comprises at least five six-degree-of-freedom mechanical arms, and bionic holding supporting plates are arranged at the tail ends of the six-degree-of-freedom mechanical arms. The multi-mode sensing system comprises a surface electromyographic electrode used for collecting electromyographic signals of a patient, a visual unit, a force sense unit and the like. The method comprises the steps of manual pre-fixing, initial recognition, self-adaptive holding, collaborative transfer, intelligent placement and physiological signal closed-loop feedback throughout the whole process, muscle tension and pain response of a patient are sensed in real time through electromyographic signals, and the motion strategy of the mechanical arm is dynamically adjusted accordingly. Meanwhile, the posture of the patient is kept absolutely stable in the moving process through a motion compensation algorithm, safe, comfortable and automatic patient transferring is achieved, and the nursing burden and risk are greatly reduced.

Description

Technical Field

[0001] This invention relates to the field of medical devices and rehabilitation robots, specifically to a multi-degree-of-freedom intelligent transfer device and its collaborative control method for assisting patients with limited mobility or localized pain to safely and comfortably transfer between beds and facilities such as wheelchairs, commodes, and treatment tables. Background Technology

[0002] In the fields of medical care and home care, transferring patients with limited mobility, postoperative recovery, localized pain, or motor dysfunction between beds, between wheelchairs, and between beds and other facilities is a frequent and critical nursing procedure. Currently, this procedure mainly relies on nurses, caregivers, or family members to manually lift and move the patient.

[0003] The existing technology has the following significant drawbacks:

[0004] Human resource dependence and safety risks: The lifting process relies entirely on human labor, which is physically demanding for nursing staff. In case of insufficient strength or coordination errors, it is very easy to cause secondary injuries such as patients falling or nursing staff suffering from lumbar muscle strain.

[0005] Lack of state perception: Caregivers cannot directly and quantitatively perceive the patient's physiological state during movement, especially the immediate response to pain. The force, angle, and speed of movement are estimated based on experience alone, making it difficult to individualize the approach.

[0006] High communication costs and poor results: To avoid patient discomfort, the lifting process relies on careful cooperation through verbal communication from the patient. However, for patients with difficulty expressing themselves, such as those who are weak, have undergone tracheotomy, or have a low pain threshold, communication efficiency is low. Improper cooperation often leads to repetitive movements or triggers severe pain, increasing the patient's suffering and psychological burden.

[0007] Poor posture adaptability: Traditional manual lifting or simple sling transport equipment is difficult to achieve smooth, multi-posture transitions, such as lying down, sitting, standing, side-lying, and prone, and cannot dynamically adjust the support points to adapt to the patient's comfort during the transfer process.

[0008] Therefore, there is an urgent need for a device and method that can intelligently sense the patient's physiological response and autonomously coordinate and adjust the movement strategy to achieve safe, comfortable and automated patient transport. Summary of the Invention

[0009] This invention provides a multi-degree-of-freedom intelligent transfer device and collaborative control method for patient transport. It can carry the patient's weight in collaboration with multiple robotic arms and sense the patient's muscle tension and pain response during the transfer process in real time through bioelectric signals, dynamically adjusting the transfer action to achieve flexible and adaptive transport.

[0010] To achieve the above objectives, the present invention provides the following technical solution: a multi-degree-of-freedom intelligent transfer device for patient transport, characterized in that it comprises: a mobile chassis for autonomous or assisted movement in the environment; a lifting column fixedly installed on the mobile chassis and capable of vertical lifting adjustment; a multi-degree-of-freedom robotic arm system connected to the lifting column, comprising at least five robotic arms with no less than six degrees of freedom, each robotic arm having a bionic holding plate at its end for supporting the patient, the bionic holding plate being provided with an auxiliary strap interface; a set of auxiliary straps, the auxiliary straps being manually pre-passed under the patient's body, and after the bionic holding plate is positioned at the corresponding part of the patient's body, the two ends of the auxiliary straps are fixed to the bionic holding plate to achieve pre-fixation of the patient; and a multimodal sensing system, comprising: an electromyography and physiological signal sensing module, including a module for attaching... Multiple surface electromyography (EMG) electrodes attached to the patient's skin are used to collect EMG signals from specific muscle groups during transport. A posture and environment perception module includes a vision unit for acquiring three-dimensional information about the patient's position and surrounding environment, a force sensing unit for detecting the contact force between the robotic arm and the patient, and an environment perception unit for navigation and obstacle avoidance. A controller is electrically connected to the mobile chassis, the lifting column, the multi-degree-of-freedom robotic arm system, and the multi-modal perception system. The controller is configured to: coordinate and control the mobile chassis, the lifting column, and the multi-degree-of-freedom robotic arm system in real time based on feedback from the multi-modal perception system, achieving adaptive holding, smooth transfer, and safe transport of the patient. When performing transport control, the controller is configured to determine the patient's muscle tension or pain response in real time based on the EMG signals and adjust the robotic arm's motion strategy accordingly.

[0011] Preferably, the multi-degree-of-freedom robotic arm system includes a torso robotic arm and four limb robotic arms. The torso robotic arm includes a head robotic arm, a shoulder robotic arm, a waist robotic arm, and a hip robotic arm. The bionic holding plate includes a hinged base plate adapted to the end of the limb robotic arms, and constraint grooves installed at the end of the head robotic arm, the shoulder robotic arm, the waist robotic arm, and the hip robotic arm. Pressure sensor arrays are distributed inside the hinged base plate and the constraint grooves.

[0012] Preferably, each joint of the robotic arm integrates a joint torque sensor; the controller is configured to perform force-based impedance control on the end effector of the robotic arm, and based on feedback from the multimodal sensing system, the pressure sensor array, and the joint torque sensor, to achieve adaptive fit and compliant contact between the bionic holding support and the patient's body shape.

[0013] Preferably, the vision unit includes a depth camera for recognizing cooperative docking markers pre-installed on the target bearing surface, wherein the cooperative docking markers are visual tags or wireless positioning beacons.

[0014] Preferably, the controller is further configured to: calculate in real time the compensating motion of the robotic arm joints when transferring a patient being held via the mobile platform, so as to counteract the disturbances caused by the movement of the mobile platform and maintain the absolute stability of the patient's posture in the air.

[0015] Preferably, a collaborative control method for patient transport, applied to the above-mentioned multi-degree-of-freedom intelligent transfer device, is characterized in that the method includes:

[0016] Manual pre-fixation step: Before the device is started, the operator passes each of the auxiliary straps under the patient's body and attaches the surface electromyography electrodes to the patient's preset muscle groups;

[0017] Initial positioning and identification steps: Control the mobile platform to move to the initial position, and identify the patient's position and target bearing surface information through the vision unit;

[0018] Equipment positioning and binding steps: Control the movement of the multi-degree-of-freedom robotic arm system so that each of the bionic holding supports is positioned at the corresponding part of the patient's body; The operator fixes the free ends of each of the auxiliary straps to the corresponding bionic holding supports to complete the mechanical fixation of the patient;

[0019] Adaptive holding step: After the binding is completed, the robotic arm is controlled in impedance control mode to adjust the posture and contact force of the tray according to the feedback of the force sensing unit, so as to achieve stable and uniform support for the patient;

[0020] Collaborative transport steps: After lifting the patient, control the mobile platform to move towards the target bearing surface. During this process, coordinate the control of the robotic arm to perform motion compensation in order to maintain the patient's stable posture.

[0021] Intelligent placement steps: Based on the real-time perception of the target bearing surface by the vision unit, with the goal of minimizing the relative movement and contact impact between the patient and the target bearing surface, the coordinated motion trajectory of the mobile platform and the robotic arm is generated to place the patient smoothly on the target bearing surface;

[0022] Physiological signal closed-loop feedback step: During the adaptive holding, coordinated transfer and intelligent placement steps, the electromyographic signal is continuously monitored; when the characteristic value of the electromyographic signal exceeds the preset pain response threshold, the current movement is immediately paused, and the posture or movement path of the robotic arm is adjusted according to the signal feedback until the electromyographic signal recovers to a safe range, and then the original task or the re-planned task is executed.

[0023] Preferably, the adaptive holding step specifically includes: planning and forming a personalized holding envelope based on the patient's three-dimensional point cloud model; controlling the robotic arm to move to a close position and then switching to impedance control mode so that the bionic holding support plate fits the patient's body with a constant small contact force; adjusting the support angle of each support plate in real time according to the feedback of the pressure sensor array to make the contact pressure distribution uniform.

[0024] Preferably, in the collaborative transfer step, the motion compensation is achieved in the following way: establishing an overall kinematic model including the mobile platform, the lifting column, and the multi-degree-of-freedom robotic arm system; calculating the required compensation angular velocity or angular displacement of each joint of the robotic arm in real time through the inverse kinematic solution of the overall kinematic model based on the motion speed and direction of the mobile platform; controlling the joints of the robotic arm to perform the compensation motion so that the patient's posture relative to the inertial coordinate system remains unchanged.

[0025] Preferably, the method further includes a human-machine collaborative remote operation mode, in which: a guidance command is received from an external input device; the guidance command is fused with the automatic obstacle avoidance constraint and virtual fixture constraint generated by the multimodal perception system to generate a final execution command; wherein the virtual fixture constraint is used to limit the range of motion of the robotic arm end effector or the mobile platform to prevent it from entering a preset danger zone.

[0026] Preferably, the method further includes a full-process safety monitoring step: real-time monitoring of the data from the force sensing unit, joint encoder, and inertial measurement unit; when any monitored data exceeds its corresponding safety threshold, controlling the device to enter a compliant stop state or an emergency stop state, and issuing an alarm.

[0027] The beneficial effects of this invention are as follows:

[0028] 1. Significantly improves the safety and automation level of transportation, reducing reliance on human labor and the risk of occupational injury.

[0029] Through the coordinated support of the multi-degree-of-freedom robotic arm system and the autonomous movement of the mobile chassis, a high degree of automation in the patient transfer process is achieved, freeing nursing staff from heavy manual lifting and fundamentally avoiding the risk of patients falling due to insufficient manpower or coordination errors. It also greatly reduces the incidence of occupational injuries such as lumbar strain on nursing staff.

[0030] 2. Achieve real-time closed-loop feedback and personalized comfort transport based on physiological signals.

[0031] This innovative approach incorporates surface electromyography (EMG) signals as a key feedback variable, enabling indirect and real-time sensing of muscle activation responses triggered by discomfort, tension, or pain during patient transport. The controller dynamically adjusts the robotic arm's gripping force, movement speed, or posture based on real-time changes in the EMG signals, achieving intelligent closed-loop control through a "sensing-response" mechanism. This allows the transport process to proactively adapt to the patient's individual comfort level, making it particularly suitable for patients with low pain thresholds or difficulty expressing themselves.

[0032] 3. Provides multi-attitude adaptive support and an absolutely stable aerial transport experience.

[0033] Equipped with no fewer than five six-degree-of-freedom robotic arms, it can independently and precisely support and control the posture of multiple key parts of the patient, such as the head, torso, and limbs, enabling natural and smooth transitions and maintenance of various postures, including lying down, sitting, and side-lying. A unique motion compensation algorithm can counteract chassis disturbances through the inverse compensation motion of the robotic arms during chassis movement, ensuring absolute stability of the patient's posture relative to the ground in the air and effectively eliminating the swaying and fear associated with traditional transport.

[0034] 4. Achieve high-precision, smooth collaborative docking and placement.

[0035] Combining visual recognition and force control technologies, it can accurately identify and track the pose of the target bearing surface, generating the optimal coordinated motion trajectory to achieve "zero impact" or "micro-impact" docking between the patient and the bearing surface. Based on impedance control, the compliant placement process simulates the buffering effect of manual placement, greatly improving the safety and comfort of the placement phase.

[0036] 5. Equipped with multiple safety protections and a flexible human-machine collaborative operation mode.

[0037] Integrating data from multiple sensors, including force, vision, and joint status sensors, a multi-layered safety monitoring system is constructed, enabling real-time monitoring and graded braking of abnormal forces, overspeed, and out-of-range movements. Simultaneously, it provides a human-machine collaborative remote operation mode, combining the operator's advanced intentions with constraints such as automatic obstacle avoidance and virtual grippers, ensuring operational flexibility in complex scenarios while guaranteeing operational safety through automated boundaries.

[0038] 6. Improve patients' psychological experience and doctor-patient relationship

[0039] The entire transfer process was smooth, quiet, and controlled, reducing the anxiety and fear patients experienced due to unpredictable movement. Intelligent physiological signal responses made patients feel cared for and received personalized care, enhancing their sense of dignity and satisfaction with nursing services.

[0040] In summary, this invention not only technically solves the challenges of manpower, safety, and comfort in patient transport, but also pioneers a new paradigm of rehabilitation nursing that integrates human and machine technologies and provides intelligent response through the deep integration of biosignals and robot control. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of the overall structure of the present invention;

[0043] Figure 2 This is a schematic diagram of the constraint groove, hinged base plate, and patient fixation method of the present invention;

[0044] Figure 3 This is a schematic diagram of the application state of the hinged substrate of the present invention:

[0045] Figure 4 This is a schematic diagram showing the application state of the constraint groove corresponding to the waist part of the present invention:

[0046] Figure 5 This is a flowchart of the collaborative control method of the present invention.

[0047] In the diagram: 1. Mobile chassis; 2. Lifting column; 3. Auxiliary straps; 4. Controller; 5. Torso robotic arm; 6. Limb robotic arms; 7. Head robotic arm; 8. Shoulder robotic arm; 9. Waist robotic arm; 10. Hip robotic arm; 11. Hinge base plate; 12. Constraint groove; 13. Pressure sensor array; 14. Joint torque sensor; 15. Depth camera. Detailed Implementation

[0048] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Example 1: Device Structure

[0050] according to Figure 1 , Figure 2 , Figure 3 , Figure 4As shown, a multi-degree-of-freedom intelligent transfer device for patient transport includes: a mobile chassis 1: adopting a wheeled structure, with drive wheels, driven wheels, and a steering mechanism integrated at the bottom. The chassis integrates a motion controller 4, a battery system, and environmental sensing units, such as lidar and ultrasonic sensors. The mobile chassis 1 can autonomously navigate and move on flat ground or be pushed by an operator. A lifting column 2: vertically fixed to the top of the mobile chassis 1, using an electric push rod or lead screw transmission mechanism, can perform vertical lifting movements under the command of the controller 4, with an adjustment range of 400mm to 1800mm to adapt to different heights of the source and target bearing surfaces, such as between a hospital bed and a wheelchair. A multi-degree-of-freedom robotic arm system: mounted on the top of the lifting column 2 via a robust connecting base. The system includes one trunk robotic arm 5 and four limb robotic arms 6. The trunk robotic arm 5 consists of four independent robotic arms, corresponding to the patient's head, shoulders, waist, and hips respectively. Each robotic arm has at least six degrees of freedom, enabling flexible positioning and posture adjustment of the end effector in three-dimensional space. The four-limb robotic arms 6 include two upper-limb robotic arms and two lower-limb robotic arms, also with at least six degrees of freedom. Bionic support plates: Each robotic arm is equipped with a bionic support plate at its end. For the four-limb robotic arms 6, the end plate is a hinged base plate 11, which adaptively conforms to the natural curve of the patient's limb. For the trunk robotic arm 5, the end plate is a grooved constraint slot 12, used to stably support the patient's head, shoulders, waist, and hips. All bionic support plates have an embedded pressure sensor array 13 on their inner side, i.e., the side in contact with the patient, for real-time monitoring of the contact pressure distribution. The support plates also have auxiliary strap 3 interfaces, such as quick-release buckles or Velcro fastening rings. Auxiliary straps 3: These are a set of soft, high-strength fabric or leather straps. Before the device is activated, the caregiver pre-threads the straps under the patient. Once the bionic holding support is in place, the two ends of the straps are fixed to the corresponding interfaces, thus pre-fixing the patient on the support and preventing accidental slippage during the lifting process.

[0051] The multimodal sensing system includes an electromyography (EMG) and physiological signal sensing module, and a posture and environment sensing module. The EMG and physiological signal sensing module comprises 8-16 disposable surface electromyography (sEMG) electrode patches. Nurses attach these patches to the skin surfaces of pre-defined muscle groups highly correlated with patient movement during transport, such as the erector spinae muscles of the lumbar region, abdominal muscles, and shoulder muscles, to collect EMG signals during transport as indirect indicators of pain and tension responses. The posture and environment sensing module consists of a vision unit, a force sensing unit, and an environment sensing unit. The vision unit includes a depth camera 15 (e.g., an RGB-D camera) mounted on the lifting column 2 or the robotic arm base to acquire 3D point cloud information of the patient and surrounding environment, identifying the patient's initial position, body contour, and pre-defined docking markers on target bearing surfaces, such as the wheelchair backrest (e.g., AprilTag visual tags or UWB wireless positioning beacons). The force sensing unit integrates joint torque sensors 14 at the robotic arm joints and a pressure sensor array 13 on the bionic holding plate to detect the contact force / torque between the robotic arm and the patient. The environmental perception unit is integrated with lidar, millimeter-wave radar, etc. in the mobile chassis 1 to build environmental maps and realize autonomous navigation and dynamic obstacle avoidance.

[0052] Controller 4, the central processing unit of the device, can be a high-performance industrial computer or an embedded system. Controller 4 is electrically connected to the mobile chassis 1, lifting column 2, multi-degree-of-freedom robotic arm system, and various sensing modules via wired or wireless means. Controller 4 internally runs a core control algorithm responsible for: processing feedback information from the multi-modal sensing system; coordinating and controlling the movement of the mobile chassis 1, lifting column 2, and all robotic arms in real time; and executing the device's collaborative control method.

[0053] Example 2: Cooperative Control Method

[0054] according to Figure 5 As shown, the method mainly includes the following steps:

[0055] S1: Manual Pre-fixation and Preparation Steps

[0056] The nursing staff will pass the auxiliary strap 3 through the patient's back, buttocks and under the thighs in sequence.

[0057] The surface electromyography electrodes are attached to the patient's pre-selected muscle groups, such as the skin of the waist and abdomen, and connected to the electromyography signal acquisition device.

[0058] Confirm that the device has sufficient power and that all sensors are working properly.

[0059] S2: Initial Localization and Identification Steps

[0060] Operators issue transfer instructions via a handheld terminal or the human-machine interface of the device itself, such as from a hospital bed to a wheelchair.

[0061] The controller 4 controls the mobile chassis 1 to move autonomously or with assistance to the initial working position next to the bedside.

[0062] The vision unit is activated, and the depth camera 15 scans the patient lying on the hospital bed to obtain a 3D point cloud model of the patient, identify the patient's current position, such as supine or lateral, and the position of key body parts such as the head, shoulders, waist, hips, and limbs.

[0063] At the same time, the vision unit scans the target bearing surface of the wheelchair, identifies the collaborative docking mark on it, and calculates the position and posture of the wheelchair seat plane.

[0064] S3: Device Placement and Binding Steps

[0065] Based on the patient's point cloud model and a preset holding strategy, controller 4 plans the pre-fitting position that each bionic holding support needs to reach. This position is located near the corresponding part of the patient's body, such as about 5-10cm from the body surface, and the posture is roughly parallel to the body surface.

[0066] Control all the robotic arms of the multi-degree-of-freedom robotic arm system to move smoothly to their respective pre-fitting positions.

[0067] The nursing staff checks whether the positions of each support plate are appropriate, and then fixes the two ends of each auxiliary strap 3, which has been pre-passed under the patient, to the strap interface of the corresponding bionic holding support plate to complete the mechanical fixation of the patient.

[0068] S4: Adaptive Holding Steps

[0069] S41: Generate the holding envelope. Based on the patient's point cloud model, the controller 4 plans a personalized holding envelope surface that conforms to the patient's body curve, serving as a reference path for the movement of each support plate.

[0070] S42: Switching to impedance control. This switches each robotic arm from position control mode to force-based impedance control mode. In this mode, the movement of the end effector is determined by feedback from both the target position and the contact force.

[0071] S43: Smooth Fit. Controls each tray to move slowly toward the patient's body along a planned path. When the pressure sensor array 13 detects slight contact with the patient's body surface, such as when the contact force reaches a preset value of 5N, the impedance control algorithm starts to work, causing the tray to continuously and smoothly fit the patient's body with a constant small force, such as 5-15N, until it provides full support.

[0072] S44: Pressure homogenization. Real-time reading of data from the pressure sensor array 13 on each tray. By fine-tuning the pitch and yaw angles of each tray, the pressure distribution on each tray is made as uniform as possible, and the total load-bearing capacity between the trays is reasonably distributed according to the patient's weight to avoid excessive local pressure.

[0073] S5: Collaborative Transfer Steps

[0074] After confirming that the patient is held stably, the controller 4 controls the lifting column 2 to lift smoothly, so that the patient is completely away from the source bearing surface.

[0075] Control the mobile chassis 1 to move the wheelchair towards the target bearing surface according to the planned path.

[0076] Key: Motion compensation. During the movement of the mobile chassis 1, its translation and rotation are transmitted to the robotic arm system via the lifting column 2, causing the patient to swing uncontrollably in the air. To maintain the absolute stability of the patient's posture, the controller 4 performs the following compensation calculations:

[0077] Establish an overall kinematic model that includes the mobile chassis 1, the lifting column 2, and all robotic arms.

[0078] The linear velocity and angular velocity of the mobile chassis 1 are acquired in real time, for example, through the chassis encoder and IMU.

[0079] By using the inverse kinematics of the overall model, the compensating angular velocity or angular displacement required by each robotic arm joint to counteract chassis motion disturbances can be calculated in real time.

[0080] The compensating motion commands are superimposed on the original pose-keeping commands of the robotic arm to control the execution of each joint, thereby keeping the patient's center of gravity stationary relative to the inertial coordinate system or moving slowly along the desired trajectory.

[0081] S6: Smart Placement Steps

[0082] When the mobile chassis 1 carries the patient to the predetermined position above the wheelchair, the vision unit continuously tracks the wheelchair docking marker to accurately obtain the real-time position and posture of the wheelchair seat, especially when the wheelchair may be slightly pushed.

[0083] The controller 4 generates a trajectory for the coordinated descent of the mobile chassis 1 and the robotic arm system, with the optimization objectives of minimizing the relative speed between the patient and the wheelchair seat and minimizing contact impact.

[0084] The system guides the patient to sit smoothly and slowly onto the wheelchair seat. At the moment of contact, impedance control via the robotic arm simulates a cushioned landing.

[0085] Once the patient's weight is fully supported by the wheelchair and the pressure sensor shows a significant decrease in the pressure on the support plate, the support plate is slowly released from its holding force. Finally, the auxiliary strap 3 is released, and the robotic arm is retracted to a safe parking position.

[0086] S7: Closed-loop feedback steps for physiological signals

[0087] Throughout the entire process of holding, transporting, and placing the patient, the electromyography and physiological signal sensing module continuously collects the patient's surface electromyography signals.

[0088] The controller 4 performs real-time processing on the electromyographic signals, such as filtering, rectification, calculating the root mean square (RMS) value, and extracting feature values ​​that reflect the degree of muscle tension.

[0089] A pain response threshold is set, which can be personalized based on the patient's baseline electromyography (EMG) level. When an EMG value is detected that consistently exceeds the threshold, for example, exceeding the baseline by 150% for 0.5 seconds, the controller 4 determines that the patient may be experiencing pain or severe discomfort.

[0090] Trigger Response: Immediately pause all movement, putting the mobile chassis 1 and robotic arm into hold mode. An alarm is sent to the caregiver via the HMI. After confirmation by the caregiver, the robotic arm's support posture can be automatically fine-tuned based on the area with the strongest electromyographic signal, such as slightly easing pressure or changing the support angle, or adjustments can be made by the caregiver. Once the electromyographic signal returns to a safe range, the device can continue performing its original task, or the controller 4 can re-plan part of the movement path based on the new body position.

[0091] Example 3: Additional Functions and Modes

[0092] Human-machine collaborative remote operation mode:

[0093] This mode can be enabled in complex or unstructured environments. The operator issues guiding commands via a handheld force feedback joystick or VR controller.

[0094] The controller 4 integrates the operator's guidance commands with the automatic obstacle avoidance constraints generated by the multimodal perception system to prevent collisions with the environment and virtual gripper constraints, and limits the range of motion of the robotic arm, such as prohibiting the robotic arm from entering the dangerous area below the patient's chest cavity, in real time.

[0095] The integrated instructions are sent to the actuators to achieve enhanced semi-autonomous control with human intervention in the loop, which utilizes human judgment while ensuring safety through automation.

[0096] Full-process safety monitoring:

[0097] The controller 4 continuously monitors force sensing units such as joint torque, contact force, joint encoder position, speed, and IMU data built into the robotic arm.

[0098] Set safety thresholds for various data, such as maximum joint torque, maximum contact pressure, and maximum movement speed.

[0099] When any monitored data exceeds its threshold, a safety response is immediately triggered. For minor over-limits, a compliant stop is initiated, bringing the robot to a smooth stop with maximum deceleration; for severe over-limits or sudden anomalies, such as the detection of a large impact force, an emergency stop is initiated, cutting off the drive power, holding the robotic arm in place by a passive compliant mechanism or brakes, and issuing an audible and visual alarm.

[0100] Example 4: Control Algorithm Description

[0101] Impedance control: Joint torque of the robotic arm Calculated by the formula: ,in For Jacobian matrices, , , For the desired inertia, damping, and stiffness matrices, / / This refers to the actual acceleration / velocity / position. / / As the expected value, The desired contact force. This is achieved through adjustment. , , It can achieve the soft or rigid properties of the tray in contact with the patient.

[0102] Inverse kinematics solution for motion compensation: based on the global kinematics model ,in Position of the patient To move the chassis to position 1, This refers to the joint angle of the robotic arm. To maintain... The desired speed, usually zero or very small, is known. The chassis speed can be solved by inverting the model to obtain the required joint compensation speed. .

[0103] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A multi-degree-of-freedom intelligent transfer device for patient transport, characterized in that, include: Mobile chassis (1) for autonomous or assisted movement in an environment; The lifting column (2) is fixedly installed on the mobile chassis (1) and can be vertically lifted and lowered. A multi-degree-of-freedom robotic arm system, connected to the lifting column (2), includes at least five robotic arms with no less than six degrees of freedom. Each robotic arm has a bionic holding plate at its end for supporting the patient. The bionic holding plate is provided with an auxiliary strap (3) interface. A set of auxiliary straps (3) can be manually passed under the patient's body in advance, and after the bionic holding support plate is located at the corresponding part of the patient's body, the two ends of the auxiliary straps (3) are fixed to the bionic holding support plate to achieve pre-fixation of the patient; Multimodal sensing systems include: The electromyography and physiological signal sensing module includes multiple surface electromyography electrodes for attaching to the patient's skin surface to collect electromyography signals of specific muscle groups during transport. The body posture and environment perception module includes a vision unit for acquiring three-dimensional information about the patient's body position and the surrounding environment, a force sensing unit for detecting the contact force between the robotic arm and the patient, and an environment perception unit for navigation and obstacle avoidance. The controller (4) is electrically connected to the mobile chassis (1), the lifting column (2), the multi-degree-of-freedom robotic arm system, and the multimodal sensing system. The controller (4) is configured as follows: Based on the feedback from the multimodal sensing system, the mobile chassis (1), lifting column (2) and multi-degree-of-freedom robotic arm system are coordinated and controlled in real time to achieve adaptive holding, smooth transfer and safe transport of the patient; The controller (4) is configured to determine the patient’s muscle tension or pain response in real time based on the electromyographic signal when performing transport control, and adjust the movement strategy of the robotic arm accordingly.

2. The multi-degree-of-freedom intelligent transfer device for patient transport according to claim 1, characterized in that, The multi-degree-of-freedom robotic arm system includes a torso robotic arm (5) and four limb robotic arms (6). The torso robotic arm (5) includes a head robotic arm (7), a shoulder robotic arm (8), a waist robotic arm (9), and a hip robotic arm (10). The bionic holding plate includes a hinged base plate (11) adapted to the end of the limb robotic arms (6) and a constraint groove (12) installed at the end of the head robotic arm (7), the shoulder robotic arm (8), the waist robotic arm (9), and the hip robotic arm (10). Pressure sensor arrays (13) are distributed inside the hinged base plate (11) and the constraint groove (12).

3. The multi-degree-of-freedom intelligent transfer device for patient transport according to claim 2, characterized in that, Each joint of the robotic arm is integrated with a joint torque sensor (14); the controller (4) is configured to perform force-based impedance control on the end of the robotic arm and, based on feedback from the multimodal sensing system, the pressure sensor array (13) and the joint torque sensor (14), achieve adaptive fit and compliant contact between the bionic holding support and the patient's body shape.

4. The multi-degree-of-freedom intelligent transfer device for patient transport according to claim 1, characterized in that, The vision unit includes a depth camera (15) for recognizing a cooperative docking mark pre-set on the target bearing surface, wherein the cooperative docking mark is a visual tag or a wireless positioning beacon.

5. The multi-degree-of-freedom intelligent transfer device for patient transport according to claim 1, characterized in that, The controller (4) is further configured to: When transferring a patient being held via the mobile platform, the compensatory movements of the robotic arm joints are calculated in real time to counteract the disturbances caused by the movement of the mobile platform and maintain the absolute stability of the patient's posture in the air.

6. A collaborative control method for patient transport, applied to a multi-degree-of-freedom intelligent transfer device for patient transport as described in any one of claims 1-5, characterized in that, The method includes: Manual pre-fixation steps: Before the device is started, the operator passes each of the auxiliary straps (3) under the patient's body and attaches the surface electromyography electrodes to the patient's preset muscle groups; Initial positioning and identification steps: Control the mobile platform to move to the initial position, and identify the patient's position and target bearing surface information through the vision unit; Equipment positioning and binding steps: Control the movement of the multi-degree-of-freedom robotic arm system so that each of the bionic holding trays is positioned at the corresponding part of the patient's body; The operator fixes the free end of each of the auxiliary straps (3) to the corresponding bionic holding tray to complete the mechanical fixation of the patient; Adaptive holding step: After the binding is completed, the robotic arm is controlled in impedance control mode to adjust the posture and contact force of the tray according to the feedback of the force sensing unit, so as to achieve stable and uniform support for the patient; Collaborative transport steps: After lifting the patient, control the mobile platform to move towards the target bearing surface. During this process, coordinate the control of the robotic arm to perform motion compensation in order to maintain the patient's stable posture. Intelligent placement steps: Based on the real-time perception of the target bearing surface by the vision unit, with the goal of minimizing the relative movement and contact impact between the patient and the target bearing surface, the coordinated motion trajectory of the mobile platform and the robotic arm is generated to place the patient smoothly on the target bearing surface; Physiological signal closed-loop feedback step: During the adaptive holding, coordinated transfer and intelligent placement steps, the electromyographic signal is continuously monitored; when the characteristic value of the electromyographic signal exceeds the preset pain response threshold, the current movement is immediately paused, and the posture or movement path of the robotic arm is adjusted according to the signal feedback until the electromyographic signal recovers to a safe range, and then the original task or the re-planned task is executed.

7. The collaborative control method for patient transport according to claim 6, characterized in that, The adaptive holding step specifically includes: Based on the patient's 3D point cloud model, a personalized holding envelope is planned and formed. After controlling the robotic arm to move to the close position, switch to impedance control mode so that the bionic holding support plate fits the patient's body with a constant micro contact force; Based on the feedback from the pressure sensor array (13), the support angle of each support plate is adjusted in real time to make the contact pressure distribution uniform.

8. The collaborative control method for patient transport according to claim 6, characterized in that, In the coordinated transport step, the motion compensation is achieved in the following way: Establish an overall kinematic model including the mobile platform, the lifting column (2) and the multi-degree-of-freedom robotic arm system; Based on the movement speed and direction of the mobile platform, the required compensation angular velocity or angular displacement of each joint of the robotic arm is calculated in real time through the inverse kinematics solution of the overall kinematic model. The robotic arm joints are controlled to perform the compensating motion so that the patient's pose relative to the inertial coordinate system remains unchanged.

9. The collaborative control method for patient transport according to claim 6, characterized in that, The method also includes a human-machine collaborative remote operation mode, in which: Receive guidance commands from external input devices; The guidance command is fused with the automatic obstacle avoidance constraints and virtual fixture constraints generated by the multimodal perception system to generate the final execution command; The virtual gripper constraint is used to limit the range of motion of the robotic arm end effector or the mobile platform to prevent it from entering a preset danger zone.

10. The collaborative control method for patient transport according to claim 6, characterized in that, The method also includes a full-process safety monitoring step: Real-time monitoring of data from the force sensing unit, joint encoder, and inertial measurement unit; When any monitored data exceeds its corresponding safety threshold, the device is controlled to enter a compliant stop state or an emergency stop state and an alarm is issued.

Images