Intelligent robot motion control method

Abstract

The invention discloses an intelligent robot motion control method, which comprises the following steps that: S10, a controller receives and analyzes an instruction, and determines a target posture; s20, according to the difference between the target posture and the current posture, the angle and the speed, needing to rotate, of each joint of the robot are calculated; and S30, the controller drives the motor to enable all joints of the robot to move according to the preset track and speed until the robot reaches the target posture. According to the robot, through the controller, the motors are driven to enable all joints of the robot to move, and preset coherent actions are completed.

Description

Technical Field

[0001] This invention belongs to the field of intelligent robots, and specifically relates to a motion control method for intelligent robots. Background Technology

[0002] Against the backdrop of an accelerating global aging population, effectively improving the well-being of the elderly and meeting their multi-dimensional emotional needs has become a pressing social issue. Based on the rapid development of intelligent and information technology, intelligent companion robots, by integrating cutting-edge technologies such as artificial intelligence, the Internet of Things, and robotics, have constructed a comprehensive, 24 / 7, multimodal care system. As a product of the fusion of artificial intelligence and robotics, intelligent companion robots have become an important tool for addressing the needs of elderly care. Their research and application cover multiple dimensions, including emotional companionship, health monitoring, and daily living assistance, and exhibit a complex development trend in terms of technological breakthroughs, market expansion, and ethical exploration. Intelligent robot companion systems not only significantly reduce the cost of human care but also improve care efficiency through precise and personalized service models. Currently, in addressing the dual challenges of aging and digital transformation, intelligent companion robots have become a key area of ​​global academic research and industrial application, with their technological evolution path exhibiting significant transnational differences.

[0003] By introducing reinforcement learning algorithms and affective computing models, the new generation system can proactively identify users' emotional fluctuations (such as through facial micro-expression recognition and speech prosody analysis) and provide timely psychological guidance based on context-aware technology. Furthermore, the adoption of a distributed edge computing architecture enables the system to achieve cross-device collaboration and real-time decision-making capabilities while ensuring data privacy. This has significant theoretical and practical value for building a sustainable smart elderly care ecosystem. Summary of the Invention

[0004] In view of this, the present invention proposes a motion control method for an intelligent robot, comprising the following steps: S10, the controller receives and parses the command to determine the target attitude; S20: Based on the difference between the target posture and the current posture, calculate the angle and speed that each joint of the robot needs to rotate; S30: The controller drives the motors to make the robot's joints move along a predetermined trajectory and speed until the robot reaches the target posture.

[0005] Preferably, adjustments are made in real time in S20 and S30 based on information fed back from the sensors.

[0006] Preferably, the sensor includes a gyroscope and an accelerometer.

[0007] Preferably, in step S30, several intermediate values ​​are periodically inserted between the starting and ending positions using an interpolation method to achieve a smooth transition.

[0008] Preferably, S30 includes a reactive method to adjust the posture in real time according to environmental changes.

[0009] Compared with the prior art, the intelligent robot motion control method disclosed in this invention has at least the following beneficial effects: The motion control of this invention can integrate programs suitable for the elderly, such as Baduanjin and Tai Chi, through a self-developed health and exercise database. It uses Kinect motion sensing technology to achieve motion-following training and combines physiological indicator detection data to build a personalized health management plan to ensure the safety of the elderly during exercise. Attached Figure Description

[0010] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration: Figure 1 This is a flowchart illustrating the steps of the intelligent robot motion control method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the time axis interface of the intelligent robot motion control method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the time axis setting for the intelligent robot motion control method according to an embodiment of the present invention; Figure 4 This is a schematic diagram of adjusting robot joints in an embodiment of the intelligent robot motion control method of the present invention. Detailed Implementation

[0011] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0012] See Figure 1 The flowchart of the intelligent robot motion control method of the present invention is as follows: S10, the controller receives and parses the instruction to determine the target posture; S20: Based on the difference between the target posture and the current posture, calculate the angle and speed that each joint of the robot needs to rotate; S30: The controller drives the motors to make the robot's joints move along a predetermined trajectory and speed until the robot reaches the target posture.

[0013] In a specific embodiment, a NAO robot is used, which has 25 degrees of freedom and drives each joint through more than 20 DC motors. With the help of mechanical structures such as gears, the robot can perform basic actions such as walking, sitting and standing, and grasping with its hands. It can even flexibly complete some complex actions, such as practicing Tai Chi or taking the "driving test".

[0014] The controller of a NAO robot is responsible for receiving commands, calculating joint motion parameters, and driving motors to bring the robot to a specified posture. The controller may employ various algorithms and techniques, such as interpolation methods (timed insertion of intermediate values ​​between the starting and ending positions for smooth transitions) and reactive methods (real-time adjustment of posture based on environmental changes). Furthermore, the controller may integrate sensors (such as gyroscopes and accelerometers) to monitor the robot's posture and motion status in real time, enabling more precise control.

[0015] When the NAO robot's posture needs to be changed, the controller first receives and parses the instruction to determine the target posture. Then, based on the difference between the target posture and the current posture, it calculates the required rotation angle and speed for each joint. Next, the controller drives the motors to move each joint along a predetermined trajectory and speed until the robot reaches the target posture. Throughout the process, the controller may also need to make real-time adjustments based on sensor feedback to ensure the accuracy and stability of the posture change.

[0016] In the motion control module, the timeline instruction box allows for intuitive coding of health-preserving exercise movements. The timeline editing interface mainly consists of three parts, see [link to relevant documentation]. Figure 2 1 is the action operation area. The top row of buttons is the timeline editor and timeline properties, and the bottom row is the stop, rewind, play, and forward buttons. 2 is a timeline. The small flag to the right indicates the start of the action, and the small flag to the left indicates the end of the action. The gray blocks record the robot's action at a certain point in time. 3 is the behavior layer. Clicking the plus sign allows you to add behavior layers. You can name them yourself. Each behavior layer is an editing area where you can add LEDs, music, etc. to the action. Dragging a behavior layer allows you to set the start time of that behavior layer.

[0017] See Figure 3 The timeline features a vertical line with a right-hand directional arrow indicating the start of an action, and a vertical line with a left-hand directional arrow indicating the end of an action. The vertical line also indicates the position of the currently selected frame. Small squares between two adjacent tick marks on the timeline represent frames, with one small square representing one frame. Every five frames are numbered. Right-clicking on a frame allows you to select the "Store Joint at Joint Frame" command, recording the joint's motor state. The frame then turns gray, signifying that it has become a keyframe. A specific interpolation method is used between two keyframes to ensure a smooth transition in the action.

[0018] Before editing the robot's movements, you should first click the "Autonomous Life On / Off" button in the status bar, and then click the "Wake Up" button. This will turn off the NAO robot's autonomous life mode. In autonomous life mode, the NAO robot will execute a series of programs, perceive its surrounding environment, and make a series of reactions. At this time, the motors are active, and the motors cannot be adjusted for choreography.

[0019] In the robot view menu, bring up the detector. Click on the robot's limbs or head in the robot view to adjust the position of all 25 joints. See [link to relevant documentation]. Figure 4 The five parameters represent the motor states of different joints of the robot arm, with degrees representing the motor angles relative to the robot's initial state. Clicking on the robot's left arm in the robot view and dragging the manipulation slider in the detector changes the robot's left arm's posture, allowing the joint motor states to be stored in the motion frame. When setting actions on a physical robot, clicking the "Enable / Deactivate Joint Chain Stiffness" button allows you to directly apply actions to the physical robot. When the software is connected to a virtual robot, the "Enable / Deactivate Joint Chain Stiffness" button is grayed out.

[0020] In the Behavior Layer module, click the Add button to create a new behavior layer. Modify the name of the behavior layer in the input box; it will be used to play background music and prompts. Right-click on a frame at a tick mark in the timeline and select the "Store Joint in Keyframe" command to create a keyframe. Multiple behavior layers can be created and keyframes added using the same steps. Each action and behavior within a behavior layer is parallel. Adjust the robot's specific actions on the keyframes and save them to allow the robot to perform the exercise.

[0021] While using a timeline command box allows for convenient and intuitive programming of the NAO robot's movements, it's difficult to achieve precise control over the robot's motion details. A better approach is to use a Python command box to call the joint control methods provided by the ALMotion module, employing appropriate interpolation methods to enable the robot to perform continuous movements.

[0022] In addition to the embodiments described above, the present invention may have other implementations. All technical solutions formed by equivalent substitution or equivalent transformation are within the scope of protection claimed by the present invention.

[0023] The present invention has been described in detail above, but its specific implementation is not limited thereto. Various modifications or alterations can be made by those skilled in the art without departing from the spirit and scope of the claims of this application.

Claims

1. A method for controlling the motion of an intelligent robot, characterized in that, Includes the following steps: S10, the controller receives and parses the command to determine the target attitude; S20: Based on the difference between the target posture and the current posture, calculate the angle and speed that each joint of the robot needs to rotate; S30: The controller drives the motors to make the robot's joints move along a predetermined trajectory and speed until the robot reaches the target posture.

2. The intelligent robot motion control method according to claim 1, characterized in that, In S20 and S30, adjustments are made in real time based on information fed back from the sensors.

3. The intelligent robot motion control method according to claim 2, characterized in that, The sensors include a gyroscope and an accelerometer.

4. The intelligent robot motion control method according to claim 1, characterized in that, In step S30, several intermediate values ​​are periodically inserted between the start and end positions using an interpolation method to achieve a smooth transition.

5. The intelligent robot motion control method according to claim 1, characterized in that, The S30 includes a reactive method to adjust the posture in real time according to environmental changes.

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