Brain-controlled self-rolling spherical robot based on SSVEP and control method thereof

Abstract

The application discloses a brain-controlled self-rolling spherical robot based on SSVEP and a control method thereof, which comprises an upper hemisphere shell and a lower hemisphere shell, the upper hemisphere shell and the lower hemisphere shell are fixed through buckles, a main body support is arranged in an inner cavity formed by the upper hemisphere shell and the lower hemisphere shell, a main control circuit board is fixed to the upper half of the main body support, a direct current motor A, a direct current motor B and a central shaft are fixed to the lower half of the main body support, an LED circuit board is arranged on the main control circuit board, the LED circuit board is in X shape, and LED lamp beads are arranged on four corners of the LED circuit board. The application integrates LEDs as stimulation sources in the spherical robot body, so that a user can control the robot and capture and judge the surrounding environment in real time. The application designs a spherical robot with a unique structure, and can ensure that the internal structure does not shake in any process.

Description

Technical Field

[0001] This invention relates to the field of robotics, and more particularly to a brain-controlled self-rolling spherical robot based on SSVEP and its control method. Background Technology

[0002] Spherical robots are a new type of mobile robot with a spherical shape. They can move and explore flexibly in their environment. Due to their unique structure, the actuators, control systems, sensing systems, and power supply systems required for their movement can be integrated inside their spherical shell. This results in a compact structure, flexible movement, and strong self-balancing capabilities. Based on their flexible movement characteristics, spherical robots can be used in confined spaces and can perform actions that wheeled and tracked robots cannot. Because of their strong adaptability and sealing, they can adapt to different movement conditions, such as sand and water surfaces, meeting the requirements for robot mobility and adaptability to harsh environments. These robots can be used as special-purpose robots for detection and reconnaissance in confined and complex spaces. Compared to traditional wheeled robots, their unique structure allows them to fully utilize their flexibility and maneuverability in complex terrain, greatly improving work efficiency. Due to the safety inherent in their unique shape, self-rolling robots are also widely used as educational toys for children.

[0003] In recent years, spherical robots have entered the education and home entertainment fields as commercial products. However, research on the motion control of spherical robots is still incomplete. Combining brain-computer interface (BCI) control methods can achieve remote thought control. The integration of this novel human-computer interaction method with the mobility of spherical robots holds broad application prospects.

[0004] Brain-computer interface (BCI) technology provides an interactive channel that bypasses normal neural pathways: when the brain is active, neurons in the cerebral cortex generate electrical signals. These signals can be collected using EEG acquisition devices, processed to decode the brain's intentions, and then the corresponding control signals are transmitted to control external devices. In recent years, BCI technology has developed rapidly. From initially controlling various input devices to input commands into computers, it has evolved to control physical devices such as wheelchairs and robotic arms. Developing BCI-based control of physical devices has significant practical implications.

[0005] SSVEP (Specialized Vessel Vibration Episode) is an EEG feature that elicits a response to a sustained periodic blinking stimulus. Specifically, it manifests as a strong, synchronous EEG signal in the occipital lobe of the brain; this characteristic signal is SSVEP. Algorithms designed to recognize this feature can decode the user's current gaze intent. Compared to other paradigms, SSVEP has advantages such as high information transmission rate and high signal-to-noise ratio, leading to its widespread research. Currently, the most significant factor limiting the application of SSVEP brain-computer interfaces is the separation between stimulus presentation and control entity. This often necessitates the use of third- or first-person perspective video feedback to provide information about the actual scene before deploying the blinking stimulus to induce SSVEP. This often results in incomplete and delayed real-time information capture.

[0006] Therefore, those skilled in the art are dedicated to developing a spherical robot system based on brain-computer interface control, enabling thought-based control of the spherical robot. Using LEDs as a stimulus source, integrated into the spherical robot body, users can control the robot's movement via the brain-computer interface, while simultaneously capturing and assessing the robot's surrounding environment in real time. Based on this brain-controlled spherical robot, further research can be conducted on efficient SSVEP evoked paradigms and high-accuracy continuous decoding algorithms for educational and entertainment scenarios, developing efficient motion mechanisms and robust motion control methods for spherical robots. Summary of the Invention

[0007] To achieve the above objectives, the present invention provides a brain-controlled self-rolling spherical robot based on SSVEP, comprising an upper hemisphere and a lower hemisphere, which are fixed together by snap-fit. A main support frame is provided in the cavity formed by the upper and lower hemispheres. A main control circuit board is fixed on the upper part of the main support frame, and a DC motor A, a DC motor B, and a central shaft are fixed on the lower part of the main support frame. An LED circuit board is provided on the main control circuit board, which is X-shaped and has LED beads at its four corners.

[0008] Furthermore, the main support frame is also provided with a bullseye wheel B and a bullseye wheel C.

[0009] Furthermore, the main control circuit board is also provided with a bullseye wheel bracket, and the main control circuit board, LED circuit board and bullseye wheel bracket are fixed to the main body bracket by fasteners.

[0010] Furthermore, a bullseye wheel A is fixed on the bullseye wheel bracket, and bullseye wheels A, B, and C are in contact with the inner surfaces of the upper and lower hemispheres, supporting the entire internal structure.

[0011] Furthermore, the DC motors A and B serve as actuators, with pinions A and B fixed on their respective output shafts and meshing with the large gears inside rubber wheels A and B to complete the transmission. The outer rubber portions of rubber wheels A and B achieve the robot's movement through friction with the upper and lower hemispheres.

[0012] Furthermore, the rubber wheel A and rubber wheel B are composed of a large gear on the inner side and a rubber part on the outer side. The rubber part enables the robot to move by friction with the upper and lower hemispheres. The rubber wheel A and rubber wheel B are fixed on the central shaft by flange bearings and can rotate around the central shaft.

[0013] Furthermore, the LED beads are numbered sequentially, starting with the number 1 at the bottom right corner.

[0014] Furthermore, a battery is also fixed on the upper part of the main support frame.

[0015] A control method for a brain-controlled self-rolling spherical robot based on SSVEP, as described above: The user induces SSVEP by looking at different LED beads inside the spherical robot. The user's EEG signal is acquired through a wireless EEG acquisition device. The acquired EEG signal is amplified and then processed and feature-recognized by a host computer to obtain the sequence number of the LED bead that the user is looking at, thereby controlling the movement of the spherical robot.

[0016] Furthermore, the LED bead serial numbers flash at different frequencies and / or in different colors.

[0017] Compared with the prior art, the beneficial effects of this invention are:

[0018] This invention integrates LEDs as a stimulus source into the body of a spherical robot, allowing users to simultaneously monitor and assess the surrounding environment while controlling the robot. Each of the four corners of the circuit board inside the spherical robot has an LED that flashes at different frequencies. Users can control the spherical robot by directly observing it within a certain range, without needing other devices. By collecting and decoding the user's EEG signals, the LED number the user is looking at can be obtained, allowing the user to issue corresponding commands to the robot. Compared to controlling the robot through a separate interface, this method of direct observation allows users to more easily observe the robot's working environment and make more timely and effective judgments.

[0019] This invention designs a spherical robot with a unique structure that ensures the internal structure remains stable during any process. Multiple bullseye wheels contact the inner surface of the spherical shell, limiting the translation of the internal structure without affecting its rotation. Spherical robots using this structure are more stable and easier to control. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of a preferred embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of the structure from another direction of a preferred embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of the structure of a motor according to a preferred embodiment of the present invention;

[0023] Figure 4 This is a schematic diagram of the LED circuit board according to a preferred embodiment of the present invention;

[0024] Figure 5 This is a block diagram of a brain-controlled spherical robot system according to a preferred embodiment of the present invention. Detailed Implementation

[0025] The preferred embodiments of the present invention are described below with reference to the accompanying drawings to make the technical content clearer and easier to understand. The present invention can be embodied in many different forms, and the scope of protection of the present invention is not limited to the embodiments mentioned herein.

[0026] The mechanical structure of the spherical robot is composed of Figure 1 , Figure 2 As shown, the spherical robot's outer shell consists of an upper hemisphere 1 and a lower hemisphere 2, which can be secured to each other by snap-fitting.

[0027] The spherical robot uses DC motors A3 and B4 as actuators. Rubber wheels A7 and B8 consist of a large inner gear and an outer rubber section, which rotate synchronously. Small gears A5 and B6 are fixed to the output shafts of DC motors A3 and B4, respectively, and mesh with the large inner gears of rubber wheels A7 and B8 to complete the transmission. The outer rubber sections of rubber wheels A7 and B8 achieve robot movement through friction with the upper hemisphere 1 and lower hemisphere 2. Rubber wheels A7 and B8 are fixed to a central shaft via flange bearings and can rotate around the central shaft. Threaded holes are opened at both ends of the central shaft; bolt A passes through washer A, and bolt B passes through washer B, and are fixed to the threaded holes on both sides of the central shaft. The inner flange bearing contacts the main support, and the outer flange bearing contacts the washer, preventing axial movement of the rubber wheels and flange bearings.

[0028] DC motors A3 and B4, with their central shafts 9 fixed to the lower half of the main support 10, have their upper half structure used to fix the battery 11 and the main control circuit board 12. The main control circuit board 12 is located above the battery 11, and the battery 11 provides power to the main control circuit board 12.

[0029] Above the main control circuit board 12 are the LED circuit board 13 and the bullseye wheel bracket 14. All three have holes at corresponding positions for fixing, allowing them to be secured to the main support bracket 10 using fasteners such as long bolts 15.

[0030] Bullseye wheel A6 is fixed to bullseye wheel bracket 14, while bullseye wheels B17 and C18 are fixed to main body bracket 10. Bullseye wheels A6, B17, and C18 are in contact with the inner surfaces of the upper hemisphere 1 and lower hemisphere 2, supporting the entire internal structure and preventing it from shaking during the operation of the spherical robot.

[0031] Four LEDs are distributed on LED circuit board 13, each flashing at a certain frequency during operation, serving as the stimulation source for SSVEP. The specific shape of this circuit board and the position of the LEDs are as follows. Figure 4 As shown. Each LED is labeled with a number from 1 to 4, indicating its serial number, starting with number 1 in the bottom right corner and ordered counterclockwise. Based on feedback from the inertial measurement unit and motor encoder, the spherical robot can be controlled to maintain a stable posture during movement, making it easier for users to observe the LEDs inside the spherical shell.

[0032] To use, start the robot and place it flat on the ground, ensuring it can connect to the wireless network. At this point, the four LEDs inside the robot (numbered 1-4) should blink normally at 9Hz, 10Hz, 11Hz, and 12Hz respectively. The user should stand within a circular area with a radius of 2 meters centered on the spherical robot, ensuring they can clearly see the blinking LEDs inside the robot and that there are no obstructions between them and the robot. The user can move along with the spherical robot as it moves.

[0033] The user induces SSVEP by gazing at different LEDs inside a spherical robot. The user's EEG signals are acquired via a wireless EEG acquisition device, amplified, and then processed and identified by a host computer.

[0034] Identification of visual steady-state evoked potentials

[0035] The SSVEP signal is identified using a canonical correlation analysis (CCA) decoder, which has high accuracy and low computational complexity.

[0036] For high-dimensional variables X and Y, and their linear combination x = X T WX y = Y T W Y CCA can find the optimal weight vector W that maximizes the correlation coefficient between x and y by solving the following problem. X W Y :

[0037]

[0038] The largest ρ(x,y) obtained by this method is the maximum correlation coefficient.

[0039] For the identification of multi-channel SSVEP signals, assuming the number of channels is N and the stimulation frequencies are f1, f2, ..., f... k There are k in total. In the above problem, X is an EEG signal with N channels and a length of M, where X∈R. N×M Template signal Y i ∈R Q×M It can be represented as:

[0040]

[0041] Where f i N represents the frequency of the visual stimulus, i = 1, 2, ..., k. h For harmonic orders, Q = 2N h .

[0042] The EEG signal and k reference signals are used as inputs for CCA (Computational Neuro-Anatomical Search). The final result C can be expressed as:

[0043]

[0044] Where ρ i For the EEG signal X and the i-th frequency f i Corresponding reference signal Y i The correlation coefficient.

[0045] This allows the system to determine the LED number the user is currently looking at, and then send the corresponding instructions to the spherical robot.

[0046] Remote control of spherical robots

[0047] The spherical robot's orientation is based on its internal structure. As the robot rotates, four LEDs, acting as visual stimuli, rotate along with the internal structure. Using the robot's orientation as a reference, the relative positions of LEDs 1-4 are right rear, right front, left front, and left rear, corresponding to the commands "stop," "turn right," "turn left," and "forward," respectively. Because the labeling next to the LEDs is small and difficult to observe from a distance, and because the robot's internal structure is symmetrical, users may have difficulty discerning the robot's current orientation after multiple rotations. To make it easier for users to distinguish the commands represented by each LED, the four LEDs can flash in different colors. This allows users to easily differentiate the four LEDs and reduces reaction time.

[0048] CCA (Comparison and Analysis) is used to identify the user's EEG signals and obtain the correlation coefficients between the user's EEG signals and various reference signals. A threshold δ is set; when the difference between the largest correlation coefficient and all other correlation coefficients is greater than δ, it is assumed that the user is focusing on the target corresponding to the largest correlation coefficient, and the corresponding instruction is sent to the spherical robot. After sending an instruction, the decoding of the EEG signal will continue for a period of time, and new instructions will be decoded after a certain interval. During this period, the user can switch targets. The spherical robot will execute the corresponding operation according to the received instruction. After receiving an instruction, the spherical robot will execute the instruction until the instruction is switched or a stop instruction is received. Adjusting parameters such as instruction execution time, instruction sending interval, and robot movement speed can achieve a more ideal control effect. The block diagram of the brain-controlled spherical robot system is as follows. Figure 5 As shown.

[0049] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A SSVEP-based brain-controlled self-rolling spherical robot, characterized in that, The application relates to a spherical robot, which comprises an upper hemisphere shell (1) and a lower hemisphere shell (2), the upper hemisphere shell (1) and the lower hemisphere shell (2) are fixed through buckling, an inner cavity formed by the upper hemisphere shell (1) and the lower hemisphere shell (2) is provided with a main support (10), a main control circuit board (12) is fixed on the upper half of the main support (10), a direct current motor A (3), a direct current motor B (4) and a central shaft (9) are fixed on the lower half of the main support (10), an LED circuit board (13) is arranged on the main control circuit board (12), the LED circuit board (13) is X-shaped, and LED lamp beads are arranged on four corners, the direct current motor A (3) and the direct current motor B (4) are actuators, a small gear A (5) and a small gear B (6) are respectively fixed on output shafts of the direct current motor A (3) and the direct current motor B (4) and are respectively meshed with large gears on inner sides of rubber wheels A (7) and rubber wheels B (8) to complete transmission, and the outer rubber parts of the rubber wheels A (7) and the rubber wheels B (8) realize the movement of the robot through friction with the upper hemisphere shell (1) and the lower hemisphere shell (2).

2. The SSVEP-based brain-controlled self-rolling spherical robot according to claim 1, wherein, The main support (10) is further provided with a bull's eye wheel B (17) and a bull's eye wheel C (18).

3. The SSVEP-based brain-controlled self-rolling spherical robot according to claim 2, wherein, The main control circuit board (12) is further provided with a bull's eye wheel support (14), and the main control circuit board (12), the LED circuit board (13) and the bull's eye wheel support (14) are fixed to the main support (10) through fasteners (15).

4. The SSVEP-based brain-controlled self-rolling spherical robot according to claim 3, wherein, The bull's eye wheel support (14) is fixed with a bull's eye wheel A (16), and the bull's eye wheel A (16), the bull's eye wheel B (17) and the bull's eye wheel C (18) are in contact with inner surfaces of the upper hemisphere shell (1) and the lower hemisphere shell (2) to support the whole internal structure.

5. The SSVEP-based brain-controlled self-rolling spherical robot according to claim 1, wherein, The rubber wheels A (7) and the rubber wheels B (8) are composed of large gears on inner sides and rubber parts on outer sides, the rubber parts realize the movement of the robot through friction with the upper hemisphere shell (1) and the lower hemisphere shell (2), and the rubber wheels A (7) and the rubber wheels B (8) are fixed on the central shaft (9) through flange bearings and can rotate around the central shaft (9).

6. The SSVEP-based brain-controlled self-rolling spherical robot according to claim 1, wherein, The LED lamp beads start from No. 1 in the lower right corner and are sequentially numbered.

7. The SSVEP-based brain-controlled self-rolling spherical robot according to claim 1, wherein, The main support (10) is further fixed with a battery (11) on the upper half.

8. The control method of the SSVEP-based brain-controlled self-rolling spherical robot according to any one of claims 1-7, characterized in that: A user induces SSVEP by staring at different LED lamp beads in the spherical robot, brain electrical signals of the user are acquired through a wireless brain electrical acquisition device, the acquired brain electrical signals are amplified and then subjected to signal processing and feature recognition by an upper computer to obtain the LED lamp bead serial number stared at by the user, so that the movement of the spherical robot is controlled. 9.The SSVEP-based control method of the brain-controlled self-rolling spherical robot according to claim 8, wherein, The LED lamp bead serial number is blinked at different frequencies and / or different colors.

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