We present a protocol on modular design and production of intelligent robots to help scientific and technical workers design intelligent robots with special production tasks based on personal needs and individualized design.
Intelligent robots are part of a new generation of robots that are able to sense the surrounding environment, plan their own actions and eventually reach their targets. In recent years, reliance upon robots in both daily life and industry has increased. The protocol proposed in this paper describes the design and production of a handling robot with an intelligent search algorithm and an autonomous identification function.
First, the various working modules are mechanically assembled to complete the construction of the work platform and the installation of the robotic manipulator. Then, we design a closed-loop control system and a four-quadrant motor control strategy, with the aid of debugging software, as well as set steering gear identity (ID), baud rate and other working parameters to ensure that the robot achieves the desired dynamic performance and low energy consumption. Next, we debug the sensor to achieve multi-sensor fusion to accurately acquire environmental information. Finally, we implement the relevant algorithm, which can recognize the success of the robot's function for a given application.
The advantage of this approach is its reliability and flexibility, as the users can develop a variety of hardware construction programs and utilize the comprehensive debugger to implement an intelligent control strategy. This allows users to set personalized requirements based on their needs with high efficiency and robustness.
Robots are complex, intelligent machines that combine knowledge of several disciplines, including mechanics, electronics, control, computers, sensors and artificial intelligence 1,2. Increasingly, robots are assisting or even replacing humans in the workplace, especially in industrial production, due to the advantages robots possess in performing repetitive or dangerous tasks. The design of the intelligent robot protocol in the current study is based on a closed-loop control strategy, specifically path planning based on a genetic algorithm. Furthermore, the functional modules have been strictly divided3,4, which may provide a solid foundation for future optimization work, so that the robots have a strong capacity for upgrades.
The modular implementation of the robotic platform is based primarily on the following methods: multi-dimensional combination control strategy in motor control module5,6, and intelligent exploration based on a genetic algorithm in the optimization algorithm module.
We use double closed-loop control of the DC motor and four-quadrant motor operation in the motor control module. Double closed-loop speed control means that the output of the speed regulator serves as the input of the current regulator, allowing it to control the current and torque of the motor. The advantage of this system is that the torque of the motor can be controlled in real-time based on the difference between the given speed and the actual speed. When the difference between given and actual speeds is relatively large, the motor torque increases and the speed changes faster to drive the motor speed toward the given value as quickly as possible, which makes for rapid speed regulation7,8,9. Conversely, when the speed is relatively close to the given value, it can automatically reduce the torque of the motor to avoid excessive speed, allowing the speed to achieve the given value relatively quickly with no error6,10. Since the equivalent time constant of the electric current loop is relatively small, the four-quadrant motor11,12 can respond more quickly to suppress the impact of interference when the system is subject to external interference. This allows it to improve the stability and anti-jamming ability of the system.
We choose a genetic intelligent optimization algorithm with the highest efficiency based on the results of a simulation run in MATLAB. A genetic algorithm is a stochastic parallel search algorithm based on the theory of natural selection in genetics. It constitutes an efficient method for finding the global optimal solution in the absence of any initial information. It regards the solution set of the problem as a population, thereby increasing the quality of the solution via continuous selection, crossover, mutation and other genetic operations. With regard to path planning by intelligent robots, difficulty arises as a result of insufficient initial information, complicated environments and nonlinearity. Genetic algorithms are better able to solve the problem of path planning because they possess global optimization ability, strong adaptability and robustness in solving nonlinear problems; there are no specific restrictions on the problem; the calculation process is simple; and there are no special requirements for the search space 13,14.
1. Construction of the Machine
2. Debugging the Steering Engine and Driver Module
3. Debugging the Sensors
4. Control Scheme
5. Write the Program
6. Application Scenario
In the diagram of the double closed-loop motion control program, purple represents a given speed signal and yellow represents the value of the control system output. Figure 17 clearly shows that the double closed-loop control system is significantly more effective than an open-loop system. The actual overshoot of the output of the double closed-loop system is relatively small and the dynamic performance of the system is better. ( Figure 17)
Figure 18 shows the robot's color recognition accuracy under the influence of reflected light at different wavelengths. In practice, due to different light conditions, the reflected light wavelength of the target object will fluctuate within a certain range. To inspect the recognition accuracy of the machine, a test is conducted in the range of the wavelengths of yellow light (565-595 nm) and red light (625-740 nm). If the value returned by the camera is 1, the color recognition is accurate. In the range of 585-593 nm, the yellow light recognition accuracy rate of the camera exceeds 90%, whereas the rate outside the range decreases rapidly. Similarly, within the 660-700 nm range, the red light recognition accuracy rate exceeds 90%, while the rate outside the range decreases rapidly. The test results demonstrate that, under appropriate illumination, the robot achieves color recognition with a small margin of error. ( Figure 18)
Figure 19 illustrates the relation between the camera's color recognition accuracy and the distance. The recognition accuracy is inversely correlated with the distance. As illustrated in the experimental results, when the distance is between 0-30 cm, the color recognition accuracy of the camera is greater than 80%. The results demonstrate that this program has strong utility. ( Figure 19)
Figure 1: Construction of the chassis. Please click here to view a larger version of this figure.
Figure 2: Installation of the infrared sensors. Please click here to view a larger version of this figure.
Figure 3: The effect of installation. Please click here to view a larger version of this figure.
Figure 4: Debugging work screen. Please click here to view a larger version of this figure.
Figure 5: Connection of robot steering gear. Please click here to view a larger version of this figure.
Figure 6: Electrical connection principles. Please click here to view a larger version of this figure.
Figure 7: Electrical connection principles. Please click here to view a larger version of this figure.
Figure 8: Set ID number. Please click here to view a larger version of this figure.
Figure 9: Two sensors. Please click here to view a larger version of this figure.
Figure 10: Simulation model of DC motor. Please click here to view a larger version of this figure.
Figure 11: Current regulatory system. Please click here to view a larger version of this figure.
Figure 12: Simulation model of double closed-loop control. Please click here to view a larger version of this figure.
Figure 13: Diagram of four-quadrant operation of the motor. Please click here to view a larger version of this figure.
Figure 14: H-bridge circuit. Please click here to view a larger version of this figure.
Figure 15: The workflow of color recognition. Please click here to view a larger version of this figure.
Figure 16: The workflow of quick search. Please click here to view a larger version of this figure.
Figure 17: Simulink diagram. Please click here to view a larger version of this figure.
Figure 18: Color recognition accuracy under the influence of reflected light at different wavelengths. Please click here to view a larger version of this figure.
Figure 19: Relationship between the camera's color recognition accuracy and the distance. Please click here to view a larger version of this figure.
In this paper, we designed a type of intelligent robot that can be built autonomously. We implemented the proposed intelligent search algorithm and autonomous recognition by integrating several software programs with hardware. In the protocol, we introduced basic approaches for configuring the hardware and debugging the intelligent robot, which may help users design a suitable mechanical structure of their own robot. However, during actual operation, it is necessary to pay attention to stability of the structure, its operating range, the degree of freedom and space utilization, to ensure that these parameters meet the requirements. A reasonable mechanical structure ensures high precision, high flexibility, and high robustness of the robot. To design complicated mechanical structures, the user can combine software such as Adams to construct a simulation model and apply virtual prototyping technology. This may allow them to exclude possibilities that do not satisfy the technical requirements or possibilities that are not mechanically feasible.
One potential issue is the inability of the robot to accurately achieve its desired functions. This may stem primarily from two causes. The first is the inability of the sensors to meet the requirements. For example, during the first test, the cleaning robot in this study was unable to successfully push obstacles out of the working area. This was because the range of the infrared sensor on the equipment was somewhat narrow, which meant that the robot could not achieve the requisite acceleration when it detected an obstacle. This issue could be solved by increasing the detection range of the infrared sensor. To address these issues, an additional level of debugging of the sensors may be necessary, based on the situation or application. The second is the inability of the selected motor to meet the performance requirement. When choosing a motor, priority must be given to a motor with suitable starting performance, operational stability and low noise within the budget.
To begin design and production of a new robot, the parameters for a manual configuration scheme must be defined to control the behavior of the robot, so that it may adapt to the demands of a new task. Simultaneously, all processes must follow the steps presented in the protocol. An advantage of the modular design of the robot lies in its clear division of work, which allows it to be developed via the collaboration of various engineers. Mechanical engineers design the structure of the hardware, electrical engineers design the motor control strategy, and controls engineers design the search algorithm. Thus, the work of each module can be developed independently to accomplish a specific task. We provide a basic design scheme for each module, to help users search for the optimal scheme for a particular application.
The range of potential applications will expand considerably as intelligent robot technology matures. It will prove to be an invaluable resource to individuals in the fields of ocean development, space exploration, industrial and agricultural production, social service, and entertainment, to name a few. This technology will gradually replace human beings in dangerous and unsanitary work environments. Intelligent robots will continue to develop toward multi-robot cooperation, and intelligent and networked direction.
The authors have nothing to disclose.
The authors would like to express their gratitude to Mr. Yaojie He for his assistance in performing the experiments reported in this paper. This work was supported in part by the National Natural Science Foundation of China (No. 61673117).
structural parts | UPTECMONYH HAR | L1-1 | |
structural parts | UPTECMONYH HAR | L2-1 | |
structural parts | UPTECMONYH HAR | L3-1 | |
structural parts | UPTECMONYH HAR | L4-1 | |
structural parts | UPTECMONYH HAR | L5-1 | |
structural parts | UPTECMONYH HAR | L5-2 | |
structural parts | UPTECMONYH HAR | U3A | |
structural parts | UPTECMONYH HAR | U3B | |
structural parts | UPTECMONYH HAR | U3C | |
structural parts | UPTECMONYH HAR | U3F | |
structural parts | UPTECMONYH HAR | U3G | |
structural parts | UPTECMONYH HAR | U3H | |
structural parts | UPTECMONYH HAR | U3J | |
structural parts | UPTECMONYH HAR | I3 | |
structural parts | UPTECMONYH HAR | I5 | |
structural parts | UPTECMONYH HAR | I7 | |
structural parts | UPTECMONYH HAR | CGJ | |
link component | UPTECMONYH HAR | LM1 | |
link component | UPTECMONYH HAR | LM2 | |
link component | UPTECMONYH HAR | LM3 | |
link component | UPTECMONYH HAR | LM4 | |
link component | UPTECMONYH HAR | LX1 | |
link component | UPTECMONYH HAR | LX2 | |
link component | UPTECMONYH HAR | LX3 | |
link component | UPTECMONYH HAR | LX4 | |
Steering gear structure component | UPTECMONYH HAR | KD | |
Steering gear structure component | UPTECMONYH HAR | DP | |
Infrared sensor | UPTECMONYH HAR | E18-B0 | Digital sensor |
Infrared Range Finder | SHARP | GP2D12 | |
Gray level sensor | SHARP | GP2Y0A02YK0F | |
proMOTION CDS | SHARP | CDS 5516 | The robot steering gear |
motor drive module | Risym | HG7881 | |
solder wire | ELECALL | 63A | |
terminal | Bright wire | 5264 | |
motor | BX motor | 60JX | |
camera | Logitech | C270 | |
Drilling machine | XIN XIANG | 16MM | Please be careful |
Soldering station | YIHUA | 8786D | Be careful to be burn |
screwdriver | EXPLOIT | 043003 | |
Tweezers | R`DEER | RST-12 |