The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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 divided^3,4, 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 module^5,6, 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 regulation^7,8,9. 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 error^6,10. Since the equivalent time constant of the electric current loop is relatively small, the four-quadrant motor^11,12 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.