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Method Article

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

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DOI:

10.3791/56422

October 14th, 2017

In This Article

Summary

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.

Abstract

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.

Introduction

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

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Protocol

1. Construction of the Machine

  1. Assemble the chassis as illustrated, securing mechanical components using appropriate fasteners. (Figure 1)
    NOTE: The chassis, which comprises the baseboard, motor, wheels, etc., is the primary component of the robot responsible for its motion. Thus, during assembly, keep the bracket straight.
  2. Tin the wire lead and both the positive and negative electrodes. Solder two wire leads onto the two ends of the motor, connecting the red lead to the positive electrode and the black lead to the negative electrode.
  3. Assemble the shaft sleeve, the motors and the wheels.
      ....

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Results

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 1.......

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Discussion

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 ope.......

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Disclosures

The authors have nothing to disclose.

Acknowledgements

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).

....

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
structural partsUPTECMONYH HARL1-1
structural partsUPTECMONYH HARL2-1
structural partsUPTECMONYH HARL3-1
structural partsUPTECMONYH HARL4-1
structural partsUPTECMONYH HARL5-1
structural partsUPTECMONYH HARL5-2
structural partsUPTECMONYH HARU3A
structural partsUPTECMONYH HARU3B
structural partsUPTECMONYH HARU3C
structural partsUPTECMONYH HARU3F
structural partsUPTECMONYH HARU3G
structural partsUPTECMONYH HARU3H
structural partsUPTECMONYH HARU3J
structural partsUPTECMONYH HARI3
structural partsUPTECMONYH HARI5
structural partsUPTECMONYH HARI7
structural partsUPTECMONYH HARCGJ
link componentUPTECMONYH HARLM1
link componentUPTECMONYH HARLM2
link componentUPTECMONYH HARLM3
link componentUPTECMONYH HARLM4
link componentUPTECMONYH HARLX1
link componentUPTECMONYH HARLX2
link componentUPTECMONYH HARLX3
link componentUPTECMONYH HARLX4
Steering gear structure componentUPTECMONYH HARKD
Steering gear structure componentUPTECMONYH HARDP
Infrared sensorUPTECMONYH HARE18-B0Digital sensor
Infrared Range FinderSHARPGP2D12
Gray level sensorSHARPGP2Y0A02YK0F
proMOTION CDSSHARPCDS 5516The robot steering gear
motor drive moduleRisymHG7881
solder wireELECALL63A
terminalBright wire5264
motorBX motor60JX
cameraLogitechC270
Drilling machineXIN XIANG16MMPlease be careful
Soldering stationYIHUA8786DBe careful to be burn
screwdriverEXPLOIT043003
TweezersR`DEERRST-12

References

  1. Charalampous, K., Kostavelis, I., Gasteratos, A. Robot navigation in large-scale social maps: An action recognition approach. Expert Syst Appl. 66 (1), 261-273 (2016).
  2. Huang, Y., &Wang, Q. N. Disturbance rejection of Central Pattern Generator based torque-stiffness-controlled dynamic walking. Neurocomputing. 170 (1), 141-151 (2015).
  3. Tepljakov, A., Petlenkov, E., Gonzalez, E., Belikov, J.

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Tags

Four Quadrant Motor ControlMulti Sensor FusionRobotic ManipulatorHardware ConstructionSensor DebuggingAutonomous IdentificationIntelligent Search Algorithm