1 Introduction
In today's fast-paced digital age, the integration of computer technology, communication systems, and consumer electronics has transformed everyday life. As a result, embedded systems have become increasingly prevalent, with numerous devices such as smartwatches, smartphones, MP3 players, PDAs, home appliances, and automotive electronics now part of daily routines. Among these, robotics stands out as a rapidly growing field, both in research and practical applications. Industrial and service robots are now being used in various aspects of human life, from manufacturing to household tasks. Their ability to perform complex tasks autonomously is widely recognized, making them an essential component of modern living.
This paper presents an embedded intelligent tracing robot designed using the AT89S52 microcontroller. The robot is equipped with sensors, motor drivers, and software that allow it to navigate through a maze intelligently. Unlike traditional remote-controlled toy cars, this robot exhibits a degree of autonomy and intelligence, serving as a prototype for future smart toy vehicles.
2 System Hardware Architecture and Working Principle
The hardware architecture of the embedded intelligent tracing robot is illustrated in Figure 1. At its core is the AT89S52 microcontroller, which communicates with several peripheral modules, including the motor driver, power supply, communication interface, obstacle avoidance system, and online programming module. Infrared sensors are connected to ports P0.5, P0.6, and P0.7 via the mainboard interfaces P8, P9, and P10. When P0.5 = 0, it indicates an obstacle in front; P0.6 = 0 means there is an obstacle on the left, and P0.7 = 0 shows an obstacle on the right. The left and right motors are connected to the motor driver module through the P5 interface.
After power-on, the sensor collects signals from the maze wall, and based on the input from port P0, the robot controls the direction of the motors, enabling it to turn left, turn right, or move forward, thus completing the task of navigating through the maze from entrance to exit.
3 System Interface Circuit Design
3.1 Microcontroller Module
The AT89S52 is a low-power, high-performance CMOS 8-bit microcontroller featuring 8KB of ISP Flash memory, capable of being erased and reprogrammed up to 1,000 times. It is compatible with the MCS-51 instruction set and uses a standard 80C51 pin configuration. This makes it ideal for various embedded control applications due to its cost-effectiveness and flexibility.
The AT89S52 includes 40 pins, 8KB of Flash program memory, 256B of RAM, 32 I/O lines, 5 interrupt levels, two 16-bit timers, a full-duplex serial port, a watchdog timer, and an internal clock oscillator. During development, a development board is used for testing and debugging. Once the system is fully functional, the microcontroller is transferred to the robot’s motherboard, with only essential circuits like the crystal oscillator and reset circuit retained.
3.2 Sensor Module
The photoelectric sensor operates by emitting infrared light from its emitter and detecting the reflected signal. For accurate detection, the surface of the object must have both black and white areas to absorb and reflect the light. The comparator adjusts the threshold voltage using resistor R3, ensuring a clean output waveform suitable for the microcontroller. The sensor is connected to the AT89S52 through the mainboard interfaces P8, P9, and P10. Based on the sensor readings, the robot detects obstacles and adjusts its movement accordingly.
3.3 DC Motor Drive Circuit and Power Module
The DC motor is connected to the mainboard’s motor driver module via the P5 interface. The L298 chip is used as the motor driver, with four of its pins connected to the microcontroller. The microcontroller can control the motor’s direction and speed. To provide stable power, a step-up converter is used to supply approximately 5V to the microcontroller and the surrounding circuitry.
4 Software Design Module
4.1 Software Development Environment and Search Algorithm
This project uses Keil U Version 2 as the development environment, combining C and assembly languages for efficient programming. A search algorithm known as the "right-hand rule" is implemented, where the robot follows the right wall to find its way out of the maze. Compared to depth-first search, this method reduces memory usage and avoids backtracking, making it more suitable for simple hardware setups.
The algorithm assumes that the maze consists of seven basic structures: straight path, dead end, T-junction, crossroads, corner, and endpoint. Depending on the number of branches, paths are categorized into two-way and three-way junctions. The algorithm includes subroutines for straight movement, left turns, right turns, and other maneuvers. The main program coordinates these functions, guiding the robot through the maze efficiently.
4.2 Algorithm Flowchart Description
The flowchart of the maze navigation algorithm is shown in Figure 3. After turning on the power, the microcontroller reads the sensor values and decides the robot’s movement. If P0.7 = 1 (no obstacle on the left), the robot follows the right-hand rule and executes the right turn subroutine. If P0.7 = 0 and P0.5 = 0, it performs a left turn. Otherwise, it moves straight. This process continues until the robot exits the maze.
5 Conclusions and Innovations
This paper outlines the design of an embedded intelligent tracing robot based on the AT89S52 microcontroller. The robot uses a right-hand (or left-hand) rule to navigate mazes, relying on infrared sensors, a DC motor drive system, and a power supply. After thorough testing, the robot successfully completes the task of finding its way through the maze without external input.
The innovation lies in the use of infrared sensors for automatic obstacle detection and software-based control of the robot’s movements. This system is particularly useful for environments that are difficult for humans to access. With low cost and high reliability, this robot serves as a valuable reference for the development of smart toys and autonomous systems.
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