低成本智能循迹机器人,带避障和自调PID,适合教育实践
LineMaster Pro: A Low-Cost Intelligent Line Following Robot with PID Control and Ultrasonic Obstacle Avoidance for Educational Robotics
- 基于Arduino Nano,用红外+超声传感器实现精准循迹与实时避障
- 平均追踪误差1.18厘米,避障成功率96.7%,成本仅28.50美元
- 适合资源有限的学校或学生动手项目,可快速复现
循迹机器人是机器人教育的基础平台,但市售产品价格高昂(150-300美元),且缺乏集成障碍物检测能力。本文提出LineMaster Pro,基于Arduino Nano构建,采用双TCRT5000红外传感器实现高精度循迹,搭配HC-SR04超声波传感器进行实时障碍物检测,使用Ziegler-Nichols优化的数字式PID控制器,并通过分层有限状态机实现鲁棒避障。通过四阶段传感器校准方法,确保在不同光照与表面条件下稳定运行。200次受控实验及72小时连续测试表明:在0.4米/秒速度下,平均追踪误差为1.18厘米(95%置信区间[1.06, 1.30]);在10-40厘米范围内,障碍物检测可靠性达96.7%,误报率仅0.7%;路径偏离后成功恢复率达94%。相比传统开关控制,PID控制提升43%性能(p<0.001)。硬件总成本为28.50美元(据孟加拉市场价格验证),较商用方案降低94%,为资源受限环境下的机器人教育提供了实用标杆。
原文摘要 · Abstract (English)
Line following robots are fundamental platforms in robotics education, yet commercially available solutions remain prohibitively expensive ($150-300$) while lacking integrated obstacle detection capabilities essential for real-world applications. This paper presents LineMaster Pro, an intelligent low-cost line following robot implemented on an Arduino Nano platform that integrates dual TCRT5000 infrared sensors for precision line tracking, an HC-SR04 ultrasonic sensor for real-time obstacle detection, a digitally tuned PID controller with Ziegler-Nichols optimization, and a hierarchical finite state machine for robust obstacle avoidance. A systematic four-phase sensor calibration methodology ensures reliable operation across varying lighting and surface conditions. Experimental validation through 200 controlled trials and 72-hour continuous operation demonstrates mean tracking accuracy of 1.18 cm at 0.4 m/s (95\% CI [1.06, 1.30]), obstacle detection reliability of 96.7\% within 10-40 cm range with 0.7\% false positive rate, and 94\% successful recovery from path deviations. The PID implementation achieves 43\% improvement over conventional on-off control ($p<0.001$). At a total hardware cost of \$28.50 based on verified Bangladesh market prices, LineMaster Pro achieves a 94\% cost reduction compared to commercial alternatives, establishing a practical benchmark for accessible robotics education in resource-constrained environments.
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