室内环境中基于ml的响应式导航自主机器人(CS)

对于在室内环境中操作的自主移动机器人来说，局部或反应式导航是必不可少的。SLAM、计算机视觉等技术需要强大的计算能力，这增加了成本。同样，使用基本方法会使机器人容易产生不一致的行为。本文的目标是开发一种平衡成本和准确性的机器人，通过使用机器学习，根据四个超声传感器的距离输入来预测最佳避障移动，这些传感器策略性地安装在机器人的前面、前面左、前面右和后面。底层硬件由Arduino Uno和树莓派3B组成。机器学习模型首先根据机器人收集的数据进行训练。然后Arduino继续轮询传感器，计算距离值，在紧急需要躲避时，Arduino做出适当的机动。在其他场景中，传感器数据通过USB连接发送到树莓派，机器学习模型生成最佳的移动来导航，然后发送到Arduino来驱动电机。该系统安装在一个2-WD机器人底盘上，并在杂乱的室内环境中进行了测试，获得了最令人印象深刻的结果。

原文题目：Autonomous bot with ML-based reactive navigation for indoor environment

原文：Local or reactive navigation is essential for autonomous mobile robots which operate in an indoor environment. Techniques such as SLAM, computer vision require significant computational power which increases cost. Similarly, using rudimentary methods makes the robot susceptible to inconsistent behavior. This paper aims to develop a robot that balances cost and accuracy by using machine learning to predict the best obstacle avoidance move based on distance inputs from four ultrasonic sensors that are strategically mounted on the front, front-left, front-right, and back of the robot. The underlying hardware consists of an Arduino Uno and a Raspberry Pi 3B. The machine learning model is first trained on the data collected by the robot. Then the Arduino continuously polls the sensors and calculates the distance values, and in case of critical need for avoidance, a suitable maneuver is made by the Arduino. In other scenarios, sensor data is sent to the Raspberry Pi using a USB connection and the machine learning model generates the best move for navigation, which is sent to the Arduino for driving motors accordingly. The system is mounted on a 2-WD robot chassis and tested in a cluttered indoor setting with most impressive results.