This article presents a collision avoidance system for multiple robots based on the current autonomous car collision avoidance system. The purpose of the system is to improve the current autonomous car collision avoidance system by including data input of other vehicles’ velocity and positioning via vehicle-to-vehicle communication into the current autonomous car collision avoidance system. There are two TurtleBots used in experimental testing. TurtleBot is used as the robot agent while Google Lightweight Communication and Marshalling is used for inter-robot communication. Additionally, Gazebo software is used to run the simulation. There are two types of collision avoidance system algorithm (collision avoidance system without inter-robot communication and collision avoidance system with inter-robot communication) that are developed and tested in two main road crash scenarios, rear end collision scenario and junction crossing intersection collision scenario. Both algorithms are tested and run both in simulation and experiment setup, each with 10 repetitions for Lead TurtleBot sudden stop, Lead TurtleBot decelerate, Lead TurtleBot slower speed, and straight crossing path conditions. Simulation and experimental results data for each algorithm are recorded and tabulated. A comprehensive comparison of performance between the proposed algorithms is analyzed. The results showed that the proposed system is able to prevent collision between vehicles with an acceptable success rate.

中文翻译：

---

多自主移动机器人防撞系统开发

本文在当前自主汽车防撞系统的基础上，提出了一种多机器人防撞系统。该系统的目的是通过将其他车辆的速度和定位数据输入通过车对车通信纳入当前的自动驾驶汽车防撞系统来改进现有的自动驾驶汽车防撞系统。实验测试中使用了两个 TurtleBot。TurtleBot 用作机器人代理，而谷歌轻量级通信和编组用于机器人间通信。此外，Gazebo 软件用于运行模拟。有两种类型的防撞系统算法（没有机器人间通信的防撞系统和有机器人间通信的防撞系统）在两种主要道路碰撞场景中开发和测试，追尾碰撞场景和路口交叉路口碰撞场景. 两种算法都在模拟和实验设置中进行了测试和运行，每个算法重复 10 次，分别用于 Lead TurtleBot 突然停止、Lead TurtleBot 减速、Lead TurtleBot 较慢的速度和直线交叉路径条件。每种算法的模拟和实验结果数据都被记录并制成表格。分析了所提出算法之间的性能的综合比较。结果表明，所提出的系统能够以可接受的成功率防止车辆之间的碰撞。