Purpose

Most manufacturing plants choose the easy way of completely separating human operators from robots to prevent accidents, but as a result, it dramatically affects the overall quality and speed that is expected from human–robot collaboration. It is not an easy task to ensure human safety when he/she has entered a robot’s workspace, and the unstructured nature of those working environments makes it even harder. The purpose of this paper is to propose a real-time robot collision avoidance method to alleviate this problem.

Design/methodology/approach

In this paper, a model is trained to learn the direct control commands from the raw depth images through self-supervised reinforcement learning algorithm. To reduce the effect of sample inefficiency and safety during initial training, a virtual reality platform is used to simulate a natural working environment and generate obstacle avoidance data for training. To ensure a smooth transfer to a real robot, the automatic domain randomization technique is used to generate randomly distributed environmental parameters through the obstacle avoidance simulation of virtual robots in the virtual environment, contributing to better performance in the natural environment.

Findings

The method has been tested in both simulations with a real UR3 robot for several practical applications. The results of this paper indicate that the proposed approach can effectively make the robot safety-aware and learn how to divert its trajectory to avoid accidents with humans within the workspace.

Research limitations/implications

The method has been tested in both simulations with a real UR3 robot in several practical applications. The results indicate that the proposed approach can effectively make the robot be aware of safety and learn how to change its trajectory to avoid accidents with persons within the workspace.

Originality/value

This paper provides a novel collision avoidance framework that allows robots to work alongside human operators in unstructured and complex environments. The method uses end-to-end policy training to directly extract the optimal path from the visual inputs for the scene.

中文翻译：

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使用深度强化学习的机器人避障系统

目的

大多数制造工厂选择将人类操作员与机器人完全分开以防止事故的简单方法，但结果是，它极大地影响了人机协作的整体质量和速度。当他/她进入机器人的工作空间时，要确保人的安全并不是一件容易的事，而这些工作环境的非结构化性质使其更加困难。本文的目的是提出一种实时机器人避碰方法来缓解这一问题。

设计/方法/方法

在本文中，通过自监督强化学习算法训练模型从原始深度图像中学习直接控制命令。为了减少初始训练过程中样本效率低下和安全性的影响，使用虚拟现实平台模拟自然工作环境并生成避障数据进行训练。为确保顺利转移到真实机器人，采用自动域随机化技术，通过虚拟机器人在虚拟环境中的避障模拟，生成随机分布的环境参数，有助于在自然环境中获得更好的性能。

发现

该方法已在真实 UR3 机器人的两个模拟中进行了测试，用于多种实际应用。本文的结果表明，所提出的方法可以有效地使机器人具有安全意识，并学习如何转移其轨迹以避免在工作空间内与人类发生事故。

研究限制/影响

该方法已在多个实际应用中使用真实的 UR3 机器人进行了两次模拟测试。结果表明，所提出的方法可以有效地使机器人意识到安全并学习如何改变其轨迹以避免与工作空间内的人员发生事故。

原创性/价值

本文提供了一种新颖的防撞框架，允许机器人在非结构化和复杂的环境中与人类操作员一起工作。该方法使用端到端策略训练直接从场景的视觉输入中提取最佳路径。