This paper presents a novel multi-stage perception system for collision avoidance in mobile robots. In the here considered scenario, a mobile robot stands in a workspace with a set of potential targets to reach or interact with. When a human partner appears gesturing to the target, the robot must plan a collision-free trajectory to reach the goal. To solve this problem, a full-perception system composed of consecutive convolutional neural networks in parallel and processing stages is proposed for generating a collision-free trajectory according to the desired goal. This system is evaluated at each step in real environments and through several performance tests, proving to be a robust and fast system suitable for real-time applications.

中文翻译：

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移动机器人的多阶段深度学习感知系统

本文提出了一种新颖的多阶段感知系统，用于在移动机器人中避免碰撞。在这里考虑的场景中，移动机器人站在一个工作空间中，该工作空间具有一组可能要到达或与其交互的潜在目标。当人类伴侣出现示意目标时，机器人必须计划无碰撞的轨迹才能达到目标。为了解决这个问题，提出了一种由并行的并行卷积神经网络和处理阶段组成的全感知系统，用于根据期望的目标生成无碰撞的轨迹。该系统在实际环境中的每个步骤都经过评估，并通过了一些性能测试，被证明是一种适用于实时应用的强大而快速的系统。