The accumulation of litter is increasing in many places and is consequently becoming a problem that must be dealt with. In this paper, we present a manipulator robotic system to collect litter in outdoor environments. This system has three functionalities. Firstly, it uses colour images to detect and recognise litter comprising different materials. Secondly, depth data are combined with pixels of waste objects to compute a 3D location and segment three-dimensional point clouds of the litter items in the scene. The grasp in 3 Degrees of Freedom (DoFs) is then estimated for a robot arm with a gripper for the segmented cloud of each instance of waste. Finally, two tactile-based algorithms are implemented and then employed in order to provide the gripper with a sense of touch. This work uses two low-cost visual-based tactile sensors at the fingertips. One of them addresses the detection of contact (which is obtained from tactile images) between the gripper and solid waste, while another has been designed to detect slippage in order to prevent the objects grasped from falling. Our proposal was successfully tested by carrying out extensive experimentation with different objects varying in size, texture, geometry and materials in different outdoor environments (a tiled pavement, a surface of stone/soil, and grass). Our system achieved an average score of 94% for the detection and Collection Success Rate (CSR) as regards its overall performance, and of 80% for the collection of items of litter at the first attempt.

翻译：垃圾堆积在许多地方日益增多，因而成为一个亟待解决的问题。本文提出一种用于户外环境垃圾收集的机械臂机器人系统。该系统具备三项功能。首先，它利用彩色图像检测并识别由不同材料构成的垃圾。其次，深度数据与废弃物像素相结合，用于计算三维定位并分割场景中垃圾物品的三维点云。随后，针对每个废弃物实例的分割点云，为配备夹爪的机械臂估算三自由度抓取位姿。最后，为实现夹爪的触觉感知，我们设计并部署了两种基于触觉的算法。本研究在指尖部署了两个低成本视觉触觉传感器：其中一个用于检测夹爪与固体废弃物之间的接触（通过触觉图像获取），另一个则专门设计用于检测滑动，以防止抓取物体脱落。我们通过在多种户外环境（瓷砖路面、石土表面及草地）中对不同尺寸、纹理、几何形状和材质的物体进行大量实验，成功验证了所提方案。系统整体性能在检测与收集成功率方面平均达到94%，首次尝试收集垃圾物品的成功率达80%。