Robotics： From Automation to Intelligent Systems

This paper presents a brief overview of the progress that has been made in autonomous robots during the past few years. It presents the fundamental problems that have been addressed to enable the successful deployment of robotic automation in industrial environments. It also describes some of the challenges facing future autonomous applications in more complex scenarios, such as urban vehicle automation.

An Improved High Precision 3D Semantic Mapping of Indoor Scenes from RGB-D Images

This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images.The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time performance.To address these issues,we first adopt the Elastic Fusion algorithm to select key frames from indoor environment image sequences captured by the Kinect sensor and construct the indoor environment space model.Then,an indoor RGB-D image semantic segmentation network is proposed,which uses multi-scale feature fusion to quickly and accurately obtain object labeling information at the pixel level of the spatial point cloud model.Finally,Bayesian updating is used to conduct incremental semantic label fusion on the established spatial point cloud model.We also employ dense conditional random fields(CRF)to optimize the 3D semantic map model,resulting in a high-precision spatial semantic map of indoor scenes.Experimental results show that the proposed semantic mapping system can process image sequences collected by RGB-D sensors in real-time and output accurate semantic segmentation results of indoor scene images and the current local spatial semantic map.Finally,it constructs a globally consistent high-precision indoor scenes 3D semantic map.

Detecting broiler chickens on litter floor with the YOLOv5-CBAM deep learning model

For commercial broiler production,about 20,000–30,000 birds are raised in each confined house,which has caused growing public concerns on animal welfare.Currently,daily evaluation of broiler wellbeing and growth is conducted manually,which is labor-intensive and subjectively subject to human error.Therefore,there is a need for an automatic tool to detect and analyze the behaviors of chickens and predict their welfare status.In this study,we developed a YOLOv5-CBAM-broiler model and tested its performance for detecting broilers on litter floor.The proposed model consisted of two parts:(1)basic YOLOv5 model for bird or broiler feature extraction and object detection;and(2)the convolutional block attention module(CBAM)to improve the feature extraction capability of the network and the problem of missed detection of occluded targets and small targets.A complex dataset of broiler chicken images at different ages,multiple pens and scenes(fresh litter versus reused litter)was constructed to evaluate the effectiveness of the new model.In addition,the model was compared to the Faster R-CNN,SSD,YOLOv3,EfficientDet and YOLOv5 models.The results demonstrate that the precision,recall,F1 score and an mAP@0.5 of the proposed method were 97.3%,92.3%,94.7%,and 96.5%,which were superior to the comparison models.In addition,comparing the detection effects in different scenes,the YOLOv5-CBAM model was still better than the comparison method.Overall,the proposed YOLOv5-CBAM-broiler model can achieve real-time accurate and fast target detection and provide technical support for the management and monitoring of birds in commercial broiler houses.

Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data

The remote mapping of minerals and discrimination of ore and waste on surfaces are important tasks for geological applications such as those in mining.Such tasks have become possible using ground-based,close-range hyperspectral sensors which can remotely measure the reflectance properties of the environ-ment with high spatial and spectral resolution.However,autonomous mapping of mineral spectra mea-sured on an open-cut mine face remains a challenging problem due to the subtleness of differences in spectral absorption features between mineral and rock classes as well as variability in the illumination of the scene.An additional layer of difficulty arises when there is no annotated data available to train a supervised learning algorithm.A pipeline for unsupervised mapping of spectra on a mine face is pro-posed which draws from several recent advances in the hyperspectral machine learning literature.The proposed pipeline brings together unsupervised and self-supervised algorithms in a unified system to map minerals on a mine face without the need for human-annotated training data.The pipeline is eval-uated with a hyperspectral image dataset of an open-cut mine face comprising mineral ore martite and non-mineralised shale.The combined system is shown to produce a superior map to its constituent algo-rithms,and the consistency of its mapping capability is demonstrated using data acquired at two differ-ent times of day.