Improving the Perception of Autonomous Robotic Systems through Sensing and Machine Learning

通过传感和机器学习改善自主机器人系统的感知

• 批准号: RGPIN-2016-05907
• 负责人: Giguère, Philippe
• 金额: $ 2.77万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710058
• 关键词: Improving; Perception; Autonomous; Robotic; Systems

Recently, the field of robotics has experienced a dramatic surge, both in terms of scientific accomplishments and industrial applications. The automation of numerous tasks has been proposed, such as driving, object manipulation or warehouse operation. To mitigate the difficulties associated with uncertainties or changing conditions, improvements to the perception pipeline are necessary. In this proposal, we will improve it both at the intelligence level and at the sensing level. For the former, we will explore the use of two machine-learning techniques: Domain Adaptation (DA) and Sparse Coding (SC). Domain Adaptation aims at improving performance of a classifier trained on a data set but used on data which is distributed slightly differently. Sparse Coding tries to automate the problem of feature extraction, by finding representations where few components of a potentially long feature vector are active (non-zero). At the sensing level, we propose for example to explore the use of custom-made hyperspectral cameras. For our short term objectives, we have identified 3 key problems in robotics for which we seek to make significant contributions: visual place recognition, autonomous navigation in forests, and grasping automation. The use of DA in place recognition will improve the detection of images of the same location taken under different illumination or weather conditions. In order to remove shadows in images, better color-constant images can be generated from hyperspectral cameras. For forested environments, we will study, in parallel, the use of this hyperspectral camera and of 3D LiDAR scans for place recognition. On top of that, we will propose ameliorations to the creation process of topometric maps, used for forest navigation purposes. For grasping, we propose a richer representation of a grasping location, called hemicylindrical view. We will also increase the robustness of the 3D sensing by fusing multiple views. Finally, we will perform representation learning with Sparse Coding. Experiments will be conducted on real data or robots. For instance, we will regularly gather datasets in Quebec City, over the four seasons to test our visual place recognition methods. For forest navigation, we will use our Clearpath Robotics Husky A200 robot and its sensor suite in the forests located on Laval University campus. For grasping, we will test our algorithms with real robotic arms. We expect to make significant scientific contributions to robotics, in the form of novel applications of advanced machine learning methods or sensing approaches. For example, we do not believe that anyone has explored the paradigm of Domain Adaptation in the context of place recognition. We also expect that our research results will transfer directly to the industry. Finally, we will train 3 Undergraduates, 2 Masters and 4 PhDs with skills and knowledge that will benefit the Canadian industry.

最近，机器人领域经历了一个戏剧性的激增，无论是在科学成就和工业应用方面。已经提出了许多任务的自动化，例如驾驶、物体操作或仓库操作。为了减轻与不确定性或不断变化的条件相关的困难，有必要改进感知管道。在这个建议中，我们将在智能水平和感知水平上进行改进。对于前者，我们将探索两种机器学习技术的使用：域自适应（DA）和稀疏编码（SC）。域自适应旨在提高在数据集上训练但在分布略有不同的数据上使用的分类器的性能。稀疏编码试图自动化特征提取的问题，通过寻找表示，其中一个潜在的长特征向量的几个组件是活跃的（非零）。例如，在传感层面，我们建议探索使用定制的高光谱相机。 对于我们的短期目标，我们已经确定了机器人技术中的3个关键问题，我们寻求做出重大贡献：视觉位置识别，森林自主导航和抓取自动化。在位置识别中使用DA将改善在不同照明或天气条件下拍摄的相同位置的图像的检测。为了去除图像中的阴影，可以从高光谱相机生成更好的颜色恒定图像。对于森林环境，我们将同时研究使用这种高光谱相机和3D激光雷达扫描进行位置识别。最重要的是，我们将建议改进地形图的创建过程，用于森林导航的目的。对于抓取，我们提出了一个更丰富的表示的抓取位置，称为hemicylindrical视图。我们还将通过融合多个视图来增加3D传感的鲁棒性。最后，我们将使用稀疏编码进行表示学习。 实验将在真实的数据或机器人上进行。例如，我们将定期收集魁北克市四季的数据集，以测试我们的视觉位置识别方法。对于森林导航，我们将在位于拉瓦尔大学校园的森林中使用我们的Clearpath Robotics Husky A200机器人及其传感器套件。对于抓取，我们将用真实的机器人手臂测试我们的算法。 我们希望以先进机器学习方法或传感方法的新应用的形式为机器人技术做出重大的科学贡献。例如，我们不相信有人在地方识别的背景下探索了领域适应的范式。我们还希望我们的研究成果能够直接转移到行业中。最后，我们将培养3名本科生，2名硕士和4名博士，他们的技能和知识将使加拿大工业受益。