Automation of Basic Forestry Operations

林业基本作业自动化

• 批准号: 538321-2018
• 负责人: Giguère, Philippe
• 金额: $ 4.47万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 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=699465
• 关键词: Automation; Basic; Forestry; Operations

The Canadian forest products industry can greatly benefit from recent advances in automation to alleviate issues such as productivity, labour shortage, safety, and repetitive injuries. In this proposal, we aim at demonstrating the automation of basic logging operations, by leveraging and building upon the current state-of-the-art in mobile robotics and machine learning. To this effect, we have selected key elements in forestry operation, namely harvesting and forwarding as the most susceptible to benefit rapidly and realistically from the current breakthroughs in these research areas. Our proposal aim at 1) building useful 3D maps online from onboard vehicles sensors with a LiDAR and an inertial measurement unit and 2) develop and test a number of algorithms for autonomous tree harvesting and forwarding in simulation (harvesting) and with a mock-up (forwarding). Our proposal will also make fundamental scientific contributions, notably in the area of 3D mapping, 3D perception and life-long learning. Indeed, the forest environment poses interesting challenges, with the prevalence of slopes and hills, significant occlusion by foliage, and high changes of scene appearances due to seasonal effects (foliage, snow). This proposal will contribute to the effort of FORAC 4.0 research program, a research consortium aimed at improving the competitiveness of the Canadian forest industry. The research will be conducted in direct partnership with leaders of the Canadian forest industry. Our proposal will provide a unique training experience to form the next generation of roboticists and data scientists.

加拿大林产品行业可以从自动化的最新进展中受益匪浅，以缓解生产力，劳动力短缺，安全和重复性伤害等问题。在本提案中，我们的目标是通过利用和建立在当前最先进的移动的机器人技术和机器学习的基础上，展示基本日志操作的自动化。为此，我们选择了林业经营中的关键要素，即采伐和转运，作为最容易从这些研究领域的当前突破中迅速而现实地受益的要素。我们的建议旨在1）从车载传感器（带有LiDAR和惯性测量单元）在线构建有用的3D地图，2）开发和测试一些算法，用于模拟（收获）和模拟（转发）中的自主树木收获和转发。我们的提案还将做出基本的科学贡献，特别是在3D映射，3D感知和终身学习领域。事实上，森林环境带来了有趣的挑战，山坡和丘陵的普遍存在，树叶的显著遮挡，以及由于季节效应（树叶，雪）而引起的场景外观的高度变化。这一建议将有助于FORAC 4.0研究计划的努力，这是一个旨在提高加拿大林业竞争力的研究联盟。该研究将与加拿大林业领导人直接合作进行。我们的建议将提供独特的培训体验，以形成下一代机器人专家和数据科学家。