Learning off-road driving from simulation

通过模拟学习越野驾驶

• 批准号: 544098-2019
• 负责人: Meger, David
• 金额: $ 1.82万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=680843
• 关键词: Learning; off; road; driving; simulation

Driving vehicles off-road in Canadian terrain is a skill of many of our forestry, construction and mining workforce, but it also poses great risk to human and equipment safety due to sliding, skidding and roll-over. CM Labs produces world-leading training and simulation products that allow operators of heavy equipment to face such situations in the safety of a game-like software experience, without personal risk, however, many of the most challenging environments and rapid motions cannot yet be captured by our state-of-the-art models. In collaboration with McGill's Mobile Robotics Lab, this project will leverage recent advances in Reinforcement Learning to produce new off-road driving policies and further improve CM Labs' tire-terrain dynamics models. A key aspect of this work is the ability of a intelligent software to explore driving possibilities only in simulation, for which we will rely on integration of CM Lab's Vortex Studio with the most recent advances in AI. Findings in this approximate domain will then be transferred to small and agile outdoor vehicles built recently at McGill, to validate the models and find differences between our simulators and reality. The results will improve outdoor automation in Canadian spaces, and the ability of CM Labs to train our workforce to safely conduct operations even in Canada's most challenging environments.

在加拿大的地形中驾驶车辆越野是我们许多林业、建筑和采矿工人的技能，但由于滑动、打滑和翻车，这也给人员和设备安全带来了巨大风险。CM Labs生产世界领先的培训和模拟产品，使重型设备的操作员能够在游戏般的软件体验中安全地面对这种情况，而没有个人风险，然而，许多最具挑战性的环境和快速动作还不能被我们最先进的模型捕获。通过与麦吉尔的移动的机器人实验室合作，该项目将利用强化学习的最新进展来制定新的越野驾驶策略，并进一步改进CM Labs的轮胎-地形动力学模型。这项工作的一个关键方面是智能软件仅在模拟中探索驾驶可能性的能力，我们将依靠CM Lab的Vortex Studio与AI最新进展的集成。在这个近似领域的研究结果将被转移到最近在麦吉尔建造的小型敏捷户外车辆上，以验证模型并找到我们的模拟器与现实之间的差异。这些成果将改善加拿大空间的户外自动化，以及CM Labs培训员工在加拿大最具挑战性的环境中安全运营的能力。