亚北极环境下滑橇转向运动学模型的评价
Evaluation of Skid-Steering Kinematic Models for Subarctic Environments
摘要：
在亚北极和北极地区，大型和重型滑移机器人因其坚固性和在困难地形上操作的能力而受到青睐。这些机器人的状态估计、运动控制和路径规划依赖于基于轮速的精确里程模型。然而，国家的最先进的里程计模型滑移转向移动机器人（SSMRs）通常是在相对轻量级的平台上进行测试。在本文中，我们专注于如何执行这些模型时，部署在一个大而重（590公斤）的SSMR。我们收集了超过2公里的雪和混凝土数据。我们比较理想的差分驱动，扩展差分驱动，曲率半径为基础的，和全线性运动模型通常部署的SSMRs。每个模型都通过在雪和混凝土上搜索其最佳参数来进行微调。然后，我们讨论了参数之间的关系，模型调整，和模型的最终精度。
英文摘要：
In subarctic and arctic areas, large and heavy skid-steered robots are preferred for their robustness and ability to operate on difficult terrain. State estimation, motion control and path planning for these robots rely on accurate odometry models based on wheel velocities. However, the state-of-the-art odometry models for skid-steer mobile robots (SSMRs) have usually been tested on relatively lightweight platforms. In this paper, we focus on how these models perform when deployed on a large and heavy (590 kg) SSMR. We collected more than 2 km of data on both snow and concrete. We compare the ideal differential-drive, extended differential-drive, radius-of-curvature-based, and full linear kinematic models commonly deployed for SSMRs. Each of the models is fine-tuned by searching their optimal parameters on both snow and concrete. We then discuss the relationship between the parameters, the model tuning, and the final accuracy of the models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
