学习移动机器人的动态时序图

IF 4.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Junyi Shi , Tomasz Piotr Kucner
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引用次数: 0

摘要

建立周围环境的地图表示对于自主机器人的成功运行至关重要。虽然大量研究都集中在几何结构和静态物体的映射上,但环境也受到动态物体运动的影响。整合环境中的空间运动模式信息有利于规划符合社会要求的轨迹,避开拥挤区域,并与人流保持一致。在本文中,我们介绍了一种深度状态空间模型,旨在学习空间运动模式的地图表示及其在特定位置的时间变化。这样,机器人就能在运动模式不断变化的环境中进行与人类相适应的操作并改进轨迹预测。我们使用两个数据集对所提出的方法进行了验证:一个数据集包含生成的运动模式,另一个数据集则包含真实世界中的行人数据。从学习能力、映射质量及其对下游机器人任务的适用性等方面对模型的性能进行了评估。为了对映射质量进行比较评估,我们将 CLiFF-Map 作为基线,并将 CLiFF-LHMP 作为另一个基线,评估其在下游运动预测任务中的性能。结果表明,我们的模型可以有效地学习相应的运动模式,在机器人任务中具有广阔的应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning temporal maps of dynamics for mobile robots
Building a map representation of the surrounding environment is crucial for the successful operation of autonomous robots. While extensive research has concentrated on mapping geometric structures and static objects, the environment is also influenced by the movement of dynamic objects. Integrating information about spatial motion patterns in an environment can be beneficial for planning socially compliant trajectories, avoiding congested areas, and aligning with the general flow of people. In this paper, we introduce a deep state-space model designed to learn map representations of spatial motion patterns and their temporal changes at specific locations. Thus enabling the robot for human-compliant operation and improved trajectory forecasting in environments with evolving motion patterns. Validation of the proposed method is conducted using two datasets: one comprising generated motion patterns and the other featuring real-world pedestrian data. The model’s performance is assessed in terms of learning capability, mapping quality, and its applicability to downstream robotics tasks. For comparative assessment of mapping quality, we employ CLiFF-Map as a baseline, and CLiFF-LHMP serves as another baseline for evaluating performance in downstream motion prediction tasks. The results demonstrate that our model can effectively learn corresponding motion patterns and holds promising potential for application in robotic tasks.
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来源期刊
Robotics and Autonomous Systems
Robotics and Autonomous Systems 工程技术-机器人学
CiteScore
9.00
自引率
7.00%
发文量
164
审稿时长
4.5 months
期刊介绍: Robotics and Autonomous Systems will carry articles describing fundamental developments in the field of robotics, with special emphasis on autonomous systems. An important goal of this journal is to extend the state of the art in both symbolic and sensory based robot control and learning in the context of autonomous systems. Robotics and Autonomous Systems will carry articles on the theoretical, computational and experimental aspects of autonomous systems, or modules of such systems.
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