Qualitative constraints for human-aware robot navigation using Velocity Costmaps
Qualitative constraints for human-aware robot navigation using Velocity Costmaps
复制标题
使用速度成本图的人类感知机器人导航的定性约束
DOI：
10.1109/roman.2016.7745177
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Marc Hanheide
中科院分区：
文献类型：
--
作者：
C. Dondrup;Marc Hanheide
在这项工作中，我们提出了与所谓的速度Costmaps的最先进的当地计划者的结合，以实现人类意识的机器人导航。环境，我们将“动态窗口方法”的样本空间限制在本地规划师中，仅允许基于对遇到的未来展开的定性描述来实现这一目标。基于定性轨迹计算的临时模型代表人类和机器人的相互导航意图，并将这些描述符转换为轨迹生成的样本空间约束。使用非全面移动机器人的模拟和现实世界中的实验表明，我们的方法超过了高斯模型的性能和安全性通过和路径穿越情况。
In this work, we propose the combination of a state-of-the-art sampling-based local planner with so-called Velocity Costmaps to achieve human-aware robot navigation. Instead of introducing humans as “special obstacles” into the representation of the environment, we restrict the sample space of a “Dynamic Window Approach” local planner to only allow trajectories based on a qualitative description of the future unfolding of the encounter. To achieve this, we use a Bayesian temporal model based on a Qualitative Trajectory Calculus to represent the mutual navigation intent of human and robot, and translate these descriptors into sample space constraints for trajectory generation. We show how to learn these models from demonstration and evaluate our approach against standard Gaussian cost models in simulation and in real-world using a non-holonomic mobile robot. Our experiments show that our approach exceeds the performance and safety of the Gaussian models in pass-by and path crossing situations.