Center for Applied Autonomous Sensor Systems (AASS), Department of Technology, Örebro University, SE-701 82 Örebro, Sweden.
Technical University Munich (TUM), 80333 Munich, Germany.
Sensors (Basel). 2024 May 22;24(11):3303. doi: 10.3390/s24113303.
Interactions between mobile robots and human operators in common areas require a high level of safety, especially in terms of trajectory planning, obstacle avoidance and mutual cooperation. In this connection, the crossings of planned trajectories and their uncertainty based on model fluctuations, system noise and sensor noise play an outstanding role. This paper discusses the calculation of the expected areas of interactions during human-robot navigation with respect to fuzzy and noisy information. The expected crossing points of the possible trajectories are nonlinearly associated with the positions and orientations of the robots and humans. The nonlinear transformation of a noisy system input, such as the directions of the motion of humans and robots, to a system output, the expected area of intersection of their trajectories, is performed by two methods: statistical linearization and the sigma-point transformation. For both approaches, fuzzy approximations are presented and the inverse problem is discussed where the input distribution parameters are computed from the given output distribution parameters.
在公共区域中，移动机器人和人类操作人员之间的交互需要高度的安全性，特别是在轨迹规划、障碍物回避和相互合作方面。在这方面，基于模型波动、系统噪声和传感器噪声的规划轨迹的交叉及其不确定性起着突出的作用。本文讨论了在考虑模糊和噪声信息的情况下，人类机器人导航过程中交互作用的预期区域的计算。可能轨迹的预期交叉点与机器人和人类的位置和方向非线性相关。将噪声系统输入（例如人类和机器人的运动方向）非线性转换为系统输出（其轨迹的预期交点区域）的过程是通过两种方法完成的：统计线性化和 sigma 点变换。对于这两种方法，都提出了模糊逼近，并讨论了从给定的输出分布参数计算输入分布参数的逆问题。
