This paper presents a whole-body robot control method for exploring and probing a given region of interest. The ergodic control formalism behind such an exploration behavior consists of matching the time-averaged statistics of a robot trajectory with the spatial statistics of the target distribution. Most existing ergodic control approaches assume the robots/sensors as individual point agents moving in space. We introduce an approach that decomposes the whole-body of a robotic manipulator into multiple kinematically constrained agents. Then, we generate control actions by calculating a consensus among the agents. To do so, we use an ergodic control formulation called heat equation-driven area coverage (HEDAC) and slow the diffusion using the non-stationary heat equation. Our approach extends HEDAC to applications where robots have multiple sensors on the whole-body (such as tactile skin) and use all sensors to optimally explore the given region. We show that our approach increases the exploration performance in terms of ergodicity and scales well to real-world problems. We compare our method in kinematic simulations with the state-of-the-art and demonstrate the applicability of an online exploration task with a 7-axis Franka Emika robot. Additional material available at https://sites.google.com/view/w-ee-d/

翻译：本文提出了一种用于探索和探测给定感兴趣区域的全身机器人控制方法。这种探索行为背后的遍历控制框架旨在使机器人轨迹的时间平均统计量与目标分布的空间统计量相匹配。现有的遍历控制方法大多将机器人/传感器视为在空间中移动的单个点状代理。我们提出了一种方法，将机器人操作器的全身分解为多个运动学约束的代理，然后通过计算代理之间的共识来生成控制动作。为此，我们采用了一种名为热方程驱动区域覆盖（HEDAC）的遍历控制公式，并利用非平稳热方程减缓扩散过程。我们的方法将HEDAC扩展至机器人全身配备多个传感器（如触觉皮肤）的应用场景，并利用所有传感器优化探索给定区域。研究表明，该方法在遍历性指标上提升了探索性能，并能良好地扩展至实际问题。我们通过运动学仿真与现有最先进方法进行了对比，并在7轴Franka Emika机器人上展示了在线探索任务的可行性。补充资料请访问：https://sites.google.com/view/w-ee-d/