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 exploiting multiple kinematically constrained agents on the whole-body of a robotic manipulator, where a consensus among the agents is found for generating control actions. To do so, we exploit an existing ergodic control formulation called heat equation-driven area coverage (HEDAC), combining local and global exploration on a potential field resulting from heat diffusion. 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 using agents distributed on multiple robot links. We compare our method with HEDAC in kinematic simulation and demonstrate the applicability of an online exploration task with a 7-axis Franka Emika robot.

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。此类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。现有的大多数遍历控制方法将机器人/传感器视为在空间中运动的单个点状智能体。我们提出了一种利用机器人机械臂全身多个运动学约束智能体的方法，通过智能体间的共识生成控制动作。为此，我们利用了名为热方程驱动区域覆盖（HEDAC）的现有遍历控制公式，该公式结合了热扩散产生的势场上的局部与全局探索。我们的方法将HEDAC扩展到机器人在全身具有多个传感器（例如触觉皮肤）的应用场景，并使用所有传感器来最优地探索给定区域。研究表明，我们的方法在遍历性方面提升了探索性能，并能良好地扩展至使用分布在多个机器人连杆上的智能体的实际应用问题。我们在运动学仿真中将我们的方法与HEDAC进行了比较，并展示了使用七轴Franka Emika机器人进行在线探索任务的适用性。