黑暗中推进：一种利用触觉反馈的移动机器人反应式推动策略
Pushing in the Dark: A Reactive Pushing Strategy for Mobile Robots Using Tactile Feedback
AI总结：
本文提出一种基于触觉反馈的反应式推动策略，使移动机器人实时调整基座运动以将物体推至目标位置，在仿真和真实实验中均优于基线方法。
摘要：
对于移动机器人而言，在杂乱或动态环境中导航常常需要非抓取式操作，尤其是在面对过大、不规则或易碎而无法抓取的物体时。这些物体不可预测的行为和变化的物理特性使操作任务显著复杂化。为应对这一挑战，本文提出了一种新颖的反应式推动策略。该策略使移动机器人能够实时动态调整其基座运动，以成功实现朝向目标位置的推动操作。值得注意的是，我们的策略基于覆盖基座的触觉传感器所获得的接触位置变化来调整机器人运动，避免了对物体相关假设及其建模行为的依赖。反应式推动策略的有效性首先在仿真环境中进行了评估，在该环境中其性能显著优于所比较的基线方法。随后，我们通过真实世界实验验证了所提出的策略，展示了机器人将物体推动至其周围整个区域内目标点的能力。在仿真和真实世界实验中，均改变了物体特定属性（形状、质量、摩擦力、惯性）以及目标位置的变化，以全面评估所提出方法的鲁棒性。
英文摘要：
For mobile robots, navigating cluttered or dynamic environments often necessitates non-prehensile manipulation, particularly when faced with objects that are too large, irregular, or fragile to grasp. The unpredictable behavior and varying physical properties of these objects significantly complicate manipulation tasks. To address this challenge, this manuscript proposes a novel Reactive Pushing Strategy. This strategy allows a mobile robot to dynamically adjust its base movements in real-time to achieve successful pushing maneuvers towards a target location. Notably, our strategy adapts the robot motion based on changes in contact location obtained through the tactile sensor covering the base, avoiding dependence on object-related assumptions and its modeled behavior. The effectiveness of the Reactive Pushing Strategy was initially evaluated in the simulation environment, where it significantly outperformed the compared baseline approaches. Following this, we validated the proposed strategy through real-world experiments, demonstrating the robot capability to push objects to the target points located in the entire vicinity of the robot. In both simulation and real-world experiments, the object-specific properties (shape, mass, friction, inertia) were altered along with the changes in target locations to assess the robustness of the proposed method comprehensively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
