Motion Planning for Manipulators in Unknown Environments with Contact Sensing Uncertainty
Motion Planning for Manipulators in Unknown Environments with Contact Sensing Uncertainty
复制标题
具有接触传感不确定性的未知环境中机械臂的运动规划
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
D. Berenson
中科院分区：
文献类型：
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作者：
Brad Saund;D. Berenson
定位误差、传感器噪声和遮挡可能会导致环境模型不完美，从而导致机器人手臂在操作时与未观察到的障碍物发生碰撞。机器人必须绕过这些障碍物，尽管不知道它们的形状或位置。没有触觉传感器，机器人只能观察到接触发生在其表面的某个地方，这种测量包含的信息非常少。我们提出了碰撞假设集表示计算的信念，从这些观察占用，我们引入了一个规划和控制架构，使用这种表示，通过未知的环境中导航。尽管缺乏信息，但我们通过实验证明，我们的算法可以绕过看不见的障碍物，进入狭窄的通道。我们在模拟的多个环境中以及在物理机器人手臂上进行了测试，无论是否有2.5D深度传感器的帮助。与基线表示相比，碰撞假设集产生大约1.5 - 3倍的加速，并在狭窄通道的测试场景中将成功率从40 - 60%提高到100%。
Localization error, sensor noise, and occlusions can lead to an imperfect model of the environment, which can result in collisions between a robot arm and unobserved obstacles when manipulating. The robot must navigate around these obstructions despite not knowing their shape or location. Without tactile sensors, the robot only observes that a contact occurred somewhere on its surface, a measurement containing very little information. We present the Collision Hypothesis Sets representation for computing a belief of occupancy from these observations, and we introduce a planning and control architecture that uses this representation to navigate through unknown environments. Despite the dearth of information, we demonstrate through experiments that our algorithms can navigate around unseen obstacles and into narrow passages. We test in multiple environments in simulation and on a physical robot arm both with and without the aid of a 2.5D depth sensor. Compared to a baseline representation Collision Hypothesis Sets produce an approximately 1.5-3x speed-up and improve the success rate from 40–60% to 100% in tested scenarios with narrow passages.