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C-space Entropy: A Measure for View Planning and Exploration for General Robot-Sensor Systems in Unknown Environments

机译:C空间熵:未知环境下通用机器人传感器系统的视图规划和探索措施

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摘要

We consider the view planning problem where a range sensor is mounted on a robot mechanism with non-trivial geometry and kinematics. The robot-sensor system is required to explore the environment for obstacles and free space. We present an information theoretical approach in which the sensing action is viewed as reducing ignorance of the planning space, the C-space of the robot. The concept of C-space entropy is introduced as a measure of this ignorance. The next view in the planning process is so chosen that it maximizes the expected reduction of C-space entropy, called the maximal (expected) entropy reduction (MER) criterion. We derive closed-form expressions for expected entropy reduction for an idealized point field-of-view sensor under a Poisson point process model of the environment. We show via simulations and real experiments that the MER criterion is significantly more efficient in sensor-based path planning and exploration tasks than other purely physical space based criteria previously used in the literature.
机译:我们考虑视图规划问题,其中将范围传感器安装在具有非平凡几何和运动学特性的机器人机构上。需要机器人传感器系统来探索环境中的障碍物和自由空间。我们提出了一种信息理论方法,其中的传感动作被视为减少了机器人C空间的规划空间的无知。引入了C空间熵的概念来衡量这种无知。这样选择计划过程中的下一个视图,以使其最大化C空间熵的预期减少量,称为最大(预期)熵减少(MER)标准。我们在环境的泊松点过程模型下,为理想的点视场传感器得出了预期的熵降低的闭式表达式。我们通过仿真和实际实验表明,MER准则在基于传感器的路径规划和探索任务中比以前在文献中使用的其他基于纯粹物理空间的准则要有效得多。

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