PiPS: Planning in perception space
PiPS: Planning in perception space
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PiPS：感知空间规划
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
P. Vela
中科院分区：
文献类型：
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作者：
Justin S. Smith;P. Vela
移动机器人的路径规划要求在不确定和变化的环境中快速找到无碰撞的轨迹。完整的碰撞检查与详细的、在线修改的机器人和世界的表示相结合，造成了延迟，从而破坏了被动避障。因此，基于视觉的反应式方法通过各种假设来得到简化的表示，例如可简化为点质量的圆形或球形机器人形状，或者总是从地面升起的障碍物。为了避免这些问题，我们直接在感知空间中对机器人进行建模，以便以最小的处理需求在一致的表示中寻找无碰撞的轨迹。这里，感知空间指的是现代消费者范围传感器可用的深度空间图像测量。我们幻想一个机器人在世界上导航，并合成其路径的深度图像，以便与直接感知到的局部世界的深度图像进行比较。该方法在3D体积中执行碰撞检查，但仅需要2D图像比较。实验表明，该实现能够实时通过由多种对象组成的障碍物路线。
Path planning for mobile robots requires rapidly finding collision-free trajectories in an uncertain and changing environment. Full collision checking with detailed, online-revised representations of the robot and world imposes a delay that undermines reactive obstacle avoidance. As a result, reactive vision-based approaches make various assumptions to arrive at simplified representations, such as circular or spherical robot shapes reducible to point masses, or obstacles that always rise from the ground. We seek to avoid these problems by modeling the robot directly in perception space so that collisionfree trajectories can be sought in a consistent representation with minimal processing needs. Here perception space refers to the depth space image measurements available by modern consumer range sensors. We hallucinate a robot navigating through the world and synthesize depth images of its path for comparison against the directly sensed depth images of the local world. The approach performs collision checking in a 3D volume but only requires 2D image comparisons. Experiments show that an implementation is able to negotiate an obstacle course consisting of miscellaneous objects in real-time.