Humanoid navigation in uneven terrain using learned estimates of traversability
Humanoid navigation in uneven terrain using learned estimates of traversability
复制标题
使用学习的可通行性估计在不平坦的地形中进行人形导航
DOI：
10.1109/humanoids.2017.8239531
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发表时间：
2017
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：
--
作者：
Yu;D. Berenson
在本文中，我们探讨了离散搜索为基础的接触空间规划的人形机器人在复杂的非结构化环境中使用手掌和脚接触。在高分支因子和稀疏可接触区域的情况下，规划器在这样的环境中快速找到接触序列是具有挑战性的。因此，我们建议学习一个预测可遍历性的函数-测量接触空间规划器可以多快地生成接触序列以遍历某个区域。通过在接触空间规划器的启发式函数中加入学习到的可遍历性估计，我们可以使规划器偏向于搜索具有更多可接触区域的区域，从而更有效地找到接触序列。在本文中，我们提出并评估两种特征向量估计遍历性：精确接触检查（ECC）和近似接触检查（ACC），这使得不同的速度和准确性之间的权衡。实验结果表明，该方法在接触空间规划中的性能优于ECC和基线启发式算法;在地形不平坦的困难环境中，与基线启发式算法相比，ACC算法的规划成功率提高了19%，平均规划时间缩短了24%.
In this paper we explore discrete search-based contact space planning for humanoids using both palm and foot contact in complex unstructured environments. With a high branching factor and sparse contactable regions, it is challenging for the planner to find a contact sequence in such environments quickly. Therefore, we propose to learn a function which predicts traversability — a measure of how quickly the contact space planner can generate contact sequences to traverse a certain region. By including a learned traversability estimate into the heuristic function of the contact space planner, we can bias the planner to search the areas with more contactable regions, and thus find contact sequences more efficiently. In this paper we propose and evaluate two kinds of feature vectors for estimating traversability: Exact Contact Checking (ECC) and Approximate Contact Checking (ACC), which make different trade-offs between speed and accuracy. The experimental results show that the proposed approach using ACC outperforms both ECC and the baseline heuristic for contact space planning; ACC increases the planning success rate by 19% and reduces average planning time by 24% compared to the baseline in difficult environments with uneven terrain.