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Whole-Body End-Pose Planning for Legged Robots on Inclined Support Surfaces in Complex Environments

机译:在复杂环境中倾斜支持表面上的腿机器人的全身终端姿态规划

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Planning balanced whole-body reaching configurations is a fundamental problem in humanoid robotics on which manipulation and locomotion planners depend on. While finding valid whole-body configurations in free space and on flat terrains is relatively straightforward, the problem becomes extremely challenging when obstacle avoidance is taken into account, and when balancing on more complex terrains, such as inclined supports or steps. Previous work using Paired Forward-Inverse Dynamic Reachability Maps demonstrated fast end-pose planning on flat terrains at different heights by decomposing the kinematic structure and leveraging combinatorics. In this paper, we present an efficient whole-body end-pose planning framework capable of finding collision-free whole-body configurations in complex environments and on sloped support regions. The main contributions in this paper are twofold: (i) the integration of contact property information of support regions into both precomputation and online planning stages, including whole-body static equilibrium robustness, and (ii) the proposal of a more informed and meaningful sampling strategy for the lower-body. We focus on humanoid robots throughout the paper, but all the principles can be applied to legged platforms other than bipedal robots. We demonstrate our method on the NASA Valkyrie humanoid platform with 38 Degrees of Freedom (DoF) over inclined supports. Analysis of the results indicate both higher success rates - greater than 95 % and 80 % on obstacle-free and highly cluttered environments, respectively - and shorter computation times compared to previous methods.
机译:规划平衡的全身到达配置是人形机器人的一个基本问题,操纵和运动策划者依赖于此。在发现自由空间和平坦地带的有效全身配置的同时,当考虑障碍物的避免时,问题变得极其挑战,并且在更复杂的地形上平衡,例如倾斜的支撑或步骤。以前的工作使用配对前逆动态可达性地图通过分解运动结构并利用组合物来展示不同高度的平坦地带的快速终端姿态规划。在本文中,我们提出了一种高效的全身终端姿态规划框架,能够在复杂的环境和倾斜的支撑区域上找到无碰撞的全身配置。本文的主要贡献是双重的:(i)将支持地区的联系物业信息与全身静态均衡稳健性(包括全身静态均衡稳健性)的整合,以及(ii)提出更明智和有意义的采样的提议下半身的策略。我们专注于整个论文的人形机器人,但所有原则都可以应用于除了双模型机器人以外的腿部平台。我们在NASA Valkyrie人形平台上展示了​​我们的方法,以38度自由(DOF)在倾斜的支持下。与先前的方法相比,结果分析较高的成功率 - 大于95%和80%,分别较短的计算时间和更短的计算次数。

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