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Impact-Aware Online Motion Planning for Fully-Actuated Bipedal Robot Walking

Yuan Gao, Xingye Da, Yan Gu

Year
2020
Citations
5

Abstract

The ability to track a general walking path with specific timing is crucial to the operational safety and reliability of bipedal robots for avoiding dynamic obstacles, such as pedestrians, in complex environments. This paper introduces a motion planner that generates the desired full-body motion in real-time for fully-actuated bipedal robotic walking. The key novelty of the proposed planner lies in its capability of online generation of impact-aware non-periodic motions along general walking paths that respect the discrete impact behaviors. The planner is derived based on full-order modeling of hybrid walking dynamics and the proposed construction of keyframe posture library. Simulation results of a three-dimensional bipedal robot are presented to confirm the effectiveness of the proposed planner in generating full-body motions for each walking step within 0.6 second.

Keywords

PlannerRobotComputer scienceMotion planningMotion (physics)NoveltySimulationKey (lock)Artificial intelligenceControl engineering

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