Running into a trap: Numerical design of task-optimal preflex behaviors for delayed disturbance responses.
IROS(2014)
摘要
Legged robots enjoy kilohertz control rates but are still making incremental gains towards becoming as nimble as animals. In contrast, bipedal animals are amazingly robust runners despite lagged state feedback from protracted neuromechanical delays. Based on evidence from biological experiments, we posit that much of disturbance rejection can be offloaded from feedback control and encoded into feed-forward pre-reflexive behaviors called preflexes. We present a framework for the offline numerical generation of preflex behaviors to optimally stabilize legged locomotion tasks in the presence of response delays. By coupling directly collocated trajectory optimizations, we optimize the preflexive motion of a simple bipedal running model to recover from uncertain terrain geometry using minimal actuator work. In simulation, the optimized preflex maneuver showed 30-77% economy improvements over a level-ground strategy when responding to terrain deviating just 2-4cm from the nominal condition. We claim this "preflex-and-replan" framework for designing efficient and robust gaits is amenable to a variety of robots and extensible to arbitrary locomotion tasks.
更多查看译文
关键词
delays,legged locomotion,numerical analysis,optimal control,robust control,state feedback,trajectory control,uncertain systems,arbitrary locomotion tasks,bipedal animals,collocated trajectory optimizations,delayed disturbance responses,disturbance rejection,feedforward pre reflexive behaviors,incremental gains,kilohertz control,lagged state feedback,legged robots,neuromechanical delays,numerical design,numerical generation,optimally stabilize legged locomotion,preflex-and-replan framework,preflexive motion,response delays,robust gaits,task optimal preflex behaviors,uncertain terrain geometry,
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要