论文标题
使用步态的列举编码：无梯度启发式方法，迈向六脚架步态适应
Towards Hexapod Gait Adaptation using Enumerative Encoding of Gaits: Gradient-Free Heuristics
论文作者
论文摘要
希望有效适应多条机器人系统对变化条件的有效适应，这将使对机器人控制和运动的新见解。在本文中，我们研究了赫克萨波德步态的枚举（阶乘）编码的性能前沿，以快速恢复到腿部失败的条件。我们使用五个自然启发的无梯度优化启发式方法的计算研究表明，可以实现可行的恢复步态策略，从而实现对所需的运动指令的最小偏差，并进行一些评估（试验）。例如，可以生成可行的恢复步态策略，达到2.5厘米。 （10厘米）平均相对于指挥方向进行40-60（20）评估/试验的偏差。我们的结果可能是有效地适应新条件，并进一步探索机器人运动问题适应的规范表示。
The quest for the efficient adaptation of multilegged robotic systems to changing conditions is expected to render new insights into robotic control and locomotion. In this paper, we study the performance frontiers of the enumerative (factorial) encoding of hexapod gaits for fast recovery to conditions of leg failures. Our computational studies using five nature-inspired gradient-free optimization heuristics have shown that it is possible to render feasible recovery gait strategies that achieve minimal deviation to desired locomotion directives with a few evaluations (trials). For instance, it is possible to generate viable recovery gait strategies reaching 2.5 cm. (10 cm.) deviation on average with respect to a commanded direction with 40 - 60 (20) evaluations/trials. Our results are the potential to enable efficient adaptation to new conditions and to explore further the canonical representations for adaptation in robotic locomotion problems.
