本論文利用安裝在機器人雙足上壓力感測器，量測步行時零力矩點變化，規劃動態閾值，並結合虛擬力線性倒單擺模型，使重心軌跡可順應外力變化，讓機器人受到外力衝擊時，即時產生合適的步態。控制過程中可傾斜身體、壓低重心以及調整步距等參數以維持平衡。一般雙足機器人是透過理想的零力矩點，規劃重心的移動軌跡，並以漸開線(或擺線)的方式計算踏腳點，以離線的方式產生步態。因此當機器人受到未知的外力衝擊時，持續固定的重心移動將導致機器人傾倒。為了即時偵測機器人的平衡狀況，本論文設計並研製新一代的腳底板，每個腳底板上安裝了四個高精準度的荷重元壓力感測器，透過放大器與濾波器，量測零力矩點實際與理想數值的誤差與誤差變化量，使機器人可在單腳支撐期瞬間切換步態，同時計算下一個步態。除此之外，本論文更整合加速度計與模糊控制器，提出模糊動態步態產生器，讓機器人能夠透過零力矩點回授，即時產生合適步態削弱外力撞擊。最後，實驗結果顯示，透過本論文所提方法，能讓機器人在踏步時，受到外力衝擊的位能變化量達2.37焦耳，相較原地站姿更提升約1.3倍。

Fuzzy dynamic gait pattern generator, which allows the teen-sized humanoid robot to real-time generate a suitable gait pattern when it is hit by an unexpected force, is proposed in this thesis. Conventional gait pattern generators usually utilize the ideal Zero Moment Point (ZMP) to plan the trajectory of the Center of Mass (CoM) along with the cycloid to generate footsteps. However, the prior planned gait pattern cannot deal with unexpected situations, especially when the robot suffers an unknown force. Therefore, we propose a dynamic gait pattern generator that leverages virtual force Linear Inverted Pendulum Model (LIPM) to adjust the trajectory of the CoM and detects the balance status by estimating the trajectories of ZMP through eight high-precision load cell pressure sensors mounted on the robot’s soles. Besides, we integrate an accelerometer with the pressure sensors by the fuzzy controller to immediately generate a suitable gait pattern. When the robot is pushed suddenly, it adapts a pre-planned gait pattern to replace the current gait. At the same time, the fuzzy controller calculates next gaits with appropriate strides and lean angle to absorb the impact. All the experimental results demonstrate that the humanoid robot, David Junior II, can endure a hit with the potential energy of 2.3721 J during walking, which is 1.3 times more robust than the status of standing.

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