本論文基於粒子群最佳化法(Particle Swarm Optimization, PSO)規劃最佳的踏步位置,讓人形機器人可在高低落差之地面行走。在視覺系統中,本論文使用RGB-D 攝影機來取得色彩資訊以及深度資訊,透過影像前處理提取所需之地面資訊,也可以降低計算量,辨識前方木板之位置,確認人形機器人適合的踏步範圍。在踏步規劃中,透過粒子群最佳化法與木板高度,在適合的踏步範圍內搜尋人形機器人之下一步最佳踏步位置,接著立即修改人形機器人下一步的踏步軌跡,來達成人形機器人在高低落差之地面行走。最後本論文針對更新踏步位置後的雙足行走加入平衡控制,增加人形機器人在踏步時的穩定。在實驗結果中,本論文提出基於粒子群最佳化法之人形機器人最佳踏步位置規劃,使人形機器人能在高低落差之地面行走,其有效提升即時行走規劃的可行性,且加入平衡控制讓人形機器人可以穩定前進。
In this thesis, optimizing step position is planned based on Particle Swarm Optimization (PSO), so that the humanoid robot can walk on the high-low ground. In the vision system, this paper uses RGB-D cameras to obtain color information and depth information, and extracts the required ground information through image pre-processing, which can also reduce the amount of calculation, identify the position of the front board, and confirm the feasible stepping region of the humanoid robot. In the footstep planning, through the particle swarm optimization method and the height of the board, search for the next best footstep position of the humanoid robot within the feasible stepping region, and then immediately modify the next step trajectory of the humanoid robot to achieve the humanoid robot walking on the ground with high-low drop. Finally, this thesis increases balance control to the bipedal walking after updating the stepping position to increase the stability of the humanoid robot when stepping. In the experimental results, this thesis proposes the optimal step position planning of humanoid robot based on particle swarm optimization method, so that the humanoid robot can walk on the ground with high and low drop, this method can effectively improve the feasibility of real-time walking planning. And adding balance control makes the humanoid robot to walk stably.