In this paper, we present a novel control architecture for the online adaptation of bipedal locomotion on inclined obstacles. In particular, we introduce a novel, cost-effective, and versatile foot sensor to detect the proximity of the robot's feet to the ground (bump sensor). By employing this sensor, feedback controllers are implemented to reduce the impact forces during the transition of the swing to stance phase or steeping on inclined unseen obstacles. Compared to conventional sensors based on contact reaction force, this sensor detects the distance to the ground or obstacles before the foot touches the obstacle and therefore provides predictive information to anticipate the obstacles. The controller of the proposed bump sensor interacts with another admittance controller to adjust leg length. The walking experiments show successful locomotion on the unseen inclined obstacle without reducing the locomotion speed with a slope angle of 12. Foot position error causes a hard impact with the ground as a consequence of accumulative error caused by links and connections' deflection (which is manufactured by university tools). The proposed framework drastically reduces the feet' impact with the ground.

步行运动计划基于运动的不同组成部分（DCM）和线性倒置模型（LIPM）是可以实现的替代方案之一，以生成在线人类人体机器人步态轨迹。该算法需要调整不同的参数。在此，我们开发了一个框架来获得最佳参数，以实现Real Robot步态的稳定且节能的轨迹。为了找到最佳轨迹，在机器人的每个下肢关节下，代表能耗的四个成本函数，关节速度和应用扭矩的总和，以及基于零矩（ZMP）稳定性标准的成本函数。遗传算法用于框架中，以优化这些成本函数中的每一个。尽管轨迹计划是在简化模型的帮助下完成的，但通过考虑Bullet Physics Engine Simulator中的完整动力学模型和脚部接触模型，可以获得每个成本函数的值。这种优化的结果是，以最有效的方式行走的最稳定性和行走是相互对比的。因此，在另一次尝试中，对ZMP和以三种不同速度的能量成本函数进行了多目标优化。最后，我们比较了使用最佳参数生成的设计轨迹，并将模拟产生的仿真模拟器。