A Robust Balance-Control Framework for the Terrain-Blind Bipedal Walking of a Humanoid Robot on Unknown and Uneven Terrain

Abstract

Research on a terrain-blind walking control that can walk stably on unknown and uneven terrain is an important research field for humanoid robots to achieve human-level walking abilities, and it is still a field that needs much improvement. This paper describes the design, implementation, and experimental results of a robust balance-control framework for the stable walking of a humanoid robot on unknown and uneven terrain. For robust balance-control against disturbances caused by uneven terrain, we propose a framework that combines a capture-point controller that modifies the control reference, and a balance controller that follows its control references in a cascading structure. The capture-point controller adjusts a zero-moment point reference to stabilize the perturbed capture-point from the disturbance, and the adjusted zero-moment point reference is utilized as a control reference for the balance controller, comprised of zero-moment point, leg length, and foot orientation controllers. By adjusting the zero-moment point reference according to the disturbance, our zero-moment point controller guarantees robust zero-moment point control performance in uneven terrain, unlike previous zero-moment point controllers. In addition, for fast posture stabilization in uneven terrain, we applied a proportional-derivative admittance controller to the leg length and foot orientation controllers to rapidly adapt these parts of the robot to uneven terrain without vibration. Furthermore, to activate position or force control depending on the gait phase of a robot, we applied gain scheduling to the leg length and foot orientation controllers, which simplifies their implementation. The effectiveness of the proposed control framework was verified by stable walking performance on various uneven terrains, such as slopes, stone fields, and lawns.

Improved Active Disturbance Rejection Control for Trajectory Tracking Control of Lower Limb Robotic Rehabilitation Exoskeleton

“…There are several control strategies for rehabilitation, such as position tracking, force and impedance control, biosignals based control and adaptive control, etc [ 42 , 43 , 44 , 45 , 46 , 47 ]. Position tracking is one of the basic control strategies for robotic rehabilitation devices in which repeatability and position accuracy of motion are improved by the help of the controller for the patient’s recovery [ 3 , 39 , 48 , 49 , 50 ].…”

Real-Time FPGA-Based Balance Control Method for a Humanoid Robot Pushed by External Forces

“…However, because to an oversimplified dynamic model would result in estimation errors, an appropriate approximation model is needed to guarantee steady walking gaits. A linear inverted pendulum model [8,9] was proposed, and developed a preview control which is integrated ZMP to modify for errors caused by the simplified model [10,11]. However, the aforementioned methods cannot be applied to many small-sized humanoid robots that have limited computing capacities.…”

Virtual Point Control for Step-Down Perturbations and Downhill Slopes in Bipedal Running

“…In the presence of an inclined terrain, many humanoid robots employ an ankle strategy, where the ankle pitch angle is adjusted based on the torso pitch angle feedback to prevent the robot from tilting [e.g., Nao (Ding et al, 2018), KHR-2 (Kim et al, 2007), SCUT-I (Sheng et al, 2013)]. Subsequent studies enhance the postural stability by increasing complexity in control: the biped robot SUBO-I adjusts its center of mass height by using a disturbance observer (Cho and Kim, 2018), Spring Flamingo adjusts the desired hip height of its virtual model controller (Chew et al, 1999), LOLA adapts its center of mass height based on contact force feedback (Sygulla and Rixen, 2020), and DRB-HUBO adapts its foot orientation (Joe and Oh, 2019) to traverse sloped surfaces and steps. On the other hand, some robots have vision-based perception, and therefore have extended capabilities to estimate the terrain and react to the changes (Fallon et al, 2015).…”