Previous studies suggest that bipedal robots that have prismatic compliance in the legs can achieve efficient walking. However, how this efficiency can be achieved still remains an open research problem. In this study, we developed a 2-degree-of-freedom planar bipedal robot comprising Neidhart springs and five-bar parallel mechanisms. The five-bar parallel mechanism allows the robot to achieve prismatic compliance. In addition, a lightweight torque limiter function is achieved by Neidhart springs with an original design. We implemented a simple controller and conducted walking experiments. Experimental results showed that the robot walked effectively using the regenerative energy stored in the prismatic springs.

Real-time planning of footsteps has been a big challenge for bipedal research. A large number of methods have been proposed over the last several years. In addition, divergent component of motion (DCM) of linear inverted pendulum (LIP) has been applied to solve this problem thus attracting a great deal of attention. In this paper, we derive an analytical solution of DCM for an arbitrary input function and propose a novel quadratic programming (QP) problem for the simultaneous optimization of zero moment point (ZMP) and foot placements based on the analytical solution. To validate the method, we conducted a push recovery experiment on real hardware. The result of the experiment shows that our new algorithm realizes a hierarchical strategy for disturbance compensation.

Applying humanoid robots in disaster sites requires strong locomotion abilities due to the cluttered and unstructured environment. Besides bipedal walking, it is beneficial to provide functionality to locomote on four limbs, and to control the transitions between bipedal and quadrupedal locomotion. In this paper, we propose a planning algorithm for bipedal and quadrupedal locomotion with intermediate transitions. The algorithm is based on the divergent component of motion for a linear time-variant inverted pendulum model with variable height and a flywheel. The model was validated by model comparisons in a simulation experiment. In addition, the algorithm was validated on a real robot, which realized a sequence of bipedal and quadrupedal locomotion without intermediate stops.