The main advantages of legged robots over wheeled ones are their abilities to traverse on uneven terrain due to the use of intermittent contacts and an ability to shift the center of mass relative to the contact location. A robot's leg design can be implemented by using an open-chain mechanism actuated with high-density torque actuators though this solution needs a vast energy budget. An alternative way to design a leg mechanism is the application of morphological computation principle. According to the principle, most of the desired robot's behavior can be delegated to the mechanics with minimum control effort needed to excite, stabilize or augment it. Within this paper, we have proposed a method to synthesize a leg for hopping robots. Due to optimization of mechanical structure, geometric parameters, mass distribution, and elasticity allocation, our method allows getting an energy-efficient robot with minimal control system complexity, which is accomplished via series elastic allocation and active variable length link. Based on this approach, we have designed a hopping robot with two low performance actuators that can achieve hopping, running, and, in the case of a biped or quadruped robot, walking motion. The paper describes a synthesized leg linkage and overviews prototype design, control strategy, and test results of a physical prototype.

Designing robots is a multiphase process aimed at solving a multi-criteria optimization problem to find the best possible detailed design. Generative design (GD) aims to accelerate the design process compared to manual design, since GD allows exploring and exploiting the vast design space more efficiently. In the field of robotics, however, relevant research focuses mostly on the generation of fully-actuated open chain kinematics, which is trivial in mechanical engineering perspective. Within this paper, we address the problem of generative design of closed-chain linkage mechanisms. A GD algorithm has to be able to generate meaningful mechanisms which satisfy conditions of existence. We propose an optimization-driven algorithm for generation of planar closed-chain linkages to follow a predefined trajectory. The algorithm creates an unlimited range of physically reproducible design alternatives that can be further tested in simulation. These tests could be done in order to find solutions that satisfy extra criteria, e.g., desired dynamic behavior or low energy consumption. The proposed algorithm is called “respawn” since it builds a new linkage after the ancestor has been tested in a virtual environment in pursuit for the optimal solution. To show that the algorithm is general enough, we show a set of generated linkages that can be used for a wide class of robots.