Autonomous speed adaptation by a muscle-driven hind leg robot modeled on a cat without intervention from brain

This study aims to design a nervous system model to drive the realistic muscle-driven legs for the locomotion of a quadruped robot. We evaluate our proposed nervous system model with a hind leg simulated model and robot. We apply a two-level central pattern generator for each leg, which generates locomotion rhythms and reproduces cat-like leg trajectories by driving different sets of the muscles at any timing during one cycle of moving the leg. The central pattern generator receives sensory feedback from leg loading. A cat simulated model and a robot with two hind legs, each with three joints driven by six muscle models, are controlled by our nervous system model. Even though their hind legs are forced backward at a wide range of speeds, they can adapt to the speed variation by autonomously adjusting its stride and cyclic duration without changing any parameters or receiving any descending inputs. In addition to the autonomous speed adaptation, the cat hind leg robot switched from a trot-like gait to a gallop-like gait while speeding up. These features can be observed in existing animal locomotion tests. These results demonstrate that our nervous system is useful as a valid and practical legged locomotion controller.

Reinforcement learning-based optimization of locomotion controller using multiple coupled CPG oscillators for elongated undulating fin propulsion

<abstract> <p>This article proposes a locomotion controller inspired by black Knifefish for undulating elongated fin robot. The proposed controller is built by a modified CPG network using sixteen coupled Hopf oscillators with the feedback of the angle of each fin-ray. The convergence rate of the modified CPG network is optimized by a reinforcement learning algorithm. By employing the proposed controller, the undulating elongated fin robot can realize swimming pattern transformations naturally. Additionally, the proposed controller enables the configuration of the swimming pattern parameters known as the amplitude envelope, the oscillatory frequency to perform various swimming patterns. The implementation processing of the reinforcement learning-based optimization is discussed. The simulation and experimental results show the capability and effectiveness of the proposed controller through the performance of several swimming patterns in the varying oscillatory frequency and the amplitude envelope of each fin-ray.</p> </abstract>

Kit Based Motion Generator for a Soft Walking Robot

In order to control high-level goals such as walking speed and direction or position of legged robots, a locomotion controller is required. This complicated task can be solved in many different ways. The approach presented here selects the optimal gait pattern from a discrete, predefined set of possibilities to get closer to a given target position. The method is based on an off-line component: elementary gait patterns are generated by trajectory optimization using a simulation model, and an on-line component: for given robot and target positions the optimal next elementary gait pattern is chosen based on a minimization problem, and the joint space references are derived from it. To ensure feasible subsequent poses, the elementary patterns always begin and end with one and the same pose, so that they can be placed on top of each other like Lego bricks. A great advantage of this method is a straightforward transition between different motion modes, such as switching from trotting to crawling. It is discussed how many different elementary patterns are needed to ensure a stable locomotion control. Finally, in simulation and experiment, it is shown that the robot can master any obstacle course using the proposed locomotion controller.