5 accepted papers
Legged robots are supposed to traverse complicated environments, which makes it challenging to design a model-based controller due to their functional complexity. Currently, using deep reinforcement learning to improve the adaptability of robots in complex scenarios has been a major research trend.
We present GOEN, an advanced navigation and path planning framework specifically engineered to tackle the complexities of dynamic and unstructured environments through real-time 3D pointcloud processing. Our approach integrates pointcloud downsampling, collision risk assessment, and obstacle endpoin
This letter presents a control framework for legged robots that enables self-perception and resistance to external disturbances. First, a novel proprioceptive-based disturbance estimator is proposed. Compared with other disturbance estimators, this estimator possesses notable advantages in terms of
A novel legged robot with 6 limbs driven by 20 proprioceptive motors, named SDUHex, is proposed in this letter. The limbs located at the middle of the robot can work as manipulators or locomotors. The topology design of the multi-mode robot is introduced and the kinematics and dynamics of the robot
A method for planning and controlling the somersault motion of a quadruped robot is proposed in this paper. The method divides the somersault motion into 5 stages according to intuitive understanding. Based on the simplified dynamic model, the linear programming method is used to obtain the maximum…