Due to the head swinging and the body winding, the self-localization and path following for snake-like robots based on vision are very challenging. In this paper, a novel pantilt compensation method and curve parameter compensation path following controller are proposed to solve these problems, which can achieve high-precision path following. More specifically, to realize real-time positioning of the snakelike robot, a camera-mounted pan-tilt is equipped on the head of the snake-like robot, and the Apritag detection is used after the angle between the Apriltag plane and the camera plane being compensated. Then, due to the modeling approximation, the snake-like robot with a simplified controller usually deviates from the desired path, so a curve parameter compensation path following controller is proposed to eliminate the deviation and improve the following accuracy. Finally, the experiments show that with proposed methods, the snake-like robot can converge to the desired path stably and accurately.

In this paper, a novel navigation learning method in continuous action space among crowds based on relational graph is proposed which can be directly deployed on differential-drive mobile robots without any change. More specifically, in order to increase generalization ability in crowd sizes, Graph Convolutional Network (GCN) is at first adopted to extract the relationships between robot and pedestrians. Then the relation features are further utilized as the inputs of the pedestrian state prediction network, the actor network, and the critic network. To efficiently and safely learn the navigation policy, all networks are pretrained through imitating ORCA which is a state-of-the-art algorithm in crowd navigation, and then a model-based reinforcement learning (RL) method which combines the model prediction and the clipped advantage-weighted regression is proposed to finetune the networks. Finally, simulation experiments are performed and it’s verified that the proposed learning method performs significantly better than ORCA and the other state-of-the-art RL methods.