Obstacle detection and autonomous stair climbing of a miniature jumping robot

Miniature jumping robots (MJRs) have difficulty executing autonomous movements in unstructured environments with obstacles because of their limited perception and computing resources. This study investigates the obstacle detection and autonomous stair climbing methods for MJRs. We propose an obstacle detection method based on a combination of attitude and distance detections, as well as MJRs' motion. A MEMS inertial sensor collects the yaw angle of the robot, and a ranging sensor senses the distance between the robot and the obstacle to estimate the size of the obstacle. We also propose an autonomous stair climbing algorithm based on the obstacle detection method. The robot can detect the height and width of stairs and its position relative to the stairs and then repeatedly jump to climb them step by step. Moreover, the height, width, and position are sent to a control terminal through a wireless sensor network to update the information regarding the MJR and stairs in a control interface. Furthermore, we conduct stair detection, modeling, and stair climbing experiments on the MJR and obtain acceptable precisions for autonomous obstacle negotiation. Thus, the proposed obstacle detection and stair climbing methods can enhance the locomotion capability of the MJR in environmental monitoring, search and rescue, etc.