An Application of the Simultaneous Localization and Mapping (SLAM) Method Based on the Unscented Kalman Filter (UKF) to a Reconfigurable Quadruped Robot with Crawling Locomotion

Motivated by availability of autonomous reconfigurable robots, we have developed a reconfigurable quadruped robot, Scorpio, which can perform crawling and rolling locomotion. In this paper, we present an approach to the simultaneous localization and mapping (SLAM) for the quadruped robot in crawling locomotion to achieve its autonomous operation in an unknown environment. For the solution of the SLAM problem, we developed a dynamic model of the crawling quadruped robot, which considers friction offered to supporting legs and the switching of supporting legs, and applied the SLAM algorithm based on the unscented Kalman filter (UKF). The feasibility of the approach is verified through numerical simulations whose results show that the algorithm provides accurate estimates of the robot trajectory and the locations of surrounding landmarks simultaneously. This work demonstrates an effective approach for the application of the SLAM algorithm to a reconfigurable robot.

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