Autonomous Obstacle Legipulation with a Hexapod Robot
This addresses the challenge of autonomous obstacle manipulation for legged robots in confined environments, representing an incremental advance in robotic locomotion and manipulation.
The paper tackles the problem of legged robots navigating confined spaces blocked by movable obstacles by developing an autonomous method for a hexapod robot to generate manipulation trajectories using RGB-D sensor input, with experiments on a 30-degree-of-freedom robot demonstrating effectiveness in moving obstacles out of the path.
Legged robots traversing in confined environments could find their only path is blocked by obstacles. In circumstances where the obstacles are movable, a multilegged robot can manipulate the obstacles using its legs to allow it to continue on its path. We present a method for a hexapod robot to autonomously generate manipulation trajectories for detected obstacles. Using a RGB-D sensor as input, the obstacle is extracted from the environment and filtered to provide key contact points for the manipulation algorithm to calculate a trajectory to move the obstacle out of the path. Experiments on a 30 degree of freedom hexapod robot show the effectiveness of the algorithm in manipulating a range of obstacles in a 3D environment using its front legs.