Autonomous navigation of hexapod robots with vision-based controller adaptation

This work introduces a novel hybrid control architecture for a hexapod platform (Weaver), making it capable of autonomously navigating in uneven terrain. The main contribution stems from the use of vision-based exteroceptive terrain perception to adapt the robot's locomotion parameters. Avoiding computationally expensive path planning for the individual foot tips, the adaptation controller enables the robot to reactively adapt to the surface structure it is moving on. The virtual stiffness, which mainly characterizes the behavior of the legs' impedance controller is adapted according to visually perceived terrain properties. To further improve locomotion, the frequency and height of the robot's stride are similarly adapted. Furthermore, novel methods for terrain characterization and a keyframe based visual-inertial odometry algorithm are combined to generate a spatial map of terrain characteristics. Localization via odometry also allows for autonomous missions on variable terrain by incorporating global navigation and terrain adaptation into one control architecture. Autonomous runs on a testbed with variable terrain types illustrate that adaptive stride and impedance behavior decreases the cost of transport by 30 % compared to a non-adaptive approach and simultaneously increases body stability (up to 88 % on even terrain and by 54 % on uneven terrain). Weaver is able to freely explore outdoor environments as it is completely free of external tethers, as shown in the experiments.