Terrain Adaption Controller for a Walking Excavator Robot using Deep Reinforcement Learning
Ajish Babu, Frank Kirchner
In 2021 20th International Conference on Advanced Robotics (ICAR), (ICAR-2021), 07.12.-10.12.2021, Ljubljana, IEEE Xplore, pages 64-70, Dec/2021.
Automation of heavy-duty vehicles using technologies
developed in the robotics domain is gaining popularity. One
such vehicle is the walking excavator with active suspension
chassis for adapting to uneven terrain. The terrain adaption
controller automates the suspension control by considering the
factors stability, underlying terrain structure, wheel-ground
distance, chassis-ground distance, etc. This work builds the
controller, which actuates the joints that control the height of
the wheels. Deep reinforcement learning is used, considering the
complexity of the problem and transferability to other robots.
The controller is learned and evaluated in simulation, where
continuous terrain with varying slopes is automatically generated.
Autoencoders compress the height-map and convert it
into latent space of different sizes. Three groups of controllers
are then designed based on observations given to the controller.
Evaluation of controllers shows that the controllers with ground
distances as observation perform better. If the ground distances
are part of the observation, there is no significant difference
in performance between controllers with different latent space
sizes. For controllers with terrain information and no ground
distances, the evaluation results match the terrain reconstruction
accuracy of the corresponding autoencoder.