13 research outputs found
Learned and Controlled Autonomous Robotic Exploration in an Extreme, Unknown Environment
Exploring and traversing extreme terrain with surface robots is difficult,
but highly desirable for many applications, including exploration of planetary
surfaces, search and rescue, among others. For these applications, to ensure
the robot can predictably locomote, the interaction between the terrain and
vehicle, terramechanics, must be incorporated into the model of the robot's
locomotion. Modeling terramechanic effects is difficult and may be impossible
in situations where the terrain is not known a priori. For these reasons,
learning a terramechanics model online is desirable to increase the
predictability of the robot's motion. A problem with previous implementations
of learning algorithms is that the terramechanics model and corresponding
generated control policies are not easily interpretable or extensible. If the
models were of interpretable form, designers could use the learned models to
inform vehicle and/or control design changes to refine the robot architecture
for future applications. This paper explores a new method for learning a
terramechanics model and a control policy using a model-based genetic
algorithm. The proposed method yields an interpretable model, which can be
analyzed using preexisting analysis methods. The paper provides simulation
results that show for a practical application, the genetic algorithm
performance is approximately equal to the performance of a state-of-the-art
neural network approach, which does not provide an easily interpretable model.Comment: Published in: 2019 IEEE Aerospace Conference Date of Conference: 2-9
March 2019 Date Added to IEEE Xplore: 20 June 201