Multi-Agent Mental Models for Cooperation of Heterogeneous Robots in a Space Scenario

Kurzfassung

With each space robotic mission, a team of specialists on ground is usually required to support the robot throughout its mission. When scaling from single robots to fleets of robots, this will no longer be a viable way of conducting missions. Instead, in order to operate more autonomously in complex scenarios and adapt to dynamically changing environments without relying on a team of human experts, robots must be able to create a mental model of their surroundings. The relevance of this requirement becomes especially salient in the Surface Avatar ISS-to-ground telerobotic technology demonstration mission, where astronauts onboard the International Space Station command a team of heterogeneous robots in our lab. This work focuses on the final Surface Avatar session with NASA astronaut Jonny Kim where he was tasked with commanding the robots in a collaborative manner in order to complete simulated science and exploration tasks. To equip a complex robot such as DLR's Rollin' Justin with the ability to collaborate and coordinate with other robots, we deploy a combination of models and heuristics that allow it to create a mental model of its environment including its surrounding agents. Rollin' Justin also shares the estimated states generated in the mental model with the other robots. In this paper, we describe the application of this concept to our space experiment, present the models used to create the mental model, and evaluate it based on data collected in the final Surface Avatar experiment.