Towards safe autonomy in space exploration using reconfigurable multi-robot systems
Thomas M. Roehr, Ronny Hartanto
In Proceedings of the International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS 2014), (iSAIRAS-2014), 17.6.-19.6.2014, Montreal, o.A., Jun/2014.

Zusammenfassung (Abstract) :

A number of space missions have proven the effectiveness of applying robots for planetary exploration. Those missions are usually handicapped by long distances and limited resources on the communication network which making such operations less efficient. One possible approach to overcome this problem is by increasing the level of autonomy of the deployed robotic systems. However, this is only cautiously being accepted as a tool for existing space missions and thus applied in a very limited fashion. Some reasons for these are limited experience with this technology in space missions, development costs, and a low to no risk tolerance in space missions. Meanwhile, the “pressure to reduce manpower during routine mission operation” [1] is real, though thus such robotic missions are far from becoming routine missions and demanding further research on novel mission design concepts. Therefore, this paper presents a novel approach for applying reconfigurable multi-robot systems to allow for more and safer autonomy in upcoming missions. [2] describes multi-robot systems for lunar sample collection in unknown environment using heterogeneous robots. However the robots are fixed and not reconfigurable, which a failure in one system might danger the overall mission. [3] presents the successful creation of a reconfigurable system, but it did not fully exploit the newly developed hardware capabilities for autonomy. This paper builds upon the experiences made in those projects and assumes a similarly capable multi-robot systems to introduce a concept for autonomous operations within safety constraints. This paper also outlines a strategy for improving an existing space control architecture relying on the Functional Reference Model (FRM) [4].

zuletzt geändert am 27.02.2023