2 research outputs found
Robotic Vision for Space Mining
Future Moon bases will likely be constructed using resources mined from the
surface of the Moon. The difficulty of maintaining a human workforce on the
Moon and communications lag with Earth means that mining will need to be
conducted using collaborative robots with a high degree of autonomy. In this
paper, we explore the utility of robotic vision towards addressing several
major challenges in autonomous mining in the lunar environment: lack of
satellite positioning systems, navigation in hazardous terrain, and delicate
robot interactions. Specifically, we describe and report the results of robotic
vision algorithms that we developed for Phase 2 of the NASA Space Robotics
Challenge, which was framed in the context of autonomous collaborative robots
for mining on the Moon. The competition provided a simulated lunar environment
that exhibits the complexities alluded to above. We show how machine
learning-enabled vision could help alleviate the challenges posed by the lunar
environment. A robust multi-robot coordinator was also developed to achieve
long-term operation and effective collaboration between robots.Comment: This paper describes our 3rd place and innovation award winning
solution to the NASA Space Robotics Challenge Phase
A Hybrid Systems-based Hierarchical Control Architecture for Heterogeneous Field Robot Teams
Field robot systems have recently been applied to a wide range of research
fields. Making such systems more automated, advanced, and activated requires
cooperation among heterogeneous robots. Classic control theory is inefficient
in managing large-scale complex dynamic systems. Therefore, the supervisory
control theory based on discrete event system needs to be introduced to
overcome this limitation. In this study, we propose a hybrid systems-based
hierarchical control architecture through a supervisory control-based
high-level controller and a traditional control-based low-level controller. The
hybrid systems and its dynamics are modeled through a formal method called
hybrid automata, and the behavior specifications expressing the control
objectives for cooperation are designed. Additionally, a modular supervisor
that is more scalable and maintainable than a centralized supervisory
controller was synthesized. The proposed hybrid systems and hierarchical
control architecture were implemented, validated, and then evaluated for
performance through the physics-based simulator. Experimental results confirmed
that the heterogeneous field robot team satisfied the given specifications and
presented systematic results, validating the efficiency of the proposed control
architecture.Comment: 23pages, 19 figures, submitted for publicatio