Robust Barrier Functions for a Fully Autonomous, Remotely Accessible Swarm-Robotics Testbed
This work addresses the need for reliable, real-time constraint satisfaction in a widely used robotics platform, though it is incremental as it builds on existing barrier function methods.
The paper tackled the problem of ensuring collision avoidance in a swarm-robotics testbed by developing robust barrier functions to handle unmodeled disturbances like wheel slip, enabling fully autonomous operation without human supervision.
The Robotarium, a remotely accessible swarm-robotics testbed, has provided free, open access to robotics and controls research for hundreds of users in thousands of experiments. This high level of usage requires autonomy in the system, which mainly corresponds to constraint satisfaction in the context of users' submissions. In other words, in case that the users' inputs to the robots may lead to collisions, these inputs must be altered to avoid these collisions automatically. However, these alterations must be minimal so as to preserve the users' objective in the experiment. Toward this end, the system has utilized barrier functions, which admit a minimally invasive controller-synthesis procedure. However, barrier functions are yet to be robustified with respect to unmodeled disturbances (e.g., wheel slip or packet loss) in a manner conducive to real-time synthesis. As such, this paper formulates robust barrier functions for a general class of disturbed control-affine systems that, in turn, is key for the Robotarium to operate fully autonomously (i.e., without human supervision). Experimental results showcase the effectiveness of this robust formulation in a long-term experiment in the Robotarium.