End-to-End Design for Self-Reconfigurable Heterogeneous Robotic Swarms
This addresses the need for more adaptable robotic swarms in complex scenarios requiring real-time data processing and situational awareness, representing a novel integration of computing techniques into robotics.
The paper tackles the problem of limited flexibility in robotic swarms for real-world applications by introducing elastic computing and dynamic resource management from edge-cloud computing to enable dynamic provisioning of collective capabilities, transforming swarms into distributed sensing and computing platforms for complex data processing tasks.
More widespread adoption requires swarms of robots to be more flexible for real-world applications. Multiple challenges remain in complex scenarios where a large amount of data needs to be processed in real-time and high degrees of situational awareness are required. The options in this direction are limited in existing robotic swarms, mostly homogeneous robots with limited operational and reconfiguration flexibility. We address this by bringing elastic computing techniques and dynamic resource management from the edge-cloud computing domain to the swarm robotics domain. This enables the dynamic provisioning of collective capabilities in the swarm for different applications. Therefore, we transform a swarm into a distributed sensing and computing platform capable of complex data processing tasks, which can then be offered as a service. In particular, we discuss how this can be applied to adaptive resource management in a heterogeneous swarm of drones, and how we are implementing the dynamic deployment of distributed data processing algorithms. With an elastic drone swarm built on reconfigurable hardware and containerized services, it will be possible to raise the self-awareness, degree of intelligence, and level of autonomy of heterogeneous swarms of robots. We describe novel directions for collaborative perception, and new ways of interacting with a robotic swarm.