The Pluggable Distributed Resource Allocator (PDRA): a Middleware for Distributed Computing in Mobile Robotic Networks
This addresses the problem of resource efficiency in mobile robotic networks, but it is incremental as it builds on existing middleware and scheduling methods.
The paper tackles the problem of enabling autonomous robotic agents in heterogeneous mobile networks to share computational resources for expensive tasks like localization and path planning, resulting in simulation-based reductions of over 50% in energy and CPU usage compared to a naive scheduler.
We present the Pluggable Distributed Resource Allocator (PDRA), a middleware for distributed computing in heterogeneous mobile robotic networks. PDRA enables autonomous robotic agents to share computational resources for computationally expensive tasks such as localization and path planning. It sits between an existing single-agent planner/executor and existing computational resources (e.g. ROS packages), intercepts the executor's requests and, if needed, transparently routes them to other robots for execution. PDRA is pluggable: it can be integrated in an existing single-robot autonomy stack with minimal modifications. Task allocation decisions are performed by a mixed-integer programming algorithm, solved in a shared-world fashion, that models CPU resources, latency requirements, and multi-hop, periodic, bandwidth-limited network communications; the algorithm can minimize overall energy usage or maximize the reward for completing optional tasks. Simulation results show that PDRA can reduce energy and CPU usage by over 50% in representative multi-robot scenarios compared to a naive scheduler; runs on embedded platforms; and performs well in delay- and disruption-tolerant networks (DTNs). PDRA is available to the community under an open-source license.