The Pluggable Distributed Resource Allocator (PDRA): a Middleware for Distributed Computing in Mobile Robotic Networks

• Published in: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 4337 - 4344
• Main Authors: Rossi, Federico, Vaquero, Tiago Stegun, Sanchez-Net, Marc, da Silva, Maira Saboia, Vander Hook, Joshua
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24.10.2020
• Subjects: Distributed computing; Handheld computers; Middleware; Resource management; Robots; Task analysis; User interfaces
• ISSN: 2153-0866
• DOI: 10.1109/IROS45743.2020.9341205

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 plan- ner/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.