Communication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh Networks

2024-07-15

AI Summary

This work addresses distributed submodular optimization in resource-constrained robotic mesh networks. We propose Resource-Aware distributed Greedy (RAG), a novel paradigm enabling real-time collaborative decision-making using only local neighborhood information and achieving linear time complexity. Theoretically, we characterize the asymmetric impact of graph sparsity on scalability and approximation ratio. Methodologically, RAG integrates graph-topology-aware design, Zigbee 3.0 communication modeling, and high-fidelity AirSim simulation. Evaluated on a 45-robot system, RAG achieves millisecond-level end-to-end latency, accelerates planning by three orders of magnitude over state-of-the-art near-optimal algorithms, and significantly improves average coverage performance. The framework effectively supports large-scale, low-overhead, high-real-time applications—including map construction, environmental monitoring, and target tracking—under stringent resource constraints.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

We provide a communication- and computation-efficient method for distributed submodular optimization in robot mesh networks. Submodularity is a property of diminishing returns that arises in active information gathering such as mapping, surveillance, and target tracking. Our method, Resource-Aware distributed Greedy (RAG), introduces a new distributed optimization paradigm that enables scalable and near-optimal action coordination. To this end, RAG requires each robot to make decisions based only on information received from and about their neighbors. In contrast, the current paradigms allow the relay of information about all robots across the network. As a result, RAG's decision-time scales linearly with the network size, while state-of-the-art near-optimal submodular optimization algorithms scale cubically. We also characterize how the designed mesh-network topology affects RAG's approximation performance. Our analysis implies that sparser networks favor scalability without proportionally compromising approximation performance: while RAG's decision time scales linearly with network size, the gain in approximation performance scales sublinearly. We demonstrate RAG's performance in simulated scenarios of area detection with up to 45 robots, simulating realistic robot-to-robot (r2r) communication speeds such as the 0.25 Mbps speed of the Digi XBee 3 Zigbee 3.0. In the simulations, RAG enables real-time planning, up to three orders of magnitude faster than competitive near-optimal algorithms, while also achieving superior mean coverage performance. To enable the simulations, we extend the high-fidelity and photo-realistic simulator AirSim by integrating a scalable collaborative autonomy pipeline to tens of robots and simulating r2r communication delays. Our code is available at https://github.com/UM-iRaL/Resource-Aware-Coordination-AirSim.

Problem

• Resource Optimization
• Robotics Network
• Decision Efficiency

Innovation

• Distributed Submodular Optimization
• Resource-Aware Greedy (RAG) Algorithm
• Real-time Planning Capabilities