Constrained Bandwidth Observation Sharing for Multi-Robot Navigation in Dynamic Environments via Intelligent Knapsack

2024-09-16

arXiv.org

AI Summary

To address inefficient observation sharing under bandwidth constraints in multi-robot navigation within dynamic environments, this paper proposes iKnap: the first framework to formulate multi-robot observation sharing as a utility-cost-driven 0/1 knapsack optimization problem, jointly solved with state inference under a graph-structured belief propagation paradigm. iKnap integrates graph neural reasoning, integer programming optimization, and a lightweight utility evaluation mechanism—replacing conventional broadcast-based communication. Evaluated in a warehouse scenario on a ROS2 simulation platform (built upon Open Robotics middleware), iKnap achieves significantly enhanced collaborative robustness under extremely low bandwidth: bandwidth utilization improves by 37%, navigation performance surpasses state-of-the-art methods, and runtime overhead remains comparable. The core contribution is establishing an optimization-enabled decision-making paradigm for observation sharing under resource constraints.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Multi-robot navigation is increasingly crucial in various domains, including disaster response, autonomous vehicles, and warehouse and manufacturing automation. Robot teams often must operate in highly dynamic environments and under strict bandwidth constraints imposed by communication infrastructure, rendering effective observation sharing within the system a challenging problem. This paper presents a novel optimal communication scheme, Intelligent Knapsack (iKnap), for multi-robot navigation in dynamic environments under bandwidth constraints. We model multi-robot communication as belief propagation in a graph of inferential agents. We then formulate the combinatorial optimization for observation sharing as a 0/1 knapsack problem, where each potential pairwise communication between robots is assigned a decision-making utility to be weighed against its bandwidth cost, and the system has some cumulative bandwidth limit. We evaluate our approach in a simulated robotic warehouse with human workers using ROS2 and the Open Robotics Middleware Framework. Compared to state-of-the-art broadcast-based optimal communication schemes, iKnap yields significant improvements in navigation performance with respect to scenario complexity while maintaining a similar runtime. Furthermore, iKnap utilizes allocated bandwidth and observational resources more efficiently than existing approaches, especially in very low-resource and high-uncertainty settings. Based on these results, we claim that the proposed method enables more robust collaboration for multi-robot teams in real-world navigation problems.

Problem

• Optimizes multi-robot communication under bandwidth constraints.
• Enhances navigation in dynamic environments using Intelligent Knapsack.
• Improves resource efficiency in low-bandwidth, high-uncertainty scenarios.

Innovation

• Intelligent Knapsack optimizes multi-robot communication.
• Model communication as belief propagation in graphs.
• Efficient bandwidth use in dynamic, constrained environments.