Optimized Human-Robot Co-Dispatch Planning for Petro-Site Surveillance under Varying Criticalities

2026-04-16 · Source: cs.AI updates on arXiv.org · Field: Science & Research — Mathematics & Computational Sciences, Engineering & Applied Sciences, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

A new model, the Human-Robot Co-Dispatch Facility Location Problem (HRCD-FLP), has been developed to optimize the deployment of human-robot teams for petroleum infrastructure surveillance. This model addresses the limitations of classical facility location models by incorporating tiered infrastructure criticality, human-robot supervision ratio constraints, and minimum utilization requirements. It evaluates command center selection across three technology maturity scenarios: Conservative (1:3 human-robot supervision), Balanced (1:5), and Future (1:10). The research demonstrates that transitioning to more autonomous operations (1:10 ratio) can lead to significant cost reductions while maintaining complete critical infrastructure coverage. For small problems (15 candidate locations, 50 demand sites), exact methods are superior in cost and computation time. However, for larger problems (500 candidates, 5,000 sites), a proposed heuristic provides feasible solutions in under 3 minutes, achieving a 91x speedup compared to exact methods, albeit with an approximate 14% optimality gap.

Key takeaway

For research scientists designing security systems for critical infrastructure, you should integrate human-in-the-loop constraints and tiered asset criticality into your strategic planning models. Consider the trade-offs between exact optimization for smaller, precise deployments and heuristic approaches for large-scale, time-sensitive scenarios, recognizing that higher automation (e.g., 1:10 human-to-robot ratio) can yield substantial cost savings.

Key insights

Optimized human-robot co-dispatch planning significantly reduces costs for critical infrastructure surveillance while ensuring full coverage.

Principles

• Human judgment is essential for threat escalation in autonomous systems.
• Supervision ratios directly impact cost-effectiveness and network topology.
• Exact methods are best for small problems, heuristics for large-scale deployments.

Method

The HRCD-FLP formulates a mixed-integer linear program (MILP) with multi-level facility selection, heterogeneous resources, and supervision ratio constraints. A two-stage metaheuristic combines a greedy algorithm with local search for scalability.

In practice

• Relaxing human-robot supervision ratios reduces operational costs.
• Consolidate operations into fewer, higher-capacity centers for efficiency.
• Use heuristics for large-scale deployment planning to ensure timely solutions.

Topics

• Human-Robot Co-Dispatch
• Facility Location Problem
• Petroleum Infrastructure Security
• Critical Infrastructure Protection
• Supervision Ratio Constraints

Code references

• naimackerman/Petro-HRCD-FLP

Best for: Research Scientist, AI Scientist, Robotics Engineer, Operations Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.