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

arXiv cs.RO / 4/16/2026

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Key Points

  • The paper proposes the Human-Robot Co-Dispatch Facility Location Problem (HRCD-FLP), extending classical facility location to include tiered infrastructure criticality and human-robot supervision constraints for petro-site surveillance.
  • It evaluates selecting command centers under three technology-maturity scenarios, showing that increasing autonomy from a conservative 1:3 human-robot ratio to a future 1:10 ratio can significantly reduce cost while still maintaining full coverage of critical assets.
  • Computational results indicate exact optimization methods outperform heuristics on small problem instances in both cost and runtime, while the proposed heuristic scales to larger instances with feasible solutions in under 3 minutes and an ~14% optimality gap when baseline comparisons are possible.
  • The authors conclude that optimized human-robot teaming planning is necessary to achieve deployments that are simultaneously cost-effective and mission-reliable for high-stakes infrastructure security.

Abstract

Securing petroleum infrastructure requires balancing autonomous system efficiency with human judgment for threat escalation, a challenge unaddressed by classical facility location models assuming homogeneous resources. This paper formulates the Human-Robot Co-Dispatch Facility Location Problem (HRCD-FLP), a capacitated facility location variant incorporating tiered infrastructure criticality, human-robot supervision ratio constraints, and minimum utilization requirements. We evaluate command center selection across three technology maturity scenarios. Results show transitioning from conservative (1:3 human-robot supervision) to future autonomous operations (1:10) yields significant cost reduction while maintaining complete critical infrastructure coverage. For small problems, exact methods dominate in both cost and computation time; for larger problems, the proposed heuristic achieves feasible solutions in under 3 minutes with approximately 14% optimality gap where comparison is possible. From systems perspective, our work demonstrate that optimized planning for human-robot teaming is key to achieve both cost-effective and mission-reliable deployments.