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

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

URL: http://arxiv.org/abs/2602.07924v1
Date: Sun, 08 Feb 2026 11:46:40 GMT
Title: Optimized Human-Robot Co-Dispatch Planning for Petro-Site Surveillance under Varying Criticalities
Authors: Nur Ahmad Khatim, Mansur Arief,
Abstract summary: This paper formulates the Human-Robot Co-Dispatch Facility Location Problem (HRCD-FLP)<n>It is a capacitated facility location variant incorporating tiered infrastructure criticality, human-robot supervision ratio constraints, and minimum utilization requirements.<n>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.
Score: 0.9668407688201359
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

Related papers

End-to-end Optimization of Belief and Policy Learning in Shared Autonomy Paradigms [0.12314765641075438]
Shared autonomy systems require principled methods for inferring user intent and determining appropriate assistance levels.<n>Previous approaches relied on static blending ratios or separated goal inference from assistance arbitration, leading to suboptimal performance in unstructured environments.<n>We introduce BRACE, a novel framework that fine-tunes Bayesian intent inference and context-adaptive assistance.
arXiv Detail & Related papers (2026-01-30T18:59:16Z)
Autonomous Collaborative Scheduling of Time-dependent UAVs, Workers and Vehicles for Crowdsensing in Disaster Response [18.44231237535367]
This paper explores the heterogeneous multi-agent online collaborative scheduling algorithm HoAs-PALN.<n>HoAs-PALN is realized through adaptive dimensionality reduction in the matching process and local Nash equilibrium game.<n>Compared with the baselines, HoAs-PALN improves task completion rates by 64.12%, 46.48%, 16.55%, and 14.03% on average.
arXiv Detail & Related papers (2025-06-04T01:58:05Z)
Towards Autonomous Micromobility through Scalable Urban Simulation [52.749987132021324]
Current micromobility depends mostly on human manual operation (in-person or remote control)<n>In this work, we present a scalable urban simulation solution to advance autonomous micromobility.
arXiv Detail & Related papers (2025-05-01T17:52:29Z)
Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning [52.64813150003228]
The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring.<n>In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas.<n>The task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV.
arXiv Detail & Related papers (2025-01-11T02:32:42Z)
Generalizability of Graph Neural Networks for Decentralized Unlabeled Motion Planning [72.86540018081531]
Unlabeled motion planning involves assigning a set of robots to target locations while ensuring collision avoidance. This problem forms an essential building block for multi-robot systems in applications such as exploration, surveillance, and transportation. We address this problem in a decentralized setting where each robot knows only the positions of its $k$-nearest robots and $k$-nearest targets.
arXiv Detail & Related papers (2024-09-29T23:57:25Z)
Robotic warehousing operations: a learn-then-optimize approach to large-scale neighborhood search [84.39855372157616]
This paper supports robotic parts-to-picker operations in warehousing by optimizing order-workstation assignments, item-pod assignments and the schedule of order fulfillment at workstations. We solve it via large-scale neighborhood search, with a novel learn-then-optimize approach to subproblem generation. In collaboration with Amazon Robotics, we show that our model and algorithm generate much stronger solutions for practical problems than state-of-the-art approaches.
arXiv Detail & Related papers (2024-08-29T20:22:22Z)
Analytical model for large-scale design of sidewalk delivery robot systems [4.510000677649468]
We propose a model that captures both the initial cost and the operation cost of the delivery system and evaluates the impact of constraints and operation strategies on the deployment. We then apply the model in neighborhoods in New York City to evaluate deploying the sidewalk delivery robot system in a real-world scenario.
arXiv Detail & Related papers (2023-10-26T15:26:12Z)
DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments [55.204450019073036]
We present a novel reinforcement learning based task allocation and decentralized navigation algorithm for mobile robots in warehouse environments. We consider the problem of joint decentralized task allocation and navigation and present a two level approach to solve it. We observe improvement up to 14% in terms of task completion time and up-to 40% improvement in terms of computing collision-free trajectories for the robots.
arXiv Detail & Related papers (2022-09-07T00:35:27Z)
SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
arXiv Detail & Related papers (2021-08-03T02:56:21Z)
Symbiotic System Design for Safe and Resilient Autonomous Robotics in Offshore Wind Farms [3.5409202655473724]
Barriers to Beyond Visual Line of Sight (BVLOS) robotics include operational safety compliance and resilience. We propose a symbiotic system; reflecting the lifecycle learning and co-evolution with knowledge sharing for mutual gain of robotic platforms and remote human operators. Our methodology enables the run-time verification of safety, reliability and resilience during autonomous missions.
arXiv Detail & Related papers (2021-01-23T11:58:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.