2460. Human-Robot Collaborative Patrol Routing Problem Balancing Hot Spot Coverage and General Area Patrolling
Invited abstract in session MB-55: Network Optimization 2, stream Network Optimization.
Monday, 10:30-12:00Room: Liberty 1.09
Authors (first author is the speaker)
| 1. | Pang Zezhao
|
| School of Management, Huazhong University of Science and Technology |
Abstract
Patrolling is critical for public safety, yet budget cuts have strained police resources. Patrol robots are introduced to adress above issue. We propose a human-robot collaboration model integrating patrol robots into policing operations. Multiple robots patrol police-frequented streets, handling routine tasks to reduce patrol time on general area routes, enabling police to arrive timely at hot spots to response to potential crimes. We formulate the problem based on the k-Chinese Postman Problem to optimize the deployment locations and number of patrol robots, as well as police patrol routes, with the objective of minimizing patrolling costs while satisfying coverage constraints for hot spot areas. The problem is solved using the Adaptive Large Neighborhood Search (ALNS) algorithm, where we designed several problem-based destroy and repair operators concerning the effectiveness of patrol robots' position and timing of police covering hot spots. Experimental results demonstrate that the human-robot collaboration model reduces the required number of police forces while ensuring hot spots coverage and reducing patrolling costs. Compared to traditional pure-police patrolling models, the proposed approach achieves lower patrolling costs. This study provides theoretical and methodological support for optimizing police resources, with significant practical applications.
Keywords
- Network Design
Status: accepted
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