D-planner: An Efficient Surrounding-aware Multi-drone System for Urban Monitoring

In monitoring urban areas with dense infrastructures, drone swarms emerge as an efficient means for locating and collecting data from urban targets. In this task, the drone swarms are required to visit a set of valuable sites. However, many challenges exist in the data collection process of drone swarms: complex streets and obstacles require drones to use computer vision and pathfinding algorithms to perceive the environment, avoid collisions, and manage power in real time with their limited onboard computation and battery. In extreme situations where battery limitations prevent the surveillance of all intended sites, drones are desired to collect information from as many valuable targets as possible. It calls for extra smart path planning. Meanwhile, dynamically changing zone priorities and environments can suddenly change target values or render planned sites dangerous, necessitating immediate path recomputation to ensure efficiencies of urban target surveillance.In this paper, we address the following question: Can we enhance the performance, including safety and efficiency, of drone swarms to conduct urban target monitoring, despite constraints like battery life, limited computational resources, and evolving environments? We introduce our solution, D-planner, a system that performs efficient drone swarm path planning with intelligent navigation algorithms, carries out collision-free navigation using a computer vision and pathfinding module, and integrates incremental dynamic path computation that leverages geometries and greedy strategies to figure out the priority of data sites and plan safe paths with a limited deviation from the original paths. To evaluate the system, we constructed a simulation environment of a large city with meter-level precision using Google Maps. Experiments on our system show that D-planner can improve the planning speed of target-rich paths by up to 6.5× and the total value by up to 25.60% compared with baseline solutions.

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Publication Details

Published
2024-10-28
DOI
https://doi.org/10.1109/milcom61039.2024.10773808
Citations
1
Primary Topic
Video Surveillance and Tracking Methods
Type
article
Field-Weighted Citation Impact
0.25
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article

D-planner: An Efficient Surrounding-aware Multi-drone System for Urban Monitoring

Tianhao Yu, Matthew Caesar, Shadman Saqib Eusuf
1 citations
Video Surveillance and Tracking Methods
0.25
article

D-planner: An Efficient Surrounding-aware Multi-drone System for Urban Monitoring

Tianhao Yu, Matthew Caesar, Shadman Saqib Eusuf
article en
1 citations

Abstract

In monitoring urban areas with dense infrastructures, drone swarms emerge as an efficient means for locating and collecting data from urban targets. In this task, the drone swarms are required to visit a set of valuable sites. However, many challenges exist in the data collection process of drone swarms: complex streets and obstacles require drones to use computer vision and pathfinding algorithms to perceive the environment, avoid collisions, and manage power in real time with their limited onboard computation and battery. In extreme situations where battery limitations prevent the surveillance of all intended sites, drones are desired to collect information from as many valuable targets as possible. It calls for extra smart path planning. Meanwhile, dynamically changing zone priorities and environments can suddenly change target values or render planned sites dangerous, necessitating immediate path recomputation to ensure efficiencies of urban target surveillance.In this paper, we address the following question: Can we enhance the performance, including safety and efficiency, of drone swarms to conduct urban target monitoring, despite constraints like battery life, limited computational resources, and evolving environments? We introduce our solution, D-planner, a system that performs efficient drone swarm path planning with intelligent navigation algorithms, carries out collision-free navigation using a computer vision and pathfinding module, and integrates incremental dynamic path computation that leverages geometries and greedy strategies to figure out the priority of data sites and plan safe paths with a limited deviation from the original paths. To evaluate the system, we constructed a simulation environment of a large city with meter-level precision using Google Maps. Experiments on our system show that D-planner can improve the planning speed of target-rich paths by up to 6.5× and the total value by up to 25.60% compared with baseline solutions.

University of Illinois Urbana-Champaign (US)
Sustainable cities and communities
Openalex Percentile: Top 44%
Video Surveillance and Tracking Methods
0.25
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