臺灣博碩士論文加值系統

隨著小型無人機價格不斷降低，它們變得越來越普及的同時也被應用於許多領域，包括物流、GIS 和救災。與單台無人機不同，無人機群可以更有效地完成任務。 然而，當前的無人機群大多是通過離線計算模擬進行預設路徑，如常見的無人機燈光表演。這種限制源於高度自主編隊演算法需要大量的訊息交換，而現有的無人機通訊框架基於單一的規則的邏輯，不適合需要動態拓撲的編隊演算法。
本文提出一種專為無人機設計的新式無人機群網路架構AreoSyn。 本論文對架構中兩種通訊方式都經過了嚴格的測試，主要測試往返時延和掉包率，確保系統的可靠性。 該架構已在無人機上部署，並在兩種編隊配置模式下進行了實飛測試。儘管領導者-跟隨者和分佈式控制演算法有不同的通訊要求，但AreoSyn依然可以通過簡單的配置來適應這些需求，證明了此系統的可行性和靈活性。
本文還將Protocol Buffers取代MAVLink作為主要通訊封包格式，其可擴展的特性使改變原有格式不會破壞與現有系統的兼容性，且Protocol Buffers在網路上已被廣泛使用。這提升無人機群和網絡系統之間的互操作性。未來將增強AreoSyn的功能，探索潛在的整合應用，並測試 5G網路下的系統性能。

Since low-cost drones have increased worldwide, they are used in many fields, including logistics, GIS, and disaster relief. Drone swarms, unlike single drones, accomplish tasks more efficiently. However, current drone swarms are pre-programmed and refined through extensive offline computational simulations, limiting their practical applications. This limitation arises from the high-autonomous formation algorithms' need for extensive information exchange, and the existing drone communication frameworks are based on single rule-based logic, which is unsuitable for dynamic topology.
This thesis proposes AreoSyn, a novel network architecture specifically designed for drones. AreoSyn has undergone rigorous quality testing for two communication methods, focusing on round-trip delay and packet loss rate, ensuring system reliability. The architecture has been successfully implemented on a UAV and tested in two configuration modes. Despite the different communication requirements of leader-follower and distributed control algorithms, AreoSyn can accommodate these with minor adjustments, demonstrating its feasibility and flexibility. This thesis also replaces MavLink with Protocol Buffers as the primary communication packet format. Its extensible characteristics adapt to the original format, not destroy compatibility with the existing system, and Protocol Buffers have been widely used on the Internet. This improves interoperability between drone swarms and networked systems. Future work will enhance AreoSyn capabilities, explore potential integration applications, and test system performance on 5G networks.

中文摘要 i
Abstract ii
Contents iii
List of Figures v
List of Tables viii
Chapter 1 Introduction 1
1.1 The Growing Popularity of UAVs 1
1.2 The Future Development of Drone Swarms 2
1.3 Contributions 3
Chapter 2 Related work 5
2.1 Current Communication Architectures 5
2.1.1 Centralized Communication Architecture 6
2.1.2 Decentralized Communication Architecture 7
2.1.3 Cellular Networks architecture 9
2.2 Robot Operating System 10
2.3 Micro Air Vehicle Link 11
2.4 Protocol Buffers 12
2.5 MQ Telemetry Transport 13
2.5.1 Quality of Service Levels 15
Chapter 3 Core System Design 17
3.1 Messaging Patterns 18
3.1.1 Publish/Subscribe pattern 19
3.1.2 Event Driven Architecture 21
3.2 Handling Unexpected Disconnections 22
3.3 Implement Shared Subscriptions for Data Storage 25
3.4 UAVConnector module 27
3.5 Proximity detection 30
Chapter 4 System Implementation 33
4.1 Comprehensive Overview of Hardware and Software Deployed 34
4.2 MQTT-ROS Pipeline 41
4.3 Maximizing Interoperability and Extensibility with Protobuf 42
4.4 Visualization Platform 44
Chapter 5 Experimental Results 47
5.1 Quality Evaluation for UAVConnector 47
5.2 Quality Evaluation for MQTT-ROS Pipeline 52
5.3 Evaluating Onboard Computer Response Time 54
5.4 Real-word Experiments 55
Chapter 6 Conclusion 62
References 64

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