Shaokun Cai

National University of Defense Technology

Papers

Total Citations

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About

Shaokun Cai is a rising researcher in the field of cooperative navigation and multi-agent systems, with a primary focus on improving the positioning accuracy of unmanned swarms. His key research areas include attitude determination, sensor fusion, and graph-based navigation for autonomous vehicles. In his most cited work, "Attitude Determination for Unmanned Cooperative Navigation Swarm Based on Multivectors in Covisibility Graph" (2023), Cai addresses a critical challenge in cost-effective swarm operations: enabling low-accuracy navigation sensors (LANs) to achieve higher positioning accuracy by leveraging information from high-accuracy navigation sensors (HANs) within the swarm. By introducing a multivector approach within a covisibility graph framework, his method enhances collaborative attitude estimation without requiring expensive sensors on every node. This contribution is particularly valuable for real-world deployments where budget constraints demand mixed-sensor architectures. With 4 citations to date, his work is gaining traction among researchers working on scalable, resilient navigation systems. Cai’s research holds promise for advancing autonomous drone swarms, search-and-rescue missions, and distributed surveillance, where reliable navigation under limited resources is essential.