Distributed trajectory management for urban air mobility operations with ground-based edge intelligence

Trajectory management is a critical undertaking in urban air mobility (UAM) to ensure safe, secure, and efficient operations. Cooperative targets have the capability to report their information while managing non-cooperative targets presents a challenge in the UAM operational environment (UOE). Consequently, ground-based non-cooperative surveillance assumes a vital role in monitoring anomalies. Given the difficulties associated with implementing centralized management in a large metropolitan area, this study proposes a distributed management architecture that leverages ground-based edge intelligence to enhance resilience in performing relevant tasks. It demonstrates that employing a developed edge computing system yields superior efficiency for heterogeneous sensors and their corresponding algorithms, such as detection, fusion, and tactical conflict management, compared to typical cloud servers. Furthermore, the proposed architecture incorporates an adaptive load balancing scheme, which monitors the real-time tasks and balances tasks among multiple edge devices to enhance the efficient resource management of the edge intelligence system. Ultimately, the distributed system offers energy-saving benefits and guarantees performance, making it suitable for providing services to diverse stakeholders involved in UAM.