Enhancing Safety and Energy-Efficiency in Advanced Air Mobility Through Trajectory Planning and Mission Feasibility Assessment Strategies
Open Access DepositedThe increasing demands on urban transportation systems, driven by rapid population growth and urbanization, have exposed critical limitations in existing ground infrastructure, including safety risks, time inefficiencies, environmental concerns, and economic burdens. To address these challenges, Advanced Air Mobility (AAM) has emerged as a promising alternative, enabling the movement of people and goods through next-generation electric vertical takeoff and landing (eVTOL) aircraft. This dissertation presents a set of trajectory planning and mission feasibility assessment strategies for AAM operations, with the overarching goal of enabling safe, scalable, energy-efficient, and resilient operations. First, a decentralized, real-time trajectory planning framework is developed to support conflict-free and scalable multi-agent operations in dense urban airspaces. The approach integrates a data-driven reachability analysis module with a Markov Decision Process (MDP)-based decision-making scheme. Simulation results involving up to 32 aircraft in a city-scale scenario demonstrate the planner’s effectiveness in reducing near mid-air collisions while maintaining computational efficiency. Second, a two-layer framework is proposed for energy-efficient trajectory planning and battery-related mission feasibility assessment, operating in a pre-departure phase. The upper layer consists of an MDP-based planner that accounts for aircraft dynamics and wind disturbances to generate energy-efficient trajectories. The lower layer evaluates the feasibility of these trajectories using a battery state prediction-based uncertainty quantification scheme. The framework is validated through high-fidelity simulations involving drone package delivery in an urban setting, as well as a real-world flight experiment. Third, a real-time mission feasibility and contingency management system is introduced to enhance operational safety during flight. The framework continuously monitors battery energy status by integrating power consumption prediction with battery voltage trajectory forecasting. In the event of an unexpected mid-flight incident resulting in energy depletion, the system autonomously reroutes the aircraft to a predefined emergency landing site. The effectiveness of this system is demonstrated through a drone delivery mission interrupted by such an incident. Collectively, these contributions offer a comprehensive set of tools to support safe, energy-efficient, and resilient AAM operations, advancing the integration of AAM aircraft into complex urban environments.
- All rights reserved
Notice to Authors
If you are the author of this work and you have any questions about the information on this page, please use the Contact form to get in touch with us.