*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Fanruiqi Zeng
Advisor: Prof. John-Paul Clarke
will propose a doctoral thesis entitled,
AUTONOMOUS VEHICLES: TRAJECTORY PLANNING AND ROUTING INTHE ERA OF ADVANCED AIR MOBILITY
Friday, December 17 at 1:30 p.m.
Montgomery Knight Building 317
Bluejeans: https://bluejeans.com/7991468082
Abstract
Advanced air mobility (AAM) is a revolutionary concept that enables on-demand air mobility, cargo delivery, and emergency services via an integrated and connected multimodal transportation network. In the era of AAM, the unmanned aerial vehicle (UAV) is envisioned as the primary tool for transporting people and cargo from point A to point B. As a result, UAVs will play an important role in AAM operations in the future, determining the level of safety, efficiency, and scalability systemwide.
Trajectory planning is critical to the safe and efficient operation of unmanned aerial vehicles (UAVs). The issue of trajectory planning under uncertainty has received a lot of attention in the robotics and control communities. Traditional trajectory planning approaches rely primarily on the premise that the uncertainty of dynamic obstacles is either bounded or can be statistically modeled. This is not the case in the urban environment, where the sources of uncertainty are diverse and their uncertain behavior is typically unpredictable, making precise modeling impossible. From this perspective, trajectory planning approaches that presume knowledge of dynamic obstacles are naturally limited, particularly in AAM operations. Motivated by this, we present a receding horizon control method with innovative trajectory planning policies that enable dynamic updating of planned trajectories in the presence of partially known and unknown uncertainty. The findings of this study have significant implications for achieving safe aviation autonomy in an AAM system.
UAVs, as an alternative mode of transportation, have advantages in terms of lower costs, better service, or the potential to provide new services that were previously not possible. Typically, those services involve routing a fleet of UAVs to serve specific demands. Despite the potential benefits, UAV has a natural limitation on the flight range due to its battery capacity. As a result, enabling the combination of UAV with ground vehicle, which can serve as a mobile charging platform for the UAV, is an important opportunity for practical impact and research challenges. In this thesis, we address the routing and coordination of a drone-truck pairing where the drone travels to multiple locations to perform specified observation tasks and rendezvous periodically with the truck to swap its batteries. We first propose a Mixed Integer Programing formulation driven by critical operational constraints. Given the NP-hard nature of the Nested-VRP, we analyze the complexity of MIP model and propose an efficient heuristic for solving the Nested-VRP model. We envision that this framework will facilitate the planning and operations of combined drone-truck missions and further improve the efficiency of AAM system.
Committee
