Cooperative UAV Trajectory Planning with Multiple Dynamic Targets

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83660/1/AIAA-2010-8437-330.pd

Hybridization of Nonlinear and Mixed-Integer Linear Programming for Aircraft Separation With Trajectory Recovery

International audienceThe approach presented in this article aims at finding a solution to the problem of conflict-free motion planning for multiple aircraft on the same flight level with trajectory recovery. One contribution of this work is to develop three consistent models, from a continuous-time representation to a discrete-time linear approximation. Each of these models guarantees separation at all times as well as trajectory recovery, but they are not equally difficult to solve. A new hybrid algorithm is thus developed in order to use the optimal solution of a mixed integer linear program as a starting point when solving a nonlinear formulation of the problem. The significance of this process is that it always finds a solution when the linear model is feasible while still taking into account the nonlinear nature of the problem. A test bed containing numerous data sets is then generated from three virtual scenarios. A comparative analysis with three different initialisations of the nonlinear optimisation validates the efficiency of the hybrid method

A Mixed Integer Programming Framework for the Fuel Optimal Guidance of Complex Spacecraft Rendezvous and Proximity Operation Missions

Space is a contested, congested, and competitive environment where space situational awareness (SSA) is a key factor in the long term sustainability of space as a national interest. Space-based SSA conducted by inspector satellites is critical to the detecting, tracking, and attribution of actions in space. Thus, space-based fuel-optimal maneuvers are essential to increasing mission life and improving the capability of inspector satellites working to characterize resident space objects (RSOs) in geosynchronous orbit (GEO). Additionally, on-orbit inspection missions can be characterized by multiple waypoint visits where an inspector is accomplishing a set of proximity operation mission objectives through the visit of multiple waypoints signifying viewing angles, natural motion circumnavigation (NMC) injection states, and rendezvous locations. Traditionally, the combinatorial and trajectory optimization aspects of these space-based multiple waypoint visits have been solved in a segregated manner. This thesis presents a Mixed Integer Programming (MIP) framework, in which the combinatorial and trajectory optimization nature of these problems are coupled resulting in the fuel-optimal guidance for complex rendezvous and proximity operation missions. First, a Mixed Integer Linear Programming (MILP) formulation is used to solve for the fuel optimal guidance of an inspector visiting multiple viewing angles, defined by waypoints, around a single RSO. This mission is subject to keep-out-zones (KOZ) and mission time constraints. Additionally, the initial MILP problem is extended to a linear cooperative control formulation where two inspectors are working together to accomplish the mission objectives. Both MILP problems are solved to global optimality using a commercial MIP solver

Autonomous search and tracking of objects using model predictive control of unmanned aerial vehicle and gimbal: Hardware-in-the-loop simulation of payload and avionics

This paper describes the design of model predictive control (MPC) for an unmanned aerial vehicle (UAV) used to track objects of interest identified by a real-time camera vision (CV) module in a search and track (SAT) autonomous system. A fully functional UAV payload is introduced, which includes an infra-red (IR) camera installed in a two-axis gimbal system. Hardware-in-loop (HIL) simulations are performed to test the MPC's performance in the SAT system, where the gimbal attitude and the UAV's flight trajectory are optimized to place the object to be tracked in the center of the IR camera's image.(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

Preference-Based Trajectory Generation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76820/1/AIAA-36214-892.pd

Generative Modeling of Human Behavior: Social Interaction and Networked Coordination in Shared Facilities

Urbanization is bringing together various modes of transport, and with that, there are challenges to maintaining the safety of all road users, especially vulnerable road users (VRUs). Therefore, there is a need for street designs that encourages cooperation and resource sharing among road users. Shared space is a street design approach that softens the demarcation of vehicles and pedestrian traffic by reducing traffic rules, traffic signals, road markings, and regulations. Understanding the interactions and trajectory formations of various VRUs will facilitate the design of safer shared spaces. It will also lead to many applications, such as implementing reliable ad hoc communication networks. In line with this motivation, this dissertation develops a methodology for generating VRUs\u27 trajectories that accounts for their walking behaviors and social interactions. The performed study leads to three traffic scenarios covering most pedestrian behavior and interactions traffic scenarios - group interactions, fixed obstacle interaction, and moving obstacle interaction. To implement the different scenarios in shared space facilities, we develop a receding horizon optimization-based trajectory planning algorithm capable of modeling pedestrian behavior and interactions. The generated trajectories are validated using two benchmark pedestrian datasets – DUT and TrajNet++. The validation is shown to yield low or near-zero Mean Euclidean Distance and Final Displacement Error values supporting the performance validity of the proposed generative algorithm. We further demonstrate the application of generated trajectories to predict the communication network topology formation, which leads to a stable network formation when integrated within ad hoc protocols. The developed pedestrian trajectory planning algorithm can be expanded as a simulation framework to provide a more realistic demonstration of how pedestrians use traffic facilities and interact with their environment. Moreover, the model\u27s applicability is not limited to road traffic and shared spaces. It can find broader applications such as the emergency evacuation of buildings, large events, airports, and railway stations