31 research outputs found
Distributed Consensus to Enable Merging and Spacing of UAS in an Urban Environment
This paper presents a novel approach to enable multiple Unmanned Aerial Systems approaching a common intersection to independently schedule their arrival time while maintaining a safe separation. Aircraft merging at a common intersection are grouped into a network and each aircraft broadcasts its arrival time interval to the network. A distributed consensus algorithm elects a leader among the aircraft approaching the intersection and helps synchronize the information received by each aircraft. The consensus algorithm ensures that each aircraft computes a schedule with the same input information. The elected leader also dictates when a schedule must be computed, which may be triggered when a new aircraft joins the network. Preliminary results illustrating the collaborative behavior of the vehicles are presented
An Optimal Coordination Framework for Connected and Automated Vehicles in two Interconnected Intersections
In this paper, we provide a decentralized optimal control framework for
coordinating connected and automated vehicles (CAVs) in two interconnected
intersections. We formulate a control problem and provide a solution that can
be implemented in real time. The solution yields the optimal
acceleration/deceleration of each CAV under the safety constraint at "conflict
zones," where there is a chance of potential collision. Our objective is to
minimize travel time for each CAV. If no such solution exists, then each CAV
solves an energy-optimal control problem. We evaluate the effectiveness of the
efficiency of the proposed framework through simulation.Comment: 8 pages, 5 figures, IEEE CONFERENCE ON CONTROL TECHNOLOGY AND
APPLICATIONS 201
Conditions for State and Control Constraint Activation in Coordination of Connected and Automated Vehicles
Connected and automated vehicles (CAVs) provide the most intriguing
opportunity to reduce pollution, energy consumption, and travel delays. In
earlier work, we addressed the optimal coordination of CAVs using Hamiltonian
analysis. In this paper, we investigate the nature of the unconstrained problem
and provide conditions under which the state and control constraints become
active. We derive a closed-form analytical solution of the constrained
optimization problem and evaluate the solution using numerical simulation
Advances in the Hierarchical Emergent Behaviors (HEB) approach to autonomous vehicles
Widespread deployment of autonomous vehicles (AVs) presents formidable challenges in terms on handling scalability and complexity, particularly regarding vehicular reaction in the face of unforeseen corner cases. Hierarchical Emergent Behaviors (HEB) is a scalable architecture based on the concepts of emergent behaviors and hierarchical decomposition. It relies on a few simple but powerful rules to govern local vehicular interactions. Rather than requiring prescriptive programming of every possible scenario, HEB’s approach relies on global behaviors induced by the application of these local, well-understood rules. Our first two papers on HEB focused on a primal set of rules applied at the first hierarchical level. On the path to systematize a solid design methodology, this paper proposes additional rules for the second level, studies through simulations the resultant richer set of emergent behaviors, and discusses the communica-tion mechanisms between the different levels.Peer ReviewedPostprint (author's final draft