Platooning connected and autonomous vehicles (CAVs) provide significant benefits in terms of traffic efficiency and fuel economy. However, most existing platooning systems assume the availability of pre-determined plans, which is not feasible in real-time scenarios. In this paper, we address this issue in time-dependent networks by formulating a Markov decision process at each junction, aiming to minimize travel time and fuel consumption. Initially, we analyze coordinated platooning without routing to explore the cooperation among controllers on an identical path. We propose two novel approaches based on approximate dynamic programming, offering suboptimal control in the context of a stochastic finite horizon problem. The results demonstrate the superiority of the approximation in the policy space. Furthermore, we investigate platooning in a network setting, where speed profiles and routes are determined simultaneously. To simplify the problem, we decouple the action space by prioritizing routing decisions based on travel time estimation. We subsequently employ the aforementioned policy approximation to determine speed profiles, considering essential parameters such as travel times. Our simulation results in SUMO indicate that our method yields better performance than conventional approaches, leading to potential travel cost savings of up to 38%. Additionally, we evaluate the resilience of our approach in dynamically changing networks, affirming its ability to maintain efficient platooning operations.

Chance-constrained optimization provides a promi- sing framework for solving control and planning problems with uncertainties, due to its modeling capability to capture randomness in real-world applications. In this paper, we consider a UAV trajectory planning problem with probabilistic geo-fence, building on the chance-constrained optimization approach. In the considered problem, randomness of the model, such as the uncertain boundaries of geo-fences, is incorporated in the formulation. By solving the formulated chance-constrained optimization with a novel sampling based solution method, the optimal UAV trajectory is achieved while limiting the probability of collision with geo-fences to a prefixed threshold. Furthermore, to obtain a totally collision-free trajectory, i.e., avoiding the collision not only at the discrete time-steps but also within the entire time horizon, we build on the idea of an iterative scheme. That is, to iterate the solving of the chance-constrained optimization until the collision with probabilistic geo-fence is avoided at any time within the time horizon. At last, we validate the effectiveness of our method via numerical simulations.

This paper presents an algorithmic framework for the distributed on-line source seeking, termed as DoSS, with a multi-robot system in an unknown dynamical environment. Our algorithm, building on a novel concept called dummy confidence upper bound (D-UCB), integrates both estimation of the unknown environment and task planning for the multiple robots simultaneously, and as a result, drives the team of robots to a steady state in which multiple sources of interest are located. Unlike the standard UCB algorithm in the context of multi-armed bandits, the introduction of D-UCB significantly reduces the computational complexity in solving subproblems of the multi-robot task planning. This also enables our DoSS algorithm to be implementable in a distributed on-line manner. The performance of the algorithm is theoretically guaranteed by showing a sub-linear upper bound of the cumulative regret. Numerical results on a real-world methane emission seeking problem are also provided to demonstrate the effectiveness of the proposed algorithm.

Advances of unmanned aircraft system (UAS) technology have spurred a rapid investment of commercial UAS use in broad public domains, such as cargo transport, agriculture support, emergency response, on-demand communication, and infrastructure health monitoring. Urban unmanned aerial transportation that can transport passengers over short distances is also on the way. With the forthcoming dense operations of UAS particularly over urban regions, ensuring airspace safety becomes an urgent issue.

In this paper, we address the issue of congestion in future Unmanned Aerial Vehicle (UAVs) traffic system in uncertain weather. We treat the traffic of UAVs as fluid queues, and introduce models for traffic dynamics at three basic traffic components: single link, tandem link, and merge link. The impact of weather uncertainty is captured as fluctuation of the saturation rate of fluid queue discharge (capacity). The uncertainty is assumed to follow a continuous-time Markov process. We define the resilience of the UAV traffic system as the long-run stability of the traffic queues and the optimal throughput strategy under uncertainties. We derive the necessary and sufficient conditions for the stabilities of the traffic queues in the three basic traffic components. Both conditions can be easily verified in practice. The optimal throughput can be calculated via the stability conditions. Our results offer strong insight and tool for designing flows in the UAV traffic system that is resilient against weather uncertainty.

Due to the rapid development of technologies for small unmanned aircraft systems (sUAS’s), the supply and demand market for sUAS’s is expanding globally. With the great number of sUAS’s ready to fly in civilian airspace, an sUAS aircraft traffic management system that can guarantee the safe and efficient operation of sUAS’s is still absent. In this paper, we propose a control protocol design and analysis method for sUAS traffic management (UTM) which can safely manage a large number of sUAS’s. The benefits of our approach are two-fold: at management level, the effort for monitoring sUAS traffic (authorities) and control/planning for each sUAS (operator/pilot) are both greatly reduced under our framework; and at operational level, the behavior of individual sUAS is guaranteed to follow the restrictions. Mathematical proofs and numerical simulations are presented to demonstrate the proposed method.

The unmanned aerial vehicle (UAV) network offers unprecedented flexibility in the future wireless communication systems. In this paper, a novel cell coverage model is proposed from the perspective of practical system implementations. At first, several UAV millimeter wave communication experiments are implemented. Then experiment results identify the inevitable impact of UAV cell coverage due to the wind-caused attitude perturbation. Furthermore, an equivalent coverage model is derived based on the experimental observation. Finally, the simulations reveal the relationship between the cell coverage and wind perturbation.

Millimeter wave (MMwave) in wireless communications has tremendous frequency bandwidth compared with traditional cellular and WLAN bands. As a promising technique of MMwave, multiple- input-multiple-output (MIMO) techniques, especially beamforming, could greatly improve the system capacity and compensate for the additional path loss in MMwave. However, a perfect beam alignment between the transmitter and receiver needs fine tuning and is vulnerable to different factors such as mobile device movement. In this paper, the widely available motion sensors in mobile devices are employed to mitigate the angle misalignment between the MMwave transmitter and receiver. A new beam alignment mechanism is proposed to reduce the system overhead, given the device attitude information. Experiment and simulation show that the proposed beam tracking mechanism could dramatically reduce the overhead with a minimum computational cost.

The network-induced time delay is one of the major challenges that appear when the design or the analysis of any Networked Control System (NCS) is needed. The delay can be tackled in the network level by performing control of network actions. Since the process of prioritizing the network access is the main contributor of the network-induced time delay, then the efficient way to control the time delay is to improve the scheduling criterion. This paper proposes an optimal scheduling criterion that improves the quality of service for each node in the network and at the same time maintains the stability of the control system. The proposed optimal scheduling protocol is a hybrid scheduling criterion that combines the advantages of the dynamic and the static scheduling protocols. First, the algorithm of the optimal scheduling is discussed. Second, the optimal control-scheduling problem is formulated. The mixed logical-dynamical optimization problem is then solved. Finally, numerical examples and simulation results are introduced and discussed.

The insertion of communication networks in the feedback loops of control systems is a defining feature of modern control systems. These systems are often subject to unknown inputs in a form of disturbances, perturbations, or attacks. The objective of this paper is to analyze and design an observer for networked systems with unknown disturbances and inputs. The network effect can be viewed as either a perturbation or time-delay to the exchanged signals. In this paper, we focus on the time-delay representation of the network. First, we review an Unknown Input Observer (UIO) design for non-networked system from the UIO literature. Second, we derive the dynamics of the UlO-based NCS, also referred to as Networked Unknown Input Observer (NetUIO). Third, we design the NetUIO such that the effect of higher delay order terms are nullified, assuring that the effect of the unknown inputs on the plant state estimation is minimized. Fourth, we derive a bound on the maximum allowable time-delay for the NetUIO. Finally, a numerical example is shown to illustrate the usefulness of the proposed model.

It is necessary to design a robust air transportation network. An experiment based on the real air transportation network is performed to show that algebraic connectivity is a fair measure for network robustness under random failures. Therefor, the goal of this paper is to maximize algebraic connectivity. Some researchers solve the maximization of the algebraic connectivity by choosing the weights for the edges in the graph. Others focus on the best way to add edges in a network in order to optimize the connectivity. In this paper, the authors formulate a new air transportation network model and show that the corresponding algebraic connectivity optimization problem is interesting because the two subproblems of adding edges and choosing edge weights cannot be treated separately. The new problem is formulated and exactly solved in a small air transportation network case. The authors also propose the approximation algorithm in order to achieve better efficiency. For large networks, the semidefinite programming with cluster decomposition is first presented. Moreover, the algebraic connectivity maximization for directed networks is discussed. Simulations are performed for a small-scale case, large-scale problem, and directed network problem.

The continuous growth of air traffic demand, skyrocketing fuel price, and increasing concerns on safety and environmental impact of air transportation necessitate the modernization of the air traffic management (ATM) system in the United States. The design of such a large-scale networked system that involves complex interactions among automation and human operators poses new challenges for many engineering fields. This paper investigates several important facets of the future ATM system from a systems-level point of view. In particular, we develop a hierarchical decentralized decision architecture that can design 4-D (space +time) path plans for a large number of flights while satisfying weather and capacity constraints of the overall system. The proposed planning framework respects preferences of individual flights and encourages information sharing among different decision makers in the system, and thus has a great potential to reduce traffic delays and weather risks while maintaining safety standards. The framework is validated through a large-scale simulation based on real traffic data over the entire airspace of the contiguous United States. We envision that the hierarchical decentralization approach developed in this paper would also provide useful insights into the design of decision and information hierarchies for other large-scale infrastructure systems.

Optimizing nationwide air traffic flow entails computational difficulty as the traffic is generally modeled as a multicommodity network, which involves a huge number of variables. This paper presents a framework that speeds up the optimization. Nationwide air traffic is modeled using a link transmission model (LTM), to which a dual-decomposition method is applied. The large-scale problem is decomposed into a master problem and a number of independent subproblems, which are easy to solve. As a result, the execution of solving the subproblem is parallelizable. A parallel computing framework is based on multiprocessing technology. The master problem is updated on a server, and a client cluster is deployed to finish the subproblems such that the most computationally intensive part of the optimization can be executed in parallel. The server and the clients communicate via Transmission Control Protocol (TCP)/User Datagram Protocol (UDP). An adaptive job allocation method is developed to balance the workload among each client, resulting in maximized utilization of the computing resources. Experiment results show that, compared with an earlier single process solution, the proposed framework considerably increases computational efficiency. The optimization of a 2-h nationwide traffic problem involving 2326 subproblems takes 6 min using ten Dell workstations. The increased computational workload due to the increased number of subproblems can be mitigated by the extension of computer deployment.