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This paper addresses decision making for networked autonomous vehicles in mobility on demand (MoD) systems. An optimization formulation, termed Pick-up, Delivery, and Rebalancing Problem with Time Windows (PDRPTW), that simultaneously account for the scheduling of the vehicles in response to existing service requests and the rebalancing of them for future requests is presented in the node-based graph with the vehicle working states. Then, the alternating direction method of multipliers (ADMM) decompose the PDRPTW problem into each vehicle's routing. The ADMM framework allows for decomposition of the problem into minimization of total vehicle routing cost and minimization of idle vehicles' waiting cost; the method leads to consensus upon the routing and waiting plans of the vehicles. Numerical examples demonstrate the efficacy and the benefits of the proposed distributed algorithm on instances of Solomon benchmark and rebalancing scenario.

This paper deals with the state estimation and control for camera-assisted landing. Uncertainties in measurement models, such as lens distortion, can adversely affect the camera's target detection and navigational functions. We propose an effective state estimator based on the Interacting Multiple Model filter to account for the uncertainties in the camera measurement model. We further introduce a phase update rule, which determines the feasibility of autonomous landing in a stochastic manner, and filter and control management logic is proposed by which to handle the different phases. The proposed framework is simulated using a multi-rotor type UAV equipped with a camera and rangefinder in the Gazebo simulation environment. The experiments validate the performance of the framework in terms of accuracy in state estimation and control.

This paper proposes the system model for autonomous mobility-on-demand in order to monitor passengers’ transport demand and develop a management system operator. Technical innovations in autonomous mobility and mobile networks’ greater availability have enabled autonomous mobility as a service. The use of shared vehicles for mobility on demand has become increasingly common, and the development of autonomous air mobility has necessitated a more generous system to control the Urban Air Mobility on Demand (UAMoD) availability of vehicle resources. This study shows an example of the autonomous mobility-on-demand system simulated in Daejeon, Korea, with the passenger assignment and vehicle rebalancing system with forecasting future demand.

Organic semiconductor materials for fabricating organic light emitting diodes (OLEDs) have attracted significant attention in the field of novel optical and optoelectronic devices. Particulation of OLEDs’ emitting materials in small-scale has been limited only to tris(8-hydroxyquinoline) aluminum (Alq3) that emits green-light. In this study, we attempted to fabricate, for the first time, red-light emitting nanoparticles of phosphorescent organic semiconductor of bis(1-phenylisoquinoline) (acetylacetonate) iridium (Ir(piq)2(acac)). Rectangular particles with length and thickness of ∼2 μm and ∼50 nm were fabricated with guidance of cetyltrimethylammonium bromide (CTAB) and micro-plates with length and thickness of ∼5 μm and ∼100 nm were fabricated by sodium dodecyl sulfate (SDS). By contrast, single-stranded DNA (ssDNA) induced nano-rods with dimension of ∼400 nm in length and 100 nm in thickness. Hence, the choice of guiding agents resulted in distinctive crystal characteristic so that the nanorods by ssDNAs showed UV absorption with a red-shift in metal-ligand charge transfer (MLCT) by 54 nm whereas the particles by surfactants did 35 nm compared to the dissolved precursor. Higher was the ssDNA-guided nanorods in relative phosphorescence of the intensity at 610 nm over that at 695 nm than the surfactant-guided particles.