Autonomous Vehicles and Commercial Real Estate

Hardly a day goes by without the release of a handful of news stories about autonomous vehicles (or AVs for short). The proverbial “tipping point” of awareness has been reached in the public consciousness as AV technology is quickly becoming the new focus of firms from Silicon Valley to Detroit and beyond. Automation has, and will continue to have far-reaching implications for many human activities, but for driving, the technology is here. Google has been in talks with automaker Ford (1), Elon Musk has declared that Tesla will have the appropriate technology in two years (2), GM is paired-up with Lyft (3), Uber is in development-mode (4), Microsoft and Volvo have announced a partnership (5), Apple has been piloting its top-secret project “Titan” (6), Toyota is working on its own technology (7), as is BMW (8). Audi (9) made a splash by sending a driverless A7 concept car 550 miles from San Francisco to Las Vegas just in time to roll-into the 2016 Consumer Electronics Show. Clearly, the race is on.

Extraction system and determination of PAH in seawater and it implementation in submarine vehicles

[EN]Policyclyc aromatic hydrocarbons (PAHs) are a potential risk for human health and marine biota in general that make necessary the monitorization of them. A miniaturized extraction system capable to extract PAHs from seawater was developed and optimized with the objective of implement it in an oceanographic buoy in the future. An analytical method was optimized by high performance liquid chromatography for the determination of extracted PAHs by the extraction system. The analytical method was validated and applicated to real samples of differents points of Gran Canaria. The method has enough sensitivity to detect and quantify concentrations below the concentrations established in the legislation. In some places where samples were taken some compounds exceed the legislation while other compounds follow it

Osservatore e controllore sensorless ad elevate prestazioni per propulsione elettrica di Unmanned Aerial Vehicles

Negli ultimi anni, tra le varie tecnologie che hanno acquisito una sempre maggiore popolarità e diffusione, una di particolare rilevanza è quella degli Unmanned Aerial Vehicles. Di questi velivoli, quelli che stanno riscuotendo maggiore successo sono i multirotori, alimentati esclusivamente da azionamenti elettrici disposti in opportune posizioni della struttura. Particolari sforzi sono stati recentemente dedicati al miglioramento di questa tecnologia in termini di efficienza e precisione, tuttavia quasi sempre si trascura la vitale importanza dello sfruttamento efficiente dei motori elettrici. La tecnica di pilotaggio adottata nella quasi totalità dei casi per questi componenti è il BLDC sensorless, anche se la struttura si dimostra spesso essere PMSM, dunque inadatta all’uso di questa strategia. Il controllo ideale per i PMSM risulterebbe essere FOC, tuttavia per l'implementazione sensorless molti aspetti scontati nel BLDC devono essere affrontati, in particolare bisogna risolvere problemi di osservazione e identificazione. Durante la procedura di avviamento, efficienti strategie di self-commissioning vengono adottate per l’identificazione dei parametri elettrici. Per la fase di funzionamento nominale viene proposto un osservatore composto da diversi componenti interfacciati tra loro tramite un filtro complementare, il tutto al fine di ottenere una stima di posizione e velocità depurata dai disturbi. In merito al funzionamento in catena chiusa, vengono esposte valutazioni preliminari sulla stabilità e sulla qualità del controllo. Infine, per provare la validità degli algoritmi proposti, vengono mostrati i risultati delle prove sperimentali condotte su un tipico azionamento per UAV, pilotato da una scheda elettronica progettata appositamente per l’applicazione in questione. Vengono fornite inoltre indicazioni sull’implementazione degli algoritmi studiati, in particolare considerazioni sull’uso delle operazioni a virgola fissa per velocizzare l'esecuzione.

Electric Vehicles as Grid Resources

Thesis (Ph.D.)--University of Washington, 2016-08

Using augmented state Kalman filter to localize multi autonomous underwater vehicles

The present paper describes a system for the construction of visual maps ("mosaics") and motion estimation for a set of AUVs (Autonomous Underwater Vehicles). Robots are equipped with down-looking camera which is used to estimate their motion with respect to the seafloor and built an online mosaic. As the mosaic increases in size, a systematic bias is introduced in its alignment, resulting in an erroneous output. The theoretical concepts associated with the use of an Augmented State Kalman Filter (ASKF) were applied to optimally estimate both visual map and the fleet position.

DESIGN, FABRICATION, AND PERFORMANCE CHARACTERIZATION OF MULTIFUNCTIONAL STRUCTURES TO HARVEST SOLAR ENERGY FOR FLAPPING WING AERIAL VEHICLES

Flapping Wing Aerial Vehicles (FWAVs) have the capability to combine the benefits of both fixed wing vehicles and rotary vehicles. However, flight time is limited due to limited on-board energy storage capacity. For most Unmanned Aerial Vehicle (UAV) operators, frequent recharging of the batteries is not ideal due to lack of nearby electrical outlets. This imposes serious limitations on FWAV flights. The approach taken to extend the flight time of UAVs was to integrate photovoltaic solar cells onto different structures of the vehicle to harvest and use energy from the sun. Integration of the solar cells can greatly improve the energy capacity of an UAV; however, this integration does effect the performance of the UAV and especially FWAVs. The integration of solar cells affects the ability of the vehicle to produce the aerodynamic forces necessary to maintain flight. This PhD dissertation characterizes the effects of solar cell integration on the performance of a FWAV. Robo Raven, a recently developed FWAV, is used as the platform for this work. An additive manufacturing technique was developed to integrate photovoltaic solar cells into the wing and tail structures of the vehicle. An approach to characterizing the effects of solar cell integration to the wings, tail, and body of the UAV is also described. This approach includes measurement of aerodynamic forces generated by the vehicle and measurements of the wing shape during the flapping cycle using Digital Image Correlation. Various changes to wing, body, and tail design are investigated and changes in performance for each design are measured. The electrical performance from the solar cells is also characterized. A new multifunctional performance model was formulated that describes how integration of solar cells influences the flight performance. Aerodynamic models were developed to describe effects of solar cell integration force production and performance of the FWAV. Thus, performance changes can be predicted depending on changes in design. Sensing capabilities of the solar cells were also discovered and correlated to the deformation of the wing. This demonstrated that the solar cells were capable of: (1) Lightweight and flexible structure to generate aerodynamic forces, (2) Energy harvesting to extend operational time and autonomy, (3) Sensing of an aerodynamic force associated with wing deformation. Finally, different flexible photovoltaic materials with higher efficiencies are investigated, which enable the multifunctional wings to provide enough solar power to keep the FWAV aloft without batteries as long as there is enough sunlight to power the vehicle.

DISPATCHING AND RELOCATION OF EMERGENCY VEHICLES ON FREEWAYS: THEORIES AND APPLICATIONS

Resource allocation decisions are made to serve the current emergency without knowing which future emergency will be occurring. Different ordered combinations of emergencies result in different performance outcomes. Even though future decisions can be anticipated with scenarios, previous models follow an assumption that events over a time interval are independent. This dissertation follows an assumption that events are interdependent, because speed reduction and rubbernecking due to an initial incident provoke secondary incidents. The misconception that secondary incidents are not common has resulted in overlooking a look-ahead concept. This dissertation is a pioneer in relaxing the structural assumptions of independency during the assignment of emergency vehicles. When an emergency is detected and a request arrives, an appropriate emergency vehicle is immediately dispatched. We provide tools for quantifying impacts based on fundamentals of incident occurrences through identification, prediction, and interpretation of secondary incidents. A proposed online dispatching model minimizes the cost of moving the next emergency unit, while making the response as close to optimal as possible. Using the look-ahead concept, the online model flexibly re-computes the solution, basing future decisions on present requests. We introduce various online dispatching strategies with visualization of the algorithms, and provide insights on their differences in behavior and solution quality. The experimental evidence indicates that the algorithm works well in practice. After having served a designated request, the available and/or remaining vehicles are relocated to a new base for the next emergency. System costs will be excessive if delay regarding dispatching decisions is ignored when relocating response units. This dissertation presents an integrated method with a principle of beginning with a location phase to manage initial incidents and progressing through a dispatching phase to manage the stochastic occurrence of next incidents. Previous studies used the frequency of independent incidents and ignored scenarios in which two incidents occurred within proximal regions and intervals. The proposed analytical model relaxes the structural assumptions of Poisson process (independent increments) and incorporates evolution of primary and secondary incident probabilities over time. The mathematical model overcomes several limiting assumptions of the previous models, such as no waiting-time, returning rule to original depot, and fixed depot. The temporal locations flexible with look-ahead are compared with current practice that locates units in depots based on Poisson theory. A linearization of the formulation is presented and an efficient heuristic algorithm is implemented to deal with a large-scale problem in real-time.

Nanostructured lipid carriers as vehicles for the delivery of poorly water-soluble drugs

186 p.

Development of polymeric drug delivery vehicles for targeted cancer therapy

The aim of my Ph. D. thesis is to generalize a method for targeted anti-cancer drug delivery. Hydrophilic polymer-drug conjugates involve complicated synthesis; drug-encapsulated polymeric nanoparticles limit the loading capability of payloads. This thesis introduces the concept of nanoconjugates to overcome difficulties in synthesis and formulation. Drugs with hydroxyl group are able to initiate polyester synthesis in a regio- and chemo- selective way, with the mediation of ligand-tunable Zinc catalyst. Herein, three anti-cancer drugs are presented to demonstrate the high efficiency and selectivity in the method (Chapter 2-4). The obtained particles are stable in salt solution, releasing drugs over weeks in controlled manner. With the conjugation of aptamer, particles are capable to target prostate cancer cells in vitro. These results open the gateway to evaluate the in vivo efficacy of nanoconjugates for target cancer therapy (Chapter 5). Mechanism study of the polymerization leads to the discovery of chemosite selective synthesis of prodrugs with acrylate functional groups. Functional copolymer-drug conjugates will expand the scope of nanoconjugates (Chapter 6). Liposome-aptamer targeting drug delivery vehicle is well studied to achieve reversible cell-specific delivery of non-hydoxyl drugs e.g. cisplatin (Chapter 7). New monomers and polymerization mechanisms are explored for polyester in order to synthesize nanoconjugates with variety on properties (Chapter 8). Initial efforts to apply this type of prodrugs will be focused on the preparation of hydrogels for stem cell research (Chapter 9).

Development of a Real-Time Hierarchical 3D Path Planning Algorithm for Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) frequently operate in partially or entirely unknown environments. As the vehicle traverses the environment and detects new obstacles, rapid path replanning is essential to avoid collisions. This thesis presents a new algorithm called Hierarchical D* Lite (HD*), which combines the incremental algorithm D* Lite with a novel hierarchical path planning approach to replan paths sufficiently fast for real-time operation. Unlike current hierarchical planning algorithms, HD* does not require map corrections before planning a new path. Directional cost scale factors, path smoothing, and Catmull-Rom splines are used to ensure the resulting paths are feasible. HD* sacrifices optimality for real-time performance. Its computation time and path quality are dependent on the map size, obstacle density, sensor range, and any restrictions on planning time. For the most complex scenarios tested, HD* found paths within 10% of optimal in under 35 milliseconds.

Energy Management of a Battery-Ultracapacitor Hybrid Energy Storage System in Electric Vehicles

Electric vehicle (EV) batteries tend to have accelerated degradation due to high peak power and harsh charging/discharging cycles during acceleration and deceleration periods, particularly in urban driving conditions. An oversized energy storage system (ESS) can meet the high power demands; however, it suffers from increased size, volume and cost. In order to reduce the overall ESS size and extend battery cycle life, a battery-ultracapacitor (UC) hybrid energy storage system (HESS) has been considered as an alternative solution. In this work, we investigate the optimized configuration, design, and energy management of a battery-UC HESS. One of the major challenges in a HESS is to design an energy management controller for real-time implementation that can yield good power split performance. We present the methodologies and solutions to this problem in a battery-UC HESS with a DC-DC converter interfacing with the UC and the battery. In particular, a multi-objective optimization problem is formulated to optimize the power split in order to prolong the battery lifetime and to reduce the HESS power losses. This optimization problem is numerically solved for standard drive cycle datasets using Dynamic Programming (DP). Trained using the DP optimal results, an effective real-time implementation of the optimal power split is realized based on Neural Network (NN). This proposed online energy management controller is applied to a midsize EV model with a 360V/34kWh battery pack and a 270V/203Wh UC pack. The proposed online energy management controller effectively splits the load demand with high power efficiency and also effectively reduces the battery peak current. More importantly, a 38V-385Wh battery and a 16V-2.06Wh UC HESS hardware prototype and a real-time experiment platform has been developed. The real-time experiment results have successfully validated the real-time implementation feasibility and effectiveness of the real-time controller design for the battery-UC HESS. A battery State-of-Health (SoH) estimation model is developed as a performance metric to evaluate the battery cycle life extension effect. It is estimated that the proposed online energy management controller can extend the battery cycle life by over 60%.

RISK-BASED MULTIOBJECTIVE PATH PLANNING AND DESIGN OPTIMIZATION FOR UNMANNED AERIAL VEHICLES

Safe operation of unmanned aerial vehicles (UAVs) over populated areas requires reducing the risk posed by a UAV if it crashed during its operation. We considered several types of UAV risk-based path planning problems and developed techniques for estimating the risk to third parties on the ground. The path planning problem requires making trade-offs between risk and flight time. Four optimization approaches for solving the problem were tested; a network-based approach that used a greedy algorithm to improve the original solution generated the best solutions with the least computational effort. Additionally, an approach for solving a combined design and path planning problems was developed and tested. This approach was extended to solve robust risk-based path planning problem in which uncertainty about wind conditions would affect the risk posed by a UAV.

Mission and Scenario Planning for Unmanned Aerial Vehicles (Path Planning and Collision Avoidance Systems)

As unmanned autonomous vehicles (UAVs) are being widely utilized in military and civil applications, concerns are growing about mission safety and how to integrate dierent phases of mission design. One important barrier to a coste ective and timely safety certication process for UAVs is the lack of a systematic approach for bridging the gap between understanding high-level commander/pilot intent and implementation of intent through low-level UAV behaviors. In this thesis we demonstrate an entire systems design process for a representative UAV mission, beginning from an operational concept and requirements and ending with a simulation framework for segments of the mission design, such as path planning and decision making in collision avoidance. In this thesis, we divided this complex system into sub-systems; path planning, collision detection and collision avoidance. We then developed software modules for each sub-system

A Ride-Sharing System with High Capacity Vehicles and Flexible Meeting Points

In many major cities, fixed route transit systems such as bus and rail serve millions of trips per day. These systems have people collect at common locations (the station or stop), and board at common times (for example according to a predetermined schedule or headway). By using common service locations and times, these modes can consolidate many trips that have similar origins and destinations or overlapping routes. However, the routes are not sensitive to changing travel patterns, and have no way of identifying which trips are going unserved, or are poorly served, by the existing routes. On the opposite end of the spectrum, personal modes of transportation, such as a private vehicle or taxi, offer service to and from the exact origin and destination of a rider, at close to exactly the time they desire to travel. Despite the apparent increased convenience to users, the presence of a large number of small vehicles results in a disorganized, and potentially congested road network during high demand periods. The focus of the research presented in this paper is to develop a system that possesses both the on-demand nature of a personal mode, with the efficiency of shared modes. In this system, users submit their request for travel, but are asked to make small compromises in their origin and destination location by walking to a nearby meeting point, as well as slightly modifying their time of travel, in order to accommodate other passengers. Because the origin and destination location of the request can be adjusted, this is a more general case of the Dial-a-Ride problem with time windows. The solution methodology uses a graph clustering algorithm coupled with a greedy insertion technique. A case study is presented using actual requests for taxi trips in Washington DC, and shows a significant decrease in the number of vehicles required to serve the demand.