78 research outputs found
Autonomous Recharging and Flight Mission Planning for Battery-operated Autonomous Drones
Autonomous drones (also known as unmanned aerial vehicles) are increasingly
popular for diverse applications of light-weight delivery and as substitutions
of manned operations in remote locations. The computing systems for drones are
becoming a new venue for research in cyber-physical systems. Autonomous drones
require integrated intelligent decision systems to control and manage their
flight missions in the absence of human operators. One of the most crucial
aspects of drone mission control and management is related to the optimization
of battery lifetime. Typical drones are powered by on-board batteries, with
limited capacity. But drones are expected to carry out long missions. Thus, a
fully automated management system that can optimize the operations of
battery-operated autonomous drones to extend their operation time is highly
desirable. This paper presents several contributions to automated management
systems for battery-operated drones: (1) We conduct empirical studies to model
the battery performance of drones, considering various flight scenarios. (2) We
study a joint problem of flight mission planning and recharging optimization
for drones with an objective to complete a tour mission for a set of sites of
interest in the shortest time. This problem captures diverse applications of
delivery and remote operations by drones. (3) We present algorithms for solving
the problem of flight mission planning and recharging optimization. We
implemented our algorithms in a drone management system, which supports
real-time flight path tracking and re-computation in dynamic environments. We
evaluated the results of our algorithms using data from empirical studies. (4)
To allow fully autonomous recharging of drones, we also develop a robotic
charging system prototype that can recharge drones autonomously by our drone
management system
An Overview of Drone Energy Consumption Factors and Models
At present, there is a growing demand for drones with diverse capabilities
that can be used in both civilian and military applications, and this topic is
receiving increasing attention. When it comes to drone operations, the amount
of energy they consume is a determining factor in their ability to achieve
their full potential. According to this, it appears that it is necessary to
identify the factors affecting the energy consumption of the unmanned air
vehicle (UAV) during the mission process, as well as examine the general
factors that influence the consumption of energy. This chapter aims to provide
an overview of the current state of research in the area of UAV energy
consumption and provide general categorizations of factors affecting UAV's
energy consumption as well as an investigation of different energy models
A Novel Path Planning Optimization Algorithm for Semi-Autonomous UAV in Bird Repellent Systems Based in Particle Swarm Optimization
Bird damage to fruit crops causes significant monetary losses to farmers annually. The
application of traditional bird repelling methods such as bird cannons and tree netting
became inefficient in the long run, keeping high maintenance and reduced mobility. Due to
their versatility, Unmanned Aerial Vehicles (UAVs) can be beneficial to solve this problem.
However, due to their low battery capacity that equals low flight duration, it is necessary to
evolve path planning optimization.
A path planning optimization algorithm of UAVs based on Particle Swarm Optimization
(PSO) is presented in this dissertation. This technique was used due to the need for an easy
implementation optimization algorithm to start the initial tests. The PSO algorithm is
simple and has few control parameters while maintaining a good performance. This path
planning optimization algorithm aims to manage the drone's distance and flight time,
applying optimization and randomness techniques to overcome the disadvantages of the
traditional systems. The proposed algorithm's performance was tested in three study cases:
two of them in simulation to test the variation of each parameter and one in the field to test
the influence on battery management and height influence. All cases were tested in the three
possible situations: same incidence rate, different rates, and different rates with no bird
damage to fruit crops.
The proposed algorithm presents promising results with an outstanding reduced average
error in the total distance for the path planning obtained and low execution time. However,
it is necessary to point out that the path planning optimization algorithm may have difficulty
finding a suitable solution if there is a bad ratio between the total distance for path planning
and points of interest. The field tests were also essential to understand the algorithm's
behavior of the path planning algorithm in the UAV, showing that there is less energy
discharged with fewer points of interest, but that do not correlates with the flight time. Also,
there is no association between the maximum horizontal speed and the flight time, which
means that the function to calculate the total distance for path planning needs to be
adjusted.Anualmente, os danos causados pelas aves em pomares criam perdas monetárias
significativas aos agricultores. A aplicação de métodos tradicionais de dispersão de aves,
como canhões repelentes de aves e redes nas árvores, torna-se ineficiente a longo prazo,
sendo ainda de alta manutenção e de mobilidade reduzida. Devido à sua versatilidade, os
VeÃculos Aéreos Não Tripulados (VANT) podem ser benéficos para resolver este problema.
No entanto, devido à baixa capacidade das suas baterias, que se traduz num baixo tempo de
voo, é necessário otimizar o planeamento dos caminhos.
Nesta dissertação, é apresentado um algoritmo de otimização para planeamento de
caminhos para VANT baseado no Particle Swarm Optimization (PSO). Para se iniciarem os
primeiros testes do algoritmo proposto, a técnica utilizada foi a supracitada devido Ã
necessidade de um algoritmo de otimização fácil de implementar. O algoritmo PSO é
simples e possuà poucos parâmetros de controlo, mantendo um bom desempenho. Este
algoritmo de otimização de planeamento de caminhos propõe-se a gerir a distância e o
tempo de voo do drone, aplicando técnicas de otimização e de aleatoriedade para superar a
sua desvantagem relativamente aos sistemas tradicionais. O desempenho do algoritmo de
planeamento de caminhos foi testado em três casos de estudo: dois deles em simulação para
testar a variação de cada parâmetro e outro em campo para testar a capacidade da bateria.
Todos os casos foram testados nas três situações possÃveis: mesma taxa de incidência, taxas
diferentes e taxas diferentes sem danos de aves.
Os resultados apresentados pelo algoritmo proposto demonstram um erro médio muto
reduzido na distância total para o planeamento de caminhos obtido e baixo tempo de
execução. Porém, é necessário destacar que o algoritmo pode ter dificuldade em encontrar
uma solução adequada se houver uma má relação entre a distância total para o planeamento
de caminhos e os pontos de interesse. Os testes de campo também foram essenciais para
entender o comportamento do algoritmo na prática, mostrando que há menos energia
consumida com menos pontos de interesse, sendo que este parâmetro não se correlaciona
com o tempo de voo. Além disso, não há associação entre a velocidade horizontal máxima e
o tempo da missão, o que significa que a função de cálculo da distância total para o
planeamento de caminhos requer ser ajustada
Towards Autonomous and Safe Last-mile Deliveries with AI-augmented Self-driving Delivery Robots
In addition to its crucial impact on customer satisfaction, last-mile
delivery (LMD) is notorious for being the most time-consuming and costly stage
of the shipping process. Pressing environmental concerns combined with the
recent surge of e-commerce sales have sparked renewed interest in automation
and electrification of last-mile logistics. To address the hurdles faced by
existing robotic couriers, this paper introduces a customer-centric and
safety-conscious LMD system for small urban communities based on AI-assisted
autonomous delivery robots. The presented framework enables end-to-end
automation and optimization of the logistic process while catering for
real-world imposed operational uncertainties, clients' preferred time
schedules, and safety of pedestrians. To this end, the integrated optimization
component is modeled as a robust variant of the Cumulative Capacitated Vehicle
Routing Problem with Time Windows, where routes are constructed under uncertain
travel times with an objective to minimize the total latency of deliveries
(i.e., the overall waiting time of customers, which can negatively affect their
satisfaction). We demonstrate the proposed LMD system's utility through
real-world trials in a university campus with a single robotic courier.
Implementation aspects as well as the findings and practical insights gained
from the deployment are discussed in detail. Lastly, we round up the
contributions with numerical simulations to investigate the scalability of the
developed mathematical formulation with respect to the number of robotic
vehicles and customers
Innovative solutions in last mile delivery: concepts, practices, challenges, and future directions
In the last decade, e-commerce has been growing consistently. Fostered by the covid pandemic, online retail has grown exponentially, particularly in industries including food, clothing, groceries and many others. This growth in online retailing activities has raised critical logistic challenges, especially in the last leg of the distribution, commonly referred to as the Last Mile. For instance, traditional truck-based home delivery has reached its limit within metropolitan areas and can no longer be an effective delivery method. Driven by technological progress, several other logistic solutions have been deployed as innovative alternatives to deliver parcels. This includes delivery by drones, smart parcel stations, robots, and crowdsourcing, among others. In this setting, this paper aims to provide a comprehensive review and analysis of the latest trends in last-mile delivery solutions from both industry and academic perspectives (see Figure 1 for overview). We use a content analysis literature review to analyse over 80 relevant publications, derive the necessary features of the latest innovation in the last mile delivery, and point out their different maturity levels and the related theoretical and operational challenges
Path Planning with Drones at CSP plants
The goal of this work is to apply mathematics knowledge and skills to efficiently solve
a practical problem posed by the industry. We study an actual problem related to the
inspection of Concentrated Solar Power (CSP) plants. Due to the big extension of
solar fields, Unmanned Aerial Vehicles (UAV), commonly called drones, are used to
inspect all the tubes of the CSP plant. We introduce a new problem, named the drone
CSP inspection problem, that aims the computation of the tours to be performed by
the drone in order to cover the CSP plant so that some penalization function is min imized. Specifically, we take into account two objective functions: the total time or
the number of refills. First, we model the energy consumption of the UAV and the
individual time inspection costs in a realistic fashion and use them as inputs for the
procedures described. We also propose several formulations adapting classical optimization problems. In addition, we prove that this particular problem is NP-complete
and develop some heuristics. An extensive comparison against the current approach
adopted by the industry shows best performance of our algorithms, saving a considerable amount of time for inspection.El objetivo de este trabajo es aplicar conocimiento y habilidades matemáticas para resolver eficientemente un problema práctico propuesto por la industria. Estudiaremos
un problema real relacionado con la inspección de plantas de concentración solar de
potencia (CSP). Debido a la gran extensión de los campos solares se utilizan vehÃculos
aéreos no pilotados (UAV), comúnmente llamados drones, para inspeccionar todos
los tubos de la planta CSP. Introduciremos un nuevo problema, el problema de inspección CSP con drones, donde se propone calcular las trayectorias a realizar por
el dron de manera que se cubra la planta CSP mientras se minimiza una cierta función de penalización. Concretamente, tendremos en cuenta dos funciones objetivo:
el tiempo total de inspección y el número de recargas que el dron necesita. Primero,
modelaremos el consumo de energÃa del UAV y los tiempos individuales de inspección de forma realista y los usaremos como entrada de los procedimientos descritos.
Propondremos varias formulaciones adaptando problemas de optimización clásicos.
Además, probaremos que este problema particular es NP-completo y desarrollaremos
algunos heurÃsticos. Comparando éstos con procedimiento actual adoptado por la industria, probamos que nuestros algoritmos tienen un mayor rendimiento, ahorrando
una considerable cantidad de tiempo total de inspección.Universidad de Sevilla. Grado en Matemáticas y EstadÃstic
HYBRID ROUTING MODELS UTILIZING TRUCKS OR SHIPS TO LAUNCH DRONES
Technological advances for unmanned aerial vehicles, commonly referred to as drones, have opened the door to a number of new and interesting applications in areas including military, healthcare, communications, cinematography, emergency response, and logistics. However, limitations due to battery capacity, maximum take-off weight, finite range of wireless communications, and legal regulations have restricted the effective operational range of drones in many practical applications.
Several hybrid operational models involving one or more drones launching from a larger vehicle, which may be a ship, truck, or airplane, have emerged to help mitigate these range limitations. In particular, the drones utilize the larger vehicle as both a mobile depot and a recharging or refueling platform. In this dissertation, we describe routing models that leverage the tandem of one or more drones with a larger vehicle. In these models, there is generally a set of targets that should be visited in an efficient (usually time-minimizing) manner. By using multiple vehicles, these targets may be visited in parallel thereby reducing the total time to visit all targets.
The vehicle routing problem with drones (VRPD) and traveling salesman problem with a drone (TSP-D) consider hybrid truck-and-drone models of delivery, where the goal is to minimize the time required to deliver a set of packages to their respective customers and return the truck(s) and drone(s) to the origin depot. In both problems, the drone can carry one homogeneous package at a time. Theoretical analysis, exact solution methods, heuristic solution methods, and computational results are presented. In the mothership and drone routing problem (MDRP), we consider the case where the larger launch vehicle is free to move in Euclidean space (the open seas) and launch a drone to visit one target location at a time, before returning to the ship to pick up new cargo or refuel. The mothership and high capacity drone routing problem (MDRP-HC) is a generalization of the mothership and drone routing problem, which allows the drone to visit multiple targets consecutively before returning to the ship. MDRP and MDRP-HC contain elements of both combinatorial optimization and continuous optimization. In the multi-visit drone routing problem (MVDRP), a drone can visit multiple targets consecutively before returning to the truck, subject to energy constraints that take into account the weight of packages carried by the drone
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