2,036 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
Multiservice UAVs for Emergency Tasks in Post-disaster Scenarios
UAVs are increasingly being employed to carry out surveillance, parcel
delivery, communication-support and other specific tasks. Their equipment and
mission plan are carefully selected to minimize the carried load an overall
resource consumption. Typically, several single task UAVs are dispatched to
perform different missions. In certain cases, (part of) the geographical area
of operation may be common to these single task missions (such as those
supporting post-disaster recovery) and it may be more efficient to have
multiple tasks carried out as part of a single UAV mission using common or even
additional specialized equipment.
In this paper, we propose and investigate a joint planning of multitask
missions leveraging a fleet of UAVs equipped with a standard set of accessories
enabling heterogeneous tasks. To this end, an optimization problem is
formulated yielding the optimal joint planning and deriving the resulting
quality of the delivered tasks. In addition, a heuristic solution is developed
for large-scale environments to cope with the increased complexity of the
optimization framework. The developed joint planning of multitask missions is
applied to a specific post-disaster recovery scenario of a flooding in the San
Francisco area. The results show the effectiveness of the proposed solutions
and the potential savings in the number of UAVs needed to carry out all the
tasks with the required level of quality
Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing
Autonomous micro aerial vehicles still struggle with fast and agile
maneuvers, dynamic environments, imperfect sensing, and state estimation drift.
Autonomous drone racing brings these challenges to the fore. Human pilots can
fly a previously unseen track after a handful of practice runs. In contrast,
state-of-the-art autonomous navigation algorithms require either a precise
metric map of the environment or a large amount of training data collected in
the track of interest. To bridge this gap, we propose an approach that can fly
a new track in a previously unseen environment without a precise map or
expensive data collection. Our approach represents the global track layout with
coarse gate locations, which can be easily estimated from a single
demonstration flight. At test time, a convolutional network predicts the poses
of the closest gates along with their uncertainty. These predictions are
incorporated by an extended Kalman filter to maintain optimal
maximum-a-posteriori estimates of gate locations. This allows the framework to
cope with misleading high-variance estimates that could stem from poor
observability or lack of visible gates. Given the estimated gate poses, we use
model predictive control to quickly and accurately navigate through the track.
We conduct extensive experiments in the physical world, demonstrating agile and
robust flight through complex and diverse previously-unseen race tracks. The
presented approach was used to win the IROS 2018 Autonomous Drone Race
Competition, outracing the second-placing team by a factor of two.Comment: 6 pages (+1 references
Congestion-aware Multi-modal Delivery Systems Utilizing Drones
Multi-modal delivery systems are a promising solution to the challenges posed
by the increasing demand of e-commerce. Due to the potential benefit drones can
have on logistics networks such as delivery systems, some countries have taken
steps towards integrating drones into their airspace. In this paper we aim to
quantify this potential by developing a mathematical model for a multi-modal
delivery system composed of trucks and drones. We propose an optimization
formulation that can be efficiently solved in order to design socially-optimal
routing and allocation policies. We incorporate both societal cost in terms of
road congestion and parcel delivery latency in our formulation. Our model is
able to quantify the effect drones have on mitigating road congestion, and can
solve for the path routing needed to minimize the chosen objective. To
accurately capture the effect of stopping trucks on road latency, we use SUMO
simulations and derive a mathematical latency function for roads shared by
trucks and cars. Based on this, we show that the proposed framework is
computationally feasible to scale due to its reliance on convex quadratic
optimization techniques
The Application of Drones in City Logistics Concepts
With the rise of city logistics (CL) problems in the last three decades, various methods, approaches, solutions, and initiatives were analyzed and proposed for making logistics in urban areas more sustainable. The most analyzed and promising solutions are those that take into account cooperation among logistics providers and consolidation of the flow of goods. Furthermore, technological innovations enable the implementation of modern vehicles/equipment in order to make CL solutions sustainable. For several years, drone-based delivery has attracted lots of attention in scientific research, but there is a serious gap in the literature regarding the application of drones in CL concepts. The goal of this paper is to analyze four CL concepts that differ in consolidation type, transformation degree of flow of goods (direct and indirect, multi-echelon flows), and the role of drones. Two of the analyzed concepts are novel, which is the main contribution of the paper. The performances of the analyzed concepts are compared to the performances of the traditional delivery model – using only trucks without prior flow consolidation. The results indicate that CL concepts which combine different consolidation models and drones in the last phase of the delivery could stand out as a sustainable CL solution
Voliro: An Omnidirectional Hexacopter With Tiltable Rotors
Extending the maneuverability of unmanned areal vehicles promises to yield a
considerable increase in the areas in which these systems can be used. Some
such applications are the performance of more complicated inspection tasks and
the generation of complex uninterrupted movements of an attached camera. In
this paper we address this challenge by presenting Voliro, a novel aerial
platform that combines the advantages of existing multi-rotor systems with the
agility of omnidirectionally controllable platforms. We propose the use of a
hexacopter with tiltable rotors allowing the system to decouple the control of
position and orientation. The contributions of this work involve the mechanical
design as well as a controller with the corresponding allocation scheme. This
work also discusses the design challenges involved when turning the concept of
a hexacopter with tiltable rotors into an actual prototype. The agility of the
system is demonstrated and evaluated in real- world experiments.Comment: Submitted to Robotics and Automation Magazin
SOTER: A Runtime Assurance Framework for Programming Safe Robotics Systems
The recent drive towards achieving greater autonomy and intelligence in
robotics has led to high levels of complexity. Autonomous robots increasingly
depend on third party off-the-shelf components and complex machine-learning
techniques. This trend makes it challenging to provide strong design-time
certification of correct operation.
To address these challenges, we present SOTER, a robotics programming
framework with two key components: (1) a programming language for implementing
and testing high-level reactive robotics software and (2) an integrated runtime
assurance (RTA) system that helps enable the use of uncertified components,
while still providing safety guarantees. SOTER provides language primitives to
declaratively construct a RTA module consisting of an advanced,
high-performance controller (uncertified), a safe, lower-performance controller
(certified), and the desired safety specification. The framework provides a
formal guarantee that a well-formed RTA module always satisfies the safety
specification, without completely sacrificing performance by using higher
performance uncertified components whenever safe. SOTER allows the complex
robotics software stack to be constructed as a composition of RTA modules,
where each uncertified component is protected using a RTA module.
To demonstrate the efficacy of our framework, we consider a real-world
case-study of building a safe drone surveillance system. Our experiments both
in simulation and on actual drones show that the SOTER-enabled RTA ensures the
safety of the system, including when untrusted third-party components have bugs
or deviate from the desired behavior
Embedded Payload Solutions in UAVs for Medium and Small Package Delivery
Investigations about the feasibility of delivery systems with unmanned aerial vehicles (UAVs) or drones have been recently expanded, owing to the exponential demand for goods to be delivered in the recent years, which has been further increased by the COVID-19 pandemic. UAV delivery can provide new contactless delivery strategies, in addition to applications for medical items, such as blood, medicines, or vaccines. The safe delivery of goods is paramount for such applications, which is facilitated if the payload is embedded in the main drone body. In this paper, we investigate payload solutions for medium and small package delivery (up to 5 kg) with a medium-sized UAV (maximum takeoff of less than 25 kg), focusing on (i) embedded solutions (packaging hosted in the drone fuselage), (ii) compatibility with transportation of medical items, and (iii) user-oriented design (usability and safety). We evaluate the design process for possible payload solutions, from an analysis of the package design (material selection, shape definition, and product industrialization) to package integration with the drone fuselage (possible solutions and comparison of quick-release systems). We present a prototype for an industrialized package, a right prism with an octagonal section made of high-performance double-wall cardboard, and introduce a set of concepts for a quick-release system, which are compared with a set of six functional parameters (mass, realization, accessibility, locking, protection, and resistance). Further analyses are already ongoing, with the aim of integrating monitoring and control capabilities into the package design to assess the condition of the delivered goods during transportation
Key technologies for safe and autonomous drones
Drones/UAVs are able to perform air operations that are very difficult to be performed by manned aircrafts. In addition, drones' usage brings significant economic savings and environmental benefits, while reducing risks to human life. In this paper, we present key technologies that enable development of drone systems. The technologies are identified based on the usages of drones (driven by COMP4DRONES project use cases). These technologies are grouped into four categories: U-space capabilities, system functions, payloads, and tools. Also, we present the contributions of the COMP4DRONES project to improve existing technologies. These contributions aim to ease drones’ customization, and enable their safe operation.This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 826610. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Austria, Belgium, Czech Republic, France, Italy, Latvia, Netherlands. The total project budget is 28,590,748.75 EUR (excluding ESIF partners), while the requested grant is 7,983,731.61 EUR to ECSEL JU, and 8,874,523.84 EUR of National and ESIF Funding. The project has been started on 1st October 2019
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