2,332 research outputs found
From a Competition for Self-Driving Miniature Cars to a Standardized Experimental Platform: Concept, Models, Architecture, and Evaluation
Context: Competitions for self-driving cars facilitated the development and
research in the domain of autonomous vehicles towards potential solutions for
the future mobility.
Objective: Miniature vehicles can bridge the gap between simulation-based
evaluations of algorithms relying on simplified models, and those
time-consuming vehicle tests on real-scale proving grounds.
Method: This article combines findings from a systematic literature review,
an in-depth analysis of results and technical concepts from contestants in a
competition for self-driving miniature cars, and experiences of participating
in the 2013 competition for self-driving cars.
Results: A simulation-based development platform for real-scale vehicles has
been adapted to support the development of a self-driving miniature car.
Furthermore, a standardized platform was designed and realized to enable
research and experiments in the context of future mobility solutions.
Conclusion: A clear separation between algorithm conceptualization and
validation in a model-based simulation environment enabled efficient and
riskless experiments and validation. The design of a reusable, low-cost, and
energy-efficient hardware architecture utilizing a standardized
software/hardware interface enables experiments, which would otherwise require
resources like a large real-scale test track.Comment: 17 pages, 19 figues, 2 table
Technology assessment of advanced automation for space missions
Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology
Particle Computation: Complexity, Algorithms, and Logic
We investigate algorithmic control of a large swarm of mobile particles (such
as robots, sensors, or building material) that move in a 2D workspace using a
global input signal (such as gravity or a magnetic field). We show that a maze
of obstacles to the environment can be used to create complex systems. We
provide a wide range of results for a wide range of questions. These can be
subdivided into external algorithmic problems, in which particle configurations
serve as input for computations that are performed elsewhere, and internal
logic problems, in which the particle configurations themselves are used for
carrying out computations. For external algorithms, we give both negative and
positive results. If we are given a set of stationary obstacles, we prove that
it is NP-hard to decide whether a given initial configuration of unit-sized
particles can be transformed into a desired target configuration. Moreover, we
show that finding a control sequence of minimum length is PSPACE-complete. We
also work on the inverse problem, providing constructive algorithms to design
workspaces that efficiently implement arbitrary permutations between different
configurations. For internal logic, we investigate how arbitrary computations
can be implemented. We demonstrate how to encode dual-rail logic to build a
universal logic gate that concurrently evaluates and, nand, nor, and or
operations. Using many of these gates and appropriate interconnects, we can
evaluate any logical expression. However, we establish that simulating the full
range of complex interactions present in arbitrary digital circuits encounters
a fundamental difficulty: a fan-out gate cannot be generated. We resolve this
missing component with the help of 2x1 particles, which can create fan-out
gates that produce multiple copies of the inputs. Using these gates we provide
rules for replicating arbitrary digital circuits.Comment: 27 pages, 19 figures, full version that combines three previous
conference article
Towards an infrastructure for preparation and control of intelligent automation systems
In an attempt to handle some of the challenges of modern production, intelligent automation systems offer solutions that are flexible, adaptive, and collaborative. Contrary to traditional solutions, intelligent automation systems emerged just recently and thus lack the supporting tools and infrastructure that traditional systems nowadays take for granted. To support efficient development, commissioning, and control of such systems, this thesis summarizes various lessons learned during years of implementation. Based on what was learned, this thesis investigates key features of infrastructure for modern and flexible intelligent automation systems, as well as a number of important design solutions. For example, an important question is raised whether to decentralize the global state or to give complete access to the main controller.Moreover, in order to develop such systems, a framework for virtual preparation and commissioning is presented, with the main goal to offer support for engineers. As traditional virtual commissioning solutions are not intended for preparing highly flexible, collaborative, and dynamic systems, this framework aims to provide some of the groundwork and point to a direction for fast and integrated preparation and virtual commissioning of such systems.Finally, this thesis summarizes some of the investigations made on planning as satisfiability, in order to evaluate how different methods improve planning performance. Throughout the thesis, an industrial material kitting use case exemplifies presented perspectives, lessons learned, and frameworks
From Cooking Recipes to Robot Task Trees -- Improving Planning Correctness and Task Efficiency by Leveraging LLMs with a Knowledge Network
Task planning for robotic cooking involves generating a sequence of actions
for a robot to prepare a meal successfully. This paper introduces a novel task
tree generation pipeline producing correct planning and efficient execution for
cooking tasks. Our method first uses a large language model (LLM) to retrieve
recipe instructions and then utilizes a fine-tuned GPT-3 to convert them into a
task tree, capturing sequential and parallel dependencies among subtasks. The
pipeline then mitigates the uncertainty and unreliable features of LLM outputs
using task tree retrieval. We combine multiple LLM task tree outputs into a
graph and perform a task tree retrieval to avoid questionable nodes and
high-cost nodes to improve planning correctness and improve execution
efficiency. Our evaluation results show its superior performance compared to
previous works in task planning accuracy and efficiency
From data to applications in the Internet of Things
Con la crescita in complessità delle infrastrutture IT e la pervasivitÃ
degli scenari di Internet of Things (IoT) emerge il bisogno
di nuovi modelli computazionali basati su entità autonome capaci di portare a termine obiettivi
di alto livello interagendo tra loro grazie al supporto di infrastrutture come il Fog Computing,
per la vicinanza alle sorgenti dei dati, e del Cloud Computing per offrire servizi
analitici complessi di back-end in grado di fornire risultati per milioni di utenti.
Questi nuovi scenarii portano a ripensare il modo in cui il software viene progettato
e sviluppato in una prospettiva agile. Le attività dei team di sviluppatori (Dev)
dovrebbero essere strettamente legate alle attività dei team che supportano
il Cloud (Ops) secondo nuove metodologie oggi note come DevOps.
Tuttavia, data la mancanza di astrazioni adeguata a livello di linguaggio di programmazione,
gli sviluppatori IoT sono spesso indotti a seguire approcci di sviluppo bottom-up che spesso
risulta non adeguato ad affrontare la compessità delle applicazione del settore e
l'eterogeneità dei compomenti software che le formano.
Poichè le applicazioni monolitiche del passato appaiono difficilmente scalabili
e gestibili in un ambiente Cloud con molteplici utenti,
molti ritengono necessaria l'adozione di un nuovo stile architetturale,
in cui un'applicazione dovrebbe essere vista come una composizione di micro-servizi,
ciascuno dedicato a uno specifica funzionalità applicativa e ciascuno sotto la
responsabilità di un piccolo team di sviluppatori, dall'analisi del problema
al deployment e al management.
Poichè al momento non si è ancora giunti a una definizione univoca
e condivisa dei microservices e di altri concetti che emergono da IoT e dal Cloud,
nè tantomento alla definzione di linguaggi sepcializzati per questo settore,
la definzione di metamodelli
custom associati alla produzione automatica del software di raccordo con
le infrastrutture potrebbe aiutare un team di sviluppo ad elevare il livello di
astrazione, incapsulando in una software factory aziendale i dettagli implementativi.
Grazie a sistemi di produzione del sofware basati sul Model Driven Software Development (MDSD),
l'approccio top-down attualmente carente può essere recuperato, permettendo
di focalizzare l'attenzione sulla business logic delle applicazioni.
Nella tesi viene
mostrato un esempio di questo possibile approccio, partendo dall'idea che
un'applicazione IoT sia in primo luogo un sistema software distribuito in cui
l'interazione tra componenti attivi (modellati come attori)
gioca un ruolo fondamentale
Advanced Automation for Space Missions
The feasibility of using machine intelligence, including automation and robotics, in future space missions was studied
Wide-Area Surveillance System using a UAV Helicopter Interceptor and Sensor Placement Planning Techniques
This project proposes and describes the implementation of a wide-area surveillance system comprised of a sensor/interceptor placement planning and an interceptor unmanned aerial vehicle (UAV) helicopter. Given the 2-D layout of an area, the planning system optimally places perimeter cameras based on maximum coverage and minimal cost. Part of this planning system includes the MATLAB implementation of Erdem and Sclaroff’s Radial Sweep algorithm for visibility polygon generation. Additionally, 2-D camera modeling is proposed for both fixed and PTZ cases. Finally, the interceptor is also placed to minimize shortest-path flight time to any point on the perimeter during a detection event.
Secondly, a basic flight control system for the UAV helicopter is designed and implemented. The flight control system’s primary goal is to hover the helicopter in place when a human operator holds an automatic-flight switch. This system represents the first step in a complete waypoint-navigation flight control system. The flight control system is based on an inertial measurement unit (IMU) and a proportional-integral-derivative (PID) controller. This system is implemented using a general-purpose personal computer (GPPC) running Windows XP and other commercial off-the-shelf (COTS) hardware. This setup differs from other helicopter control systems which typically use custom embedded solutions or micro-controllers.
Experiments demonstrate the sensor placement planning achieving \u3e90% coverage at optimized-cost for several typical areas given multiple camera types and parameters. Furthermore, the helicopter flight control system experiments achieve hovering success over short flight periods. However, the final conclusion is that the COTS IMU is insufficient for high-speed, high-frequency applications such as a helicopter control system
Space applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 3: ARAMIS overview
An overview of automation, robotics, and machine intelligence systems (ARAMIS) is provided. Man machine interfaces, classification, and capabilities are considered
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