2,003 research outputs found
Analyse inter-critère basée sur les fonctions de croyance pour l'analyse GPS
International audienceIn this paper we present an application of a new Belief Function-based Inter-Criteria Analysis (BF-ICrA) approach for Global Positioning System (GPS) Surveying Problems (GSP). GPS surveying is an NP-hard problem. For designing Global Positioning System surveying network, a given set of earth points must be observed consecutively. The survey cost is the sum of the distances to go from one point to another one. This kind of problems is hard to be solved with traditional numerical methods. In this paper we use BF-ICrA to analyze an Ant Colony Optimization (ACO) algorithm developed to provide near-optimal solutions for Global Positioning System surveying problem
Investigating the Shortest Survey Route in a GNSS Traverse Network
Upon the progress in the satellite positioning systems after 1990\u27s particularly, Global Positioning System and Global Navigation Satellite System (GNSS) networks have been setup and used in scientific researches. Operational Research (OR) techniques have been used in the design and optimization of the surveying networks based on GNSS, as well. After 2000\u27s, both developed and developing countries have launched and developed Continuously Operating Reference Stations (CORS) networks for surveying procedures, which require precision to the centimetre. Surveying the detail points by creating traverse networks in the field may be realized thanks to the accuracy of the position obtained. Although many researches in the field of the design and optimization of the GNSS networks exist, finding the shortest route in the survey of such networks remained limited. Objective of this study is to find the shortest survey route in a traverse network created in the CORS-Turkey (CORS-TR) system. Based on the optimum solution of Traveling Salesman Problem we form the best route and timing plan
Adaptive and intelligent navigation of autonomous planetary rovers - A survey
The application of robotics and autonomous systems in space has increased dramatically. The ongoing Mars rover mission involving the Curiosity rover, along with the success of its predecessors, is a key milestone that showcases the existing capabilities of robotic technology. Nevertheless, there has still been a heavy reliance on human tele-operators to drive these systems. Reducing the reliance on human experts for navigational tasks on Mars remains a major challenge due to the harsh and complex nature of the Martian terrains. The development of a truly autonomous rover system with the capability to be effectively navigated in such environments requires intelligent and adaptive methods fitting for a system with limited resources. This paper surveys a representative selection of work applicable to autonomous planetary rover navigation, discussing some ongoing challenges and promising future research directions from the perspectives of the authors
Preliminary Analysis of Quality of Contour Lines Using Smoothing Algorithms
In this paper several well-known filtering techniques were compared in the purpose of automatic line generalization. The used methods for line simplification are digital first order low-pass filter, Savitzky-Golay (SG) filter and Whittaker filter. Two versions of the algorithm for line feature generalization were tested, from source scale 1:25 000 towards target scale of 1:100 000 and from source scale 1:25 000 towards scale of 1:50 000. Also, GPS data filtering for the target scale 1:50 000 was tested. The first version of the algorithm considers that there are no control data, and the filtering parameter is dictated by the desired accuracy for the target scale. The second version involves control data in the target scale. This means that the optimal value for the filtering parameter is the value for which the difference between input and control data is the smallest. Analysis showed that the SG filter yielded the best results in general. The proposed filters can be considered as a new solution for automated cartographic line simplification
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
The Widely scalable Mobile Underwater Sonar Technology (WiMUST) H2020 project: first year status
The Widely scalable Mobile Underwater Sonar Technology (WiMUST) project aims at developing a system of cooperative Autonomous Underwater Vehicles (AUVs) for geotechnical surveying and geophysical exploration. The paper reports about the first year activities and it gives an overview of the main objectives and methods. Results relative to distributed sensor array, cooperative control, mission planning, communications and preliminary experiments are summarized
Book of abstracts of the 24th Euro Working Group on Transportation Meeting
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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
AN INTELLIGENT NAVIGATION SYSTEM FOR AN AUTONOMOUS UNDERWATER VEHICLE
The work in this thesis concerns with the development of a novel multisensor data fusion
(MSDF) technique, which combines synergistically Kalman filtering, fuzzy logic
and genetic algorithm approaches, aimed to enhance the accuracy of an autonomous
underwater vehicle (AUV) navigation system, formed by an integration of global positioning
system and inertial navigation system (GPS/INS).
The Kalman filter has been a popular method for integrating the data produced
by the GPS and INS to provide optimal estimates of AUVs position and attitude. In
this thesis, a sequential use of a linear Kalman filter and extended Kalman filter is
proposed. The former is used to fuse the data from a variety of INS sensors whose
output is used as an input to the later where integration with GPS data takes place.
The use of an adaptation scheme based on fuzzy logic approaches to cope with the
divergence problem caused by the insufficiently known a priori filter statistics is also
explored. The choice of fuzzy membership functions for the adaptation scheme is first
carried out using a heuristic approach. Single objective and multiobjective genetic
algorithm techniques are then used to optimize the parameters of the membership
functions with respect to a certain performance criteria in order to improve the overall
accuracy of the integrated navigation system. Results are presented that show
that the proposed algorithms can provide a significant improvement in the overall
navigation performance of an autonomous underwater vehicle navigation.
The proposed technique is known to be the first method used in relation to AUV
navigation technology and is thus considered as a major contribution thereof.J&S Marine Ltd.,
Qinetiq, Subsea 7 and South West Water PL
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