Construction of an Experimental Environment for Power-Assisted Control of a Mobile Measurement Mechanism for Infrastructure Inspection

2021 ◽  
Vol 2021 (0) ◽  
pp. 2P1-A03
Author(s):  
Shotaro SHIMADA ◽  
Masato MIZUKAMI ◽  
Naohiko HANAJIMA ◽  
Yoshinori FUJIHIRA
Keyword(s):  
Experimental Environment ◽  
Infrastructure Inspection ◽  
Measurement Mechanism ◽  
Mobile Measurement

Flight Path Planning of Robot for Environmental Measurement Considering Accuracy Assurance

Impact ◽  
2019 ◽  
Vol 2019 (10) ◽  
pp. 90-92
Author(s):  
Kae Doki ◽  
Yuki Funabora ◽  
Shinji Doki
Keyword(s):  
Large Scale ◽  
Highway Bridge ◽  
Climate Research ◽  
Small Uav ◽  
Inspection Process ◽  
Aerial View ◽  
Aerial Vehicle ◽  
Infrastructure Inspection ◽  
Institute Of Technology ◽  
Small Team

Every day we are seeing an increasing number of robots being employed in our day-to-day lives. They are working in factories, cleaning our houses and may soon be chauffeuring us around in vehicles. The affordability of drones too has come down and now it is conceivable for most anyone to own a sophisticated unmanned aerial vehicle (UAV). While fun to fly, these devices also represent powerful new tools for several industries. Anytime an aerial view is needed for a planning, surveillance or surveying, for example, a UAV can be deployed. Further still, equipping these vehicles with an array of sensors, for climate research or mapping, increases their capability even more. This gives companies, governments or researchers a cheap and safe way to collect vast amounts of data and complete tasks in remote or dangerous areas that were once impossible to reach. One area UAVs are proving to be particularly useful is infrastructure inspection. In countries all over the world large scale infrastructure projects like dams and bridges are ageing and in need of upkeep. Identifying which ones and exactly where they are in need of patching is a huge undertaking. Not only can this work be dangerous, requiring trained inspectors to climb these megaprojects, it is incredibly time consuming and costly. Enter the UAVs. With a fleet of specially equipped UAVs and a small team piloting them and interpreting the data they bring back the speed and safety of this work increases exponentially. The promise of UAVs to overturn the infrastructure inspection process is enticing, but there remain several obstacles to overcome. One is achieving the fine level of control and positioning required to navigate the robots around 3D structures for inspection. One can imagine that piloting a small UAV underneath a huge highway bridge without missing a single small crack is quite difficult, especially when the operators are safely on the ground hundreds of meters away. To do this knowing exactly where the vehicle is in space becomes a critical variable. The job can be made even easier if a flight plan based on set waypoints can be pre-programmed and followed autonomously by the UAV. It is exactly this problem that Dr Kae Doki from the Department of Electrical Engineering at Aichi Institute of Technology, and collaborators are focused on solving.

scholarly journals Drone Deep Reinforcement Learning: A Review

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 999
Author(s):  
Ahmad Taher Azar ◽  
Anis Koubaa ◽  
Nada Ali Mohamed ◽  
Habiba A. Ibrahim ◽  
Zahra Fathy Ibrahim ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Real Life ◽  
Environment Monitoring ◽  
Simulated Environments ◽  
Infrastructure Inspection ◽  
Remote Sensing Mapping ◽  
And Control ◽  
The Military ◽  
Uav Navigation

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios.

To Predict or Not to Predict: Influences of Task and Strategy on the Processing of Semantic Relations

2007 ◽  
Vol 19 (8) ◽  
pp. 1259-1274 ◽  
Author(s):  
Dietmar Roehm ◽  
Ina Bornkessel-Schlesewsky ◽  
Frank Rösler ◽  
Matthias Schlesewsky
Keyword(s):  
Semantic Relatedness ◽  
Presentation Mode ◽  
Judgment Task ◽  
Sentence Context ◽  
Task Demands ◽  
Event Related Potential ◽  
Semantic Relations ◽  
Decision Task ◽  
Lexical Semantic ◽  
Experimental Environment

We report a series of event-related potential experiments designed to dissociate the functionally distinct processes involved in the comprehension of highly restricted lexical-semantic relations (antonyms). We sought to differentiate between influences of semantic relatedness (which are independent of the experimental setting) and processes related to predictability (which differ as a function of the experimental environment). To this end, we conducted three ERP studies contrasting the processing of antonym relations (black-white) with that of related (black-yellow) and unrelated (black-nice) word pairs. Whereas the lexical-semantic manipulation was kept constant across experiments, the experimental environment and the task demands varied: Experiment 1 presented the word pairs in a sentence context of the form The opposite of X is Y and used a sensicality judgment. Experiment 2 used a word pair presentation mode and a lexical decision task. Experiment 3 also examined word pairs, but with an antonymy judgment task. All three experiments revealed a graded N400 response (unrelated > related > antonyms), thus supporting the assumption that semantic associations are processed automatically. In addition, the experiments revealed that, in highly constrained task environments, the N400 gradation occurs simultaneously with a P300 effect for the antonym condition, thus leading to the superficial impression of an extremely “reduced” N400 for antonym pairs. Comparisons across experiments and participant groups revealed that the P300 effect is not only a function of stimulus constraints (i.e., sentence context) and experimental task, but that it is also crucially influenced by individual processing strategies used to achieve successful task performance.

scholarly journals Determination of car on-road black carbon and particle number emission factors and comparison between mobile and stationary measurements

2015 ◽  
Vol 8 (1) ◽  
pp. 43-55 ◽  
Author(s):  
I. Ježek ◽  
L. Drinovec ◽  
L. Ferrero ◽  
M. Carriero ◽  
G. Močnik
Keyword(s):  
Black Carbon ◽  
Particle Number ◽  
Emission Factors ◽  
Emission Measurement ◽  
Stationary Method ◽  
Characteristic Value ◽  
Portable Emission Measurement System ◽  
Specific Distribution ◽  
Mobile Measurement

Abstract. We have used two methods for measuring emission factors (EFs) in real driving conditions on five cars in a controlled environment: the stationary method, where the investigated vehicle drives by the stationary measurement platform and the composition of the plume is measured, and the chasing method, where a mobile measurement platform drives behind the investigated vehicle. We measured EFs of black carbon and particle number concentration. The stationary method was tested for repeatability at different speeds and on a slope. The chasing method was tested on a test track and compared to the portable emission measurement system. We further developed the data processing algorithm for both methods, trying to improve consistency, determine the plume duration, limit the background influence and facilitate automatic processing of measurements. The comparison of emission factors determined by the two methods showed good agreement. EFs of a single car measured with either method have a specific distribution with a characteristic value and a long tail of super emissions. Measuring EFs at different speeds or slopes did not significantly influence the EFs of different cars; hence, we propose a new description of vehicle emissions that is not related to kinematic or engine parameters, and we rather describe the vehicle EF with a characteristic value and a super emission tail.

Evaluating the effectiveness of drones in emergency situations: a hybrid multi-criteria approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Justin Zuopeng Zhang ◽  
Praveen Ranjan Srivastava ◽  
Prajwal Eachempati
Keyword(s):  
Fuzzy Ahp ◽  
Use Cases ◽  
Fuzzy Decision Making ◽  
Fuzzy Dematel ◽  
Content Type ◽  
Emergency Situations ◽  
High Rise ◽  
Criteria Weights ◽  
Infrastructure Inspection ◽  
Logistic Support

PurposeThe paper aims to build a customized hybrid multi-criteria model to identify the top three utilities of drones at both personal and community levels for two use cases: firefighting in high-rise buildings and logistic support.Design/methodology/approachA hybrid multi-criterion model that integrates fuzzy analytical hierarchy process (AHP), Best Worst, fuzzy analytical network process (ANP), fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) is used to compute the criteria weights. The weights are validated by a novel ensemble ranking technique further whetted by experts at the community and personal levels to two use cases.FindingsDrones' fire handling and disaster recovery utilities are the most important to fight fire in high-rise buildings at both personal and community levels. Similarly, drones' urban planning, municipal works and infrastructure inspection utilities are the most important for providing logistics support at personal and community levels.Originality/valueThe paper presents a novel multi-criteria approach, i.e. ensemble ranking, by combining the criteria ranking of individual methods – fuzzy AHP, Best-Worst, fuzzy ANP and fuzzy DEMATEL – in the ratio of optimal weights to each technique to generate the consolidated ranking. Domain experts also validate this ranking for robustness. This paper demonstrates a viable methodology to quantify the utilities of drones and their capabilities. The proposed model can be recalibrated for different use case scenarios of drones.

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