Towards a Simple Navigation Strategy for Autonomous Inspection of Ducts and Galleries

Author(s):  
Guilherme Pereira ◽  
Carolina Duarte ◽  
David Marques ◽  
Hector Azpurua ◽  
Gustavo Pessin ◽  
...  
2014 ◽  
Author(s):  
Chi Ngo ◽  
Nora Newcombe ◽  
Ingrid Olson ◽  
Steven Weisberg

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Abdallah Daddi-Moussa-Ider ◽  
Hartmut Löwen ◽  
Benno Liebchen

AbstractAs compared to the well explored problem of how to steer a macroscopic agent, like an airplane or a moon lander, to optimally reach a target, optimal navigation strategies for microswimmers experiencing hydrodynamic interactions with walls and obstacles are far-less understood. Here, we systematically explore this problem and show that the characteristic microswimmer-flow-field crucially influences the navigation strategy required to reach a target in the fastest way. The resulting optimal trajectories can have remarkable and non-intuitive shapes, which qualitatively differ from those of dry active particles or motile macroagents. Our results provide insights into the role of hydrodynamics and fluctuations on optimal navigation at the microscale, and suggest that microorganisms might have survival advantages when strategically controlling their distance to remote walls.


2020 ◽  
Vol 2 (1) ◽  
pp. 90-105
Author(s):  
Jimmy Y. Zhong

AbstractFocusing on 12 allocentric/survey-based strategy items of the Navigation Strategy Questionnaire (Zhong & Kozhevnikov, 2016), the current study applied item response theory-based analysis to determine whether a bidimensional model could better describe the latent structure of the survey-based strategy. Results from item and model fit diagnostics, categorical response and item information curves showed that an item with the lowest rotated component loading (.27) [SURVEY12], could be considered for exclusion in future studies; and that a bidimensional model with three preference-related items constituting a content factor offered a better representation of the latent structure than a unidimensional model per se. Mean scores from these three items also correlated significantly with a pointing-to-landmarks task to the same relative magnitude as the mean scores from all items, and all items excluding SURVEY12. These findings gave early evidence suggesting that the three preference-related items could constitute a subscale for deriving quick estimates of large-scale allocentric spatial processing in healthy adults in both experimental and clinical settings. Potential cognitive and brain mechanisms were discussed, followed by calls for future studies to gather greater evidence confirming the predictive validity of the full and sub scales, along with the design of new items focusing on environmental familiarity.


Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1385
Author(s):  
Yurong Feng ◽  
Kwaiwa Tse ◽  
Shengyang Chen ◽  
Chih-Yung Wen ◽  
Boyang Li

The inspection of electrical and mechanical (E&M) devices using unmanned aerial vehicles (UAVs) has become an increasingly popular choice in the last decade due to their flexibility and mobility. UAVs have the potential to reduce human involvement in visual inspection tasks, which could increase efficiency and reduce risks. This paper presents a UAV system for autonomously performing E&M device inspection. The proposed system relies on learning-based detection for perception, multi-sensor fusion for localization, and path planning for fully autonomous inspection. The perception method utilizes semantic and spatial information generated by a 2-D object detector. The information is then fused with depth measurements for object state estimation. No prior knowledge about the location and category of the target device is needed. The system design is validated by flight experiments using a quadrotor platform. The result shows that the proposed UAV system enables the inspection mission autonomously and ensures a stable and collision-free flight.


2020 ◽  
Vol 12 ◽  
pp. 175682932092452
Author(s):  
Liang Lu ◽  
Alexander Yunda ◽  
Adrian Carrio ◽  
Pascual Campoy

This paper presents a novel collision-free navigation system for the unmanned aerial vehicle based on point clouds that outperform compared to baseline methods, enabling high-speed flights in cluttered environments, such as forests or many indoor industrial plants. The algorithm takes the point cloud information from physical sensors (e.g. lidar, depth camera) and then converts it to an occupied map using Voxblox, which is then used by a rapid-exploring random tree to generate finite path candidates. A modified Covariant Hamiltonian Optimization for Motion Planning objective function is used to select the best candidate and update it. Finally, the best candidate trajectory is generated and sent to a Model Predictive Control controller. The proposed navigation strategy is evaluated in four different simulation environments; the results show that the proposed method has a better success rate and a shorter goal-reaching distance than the baseline method.


Author(s):  
Sun Shuangchun ◽  
Li Yanlei ◽  
Yi Zhenxiao ◽  
Wang Kai ◽  
Yu Ping ◽  
...  

Author(s):  
David Alejo ◽  
Gonzalo Mier ◽  
Carlos Marques ◽  
Fernando Caballero ◽  
Luís Merino ◽  
...  

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