Adaptive Anchor Pairs Selection in a TDOA-based System Through Robot Localization Error Minimization

2021 ◽  
Author(s):  
Marcin Kolakowski
Keyword(s):  
Error Minimization ◽  
Robot Localization ◽  
Localization Error

scholarly journals Comparative Study of Two Pose Measuring Systems Used to Reduce Robot Localization Error

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1305
Author(s):  
Marek Franaszek ◽  
Geraldine S. Cheok ◽  
Jeremy A. Marvel
Keyword(s):  
Rigid Body ◽  
Body Condition ◽  
Degrees Of Freedom ◽  
Industrial Robot ◽  
Substantial Reduction ◽  
Three Dimensional ◽  
Robot Localization ◽  
Localization Error ◽  
Measuring Systems ◽  
The Difference

The performance of marker-based, six degrees of freedom (6DOF) pose measuring systems is investigated. For instruments in this class, the pose is derived from locations of a few three-dimensional (3D) points. For such configurations to be used, the rigid-body condition—which requires that the distance between any two points must be fixed, regardless of orientation and position of the configuration—must be satisfied. This report introduces metrics that gauge the deviation from the rigid-body condition. The use of these metrics is demonstrated on the problem of reducing robot localization error in assembly applications. Experiments with two different systems used to reduce the localization error of the same industrial robot yielded two conflicting outcomes. The data acquired with one system led to substantial reduction in both position and orientation error of the robot, while the data acquired with a second system led to comparable reduction in the position error only. The difference is attributed to differences between metrics used to characterize the two systems.

3-D Scan Matching for Mobile Robot Localization over Rough Terrain

2019 ◽  
Vol 139 (9) ◽  
pp. 1041-1050
Author(s):  
Hiroyuki Nakagomi ◽  
Yoshihiro Fuse ◽  
Hidehiko Hosaka ◽  
Hironaga Miyamoto ◽  
Takashi Nakamura ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Rough Terrain ◽  
Robot Localization ◽  
Scan Matching ◽  
Mobile Robot Localization

Accuracy of robot-assisted versus optical frameless navigated stereoelectroencephalography electrode placement in children

2019 ◽  
Vol 23 (3) ◽  
pp. 297-302 ◽  
Author(s):  
Julia D. Sharma ◽  
Kiran K. Seunarine ◽  
Muhammad Zubair Tahir ◽  
Martin M. Tisdall
Keyword(s):  
Soft Tissue ◽  
Entry Point ◽  
Electrode Placement ◽  
Target Point ◽  
Localization Error ◽  
Bone Thickness ◽  
Tissue Thickness ◽  
Robot Assisted ◽  
Soft Tissue Thickness ◽  
Safety Margins

OBJECTIVEThe aim of this study was to compare the accuracy of optical frameless neuronavigation (ON) and robot-assisted (RA) stereoelectroencephalography (SEEG) electrode placement in children, and to identify factors that might increase the risk of misplacement.METHODSThe authors undertook a retrospective review of all children who underwent SEEG at their institution. Twenty children were identified who underwent stereotactic placement of a total of 218 electrodes. Six procedures were performed using ON and 14 were placed using a robotic assistant. Placement error was calculated at cortical entry and at the target by calculating the Euclidean distance between the electrode and the planned cortical entry and target points. The Mann-Whitney U-test was used to compare the results for ON and RA placement accuracy. For each electrode placed using robotic assistance, extracranial soft-tissue thickness, bone thickness, and intracranial length were measured. Entry angle of electrode to bone was calculated using stereotactic coordinates. A stepwise linear regression model was used to test for variables that significantly influenced placement error.RESULTSBetween 8 and 17 electrodes (median 10 electrodes) were placed per patient. Median target point localization error was 4.5 mm (interquartile range [IQR] 2.8–6.1 mm) for ON and 1.07 mm (IQR 0.71–1.59) for RA placement. Median entry point localization error was 5.5 mm (IQR 4.0–6.4) for ON and 0.71 mm (IQR 0.47–1.03) for RA placement. The difference in accuracy between Stealth-guided (ON) and RA placement was highly significant for both cortical entry point and target (p < 0.0001 for both). Increased soft-tissue thickness and intracranial length reduced accuracy at the target. Increased soft-tissue thickness, bone thickness, and younger age reduced accuracy at entry. There were no complications.CONCLUSIONSRA stereotactic electrode placement is highly accurate and is significantly more accurate than ON. Larger safety margins away from vascular structures should be used when placing deep electrodes in young children and for trajectories that pass through thicker soft tissues such as the temporal region.

Improved DV-Hop Localization Scheme for Randomly Deployed WSNs

2020 ◽  
Vol 10 (1) ◽  
pp. 94-109 ◽  
Author(s):  
Rekha Goyat ◽  
Mritunjay Kumar Rai ◽  
Gulshan Kumar ◽  
Hye-Jin Kim ◽  
Se-Jung Lim
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Research Area ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Error ◽  
Anchor Nodes ◽  
Least Energy ◽  
Range Free

Background: Wireless Sensor Networks (WSNs) is considered one of the key research area in the recent. Various applications of WSNs need geographic location of the sensor nodes. Objective: Localization in WSNs plays an important role because without knowledge of sensor nodes location the information is useless. Finding the accurate location is very crucial in Wireless Sensor Networks. The efficiency of any localization approach is decided on the basis of accuracy and localization error. In range-free localization approaches, the location of unknown nodes are computed by collecting the information such as minimum hop count, hop size information from neighbors nodes. Methods: Although various studied have been done for computing the location of nodes but still, it is an enduring research area. To mitigate the problems of existing algorithms, a range-free Improved Weighted Novel DV-Hop localization algorithm is proposed. Main motive of the proposed study is to reduced localization error with least energy consumption. Firstly, the location information of anchor nodes is broadcasted upto M hop to decrease the energy consumption. Further, a weight factor and correction factor are introduced which refine the hop size of anchor nodes. Results: The refined hop size is further utilized for localization to reduces localization error significantly. The simulation results of the proposed algorithm are compared with other existing algorithms for evaluating the effectiveness and the performance. The simulated results are evaluated in terms localization error and computational cost by considering different parameters such as node density, percentage of anchor nodes, transmission range, effect of sensing field and effect of M on localization error. Further statistical analysis is performed on simulated results to prove the validation of proposed algorithm. A paired T-test is applied on localization error and localization time. The results of T-test depicts that the proposed algorithm significantly improves the localization accuracy with least energy consumption as compared to other existing algorithms like DV-Hop, IWCDV-Hop, and IDV-Hop. Conclusion: From the simulated results, it is concluded that the proposed algorithm offers 36% accurate localization than traditional DV-Hop and 21 % than IDV-Hop and 13% than IWCDV-Hop.

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