A survey on inspecting structures using robotic systems

Almadhoun, Randa; Taha, Tarek; Seneviratne, Lakmal; Dias, Jorge; Cai, Guowei

doi:10.1177/1729881416663664

Cited by 74 publications

(46 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, sensors and actuators equipped in these robots can be very diverse, including those visual, strength-based, ultrasonic, magnetic, electrical, thermal, radio waves or endoscopes [17]. In fact, the use of robotic systems for the inspection of structures implies a set of challenges that need multiple sensors, such as coverage for path planning, data gathering or model reconstruction [19]. Inspection of pipes is another important task in the industry.…”

Section: State Of the Artmentioning

confidence: 99%

Design of a Hyper-Redundant Robot and Teleoperation Using Mixed Reality for Inspection Tasks

Martín-Barrio

Roldán‐Gómez

Rodríguez

et al. 2020

Sensors

View full text Add to dashboard Cite

Hyper-redundant robots are highly articulated devices that present numerous technical challenges such as their design, control or remote operation. However, they offer superior kinematic skills than traditional robots for multiple applications. This work proposes an original and custom-made design for a discrete and hyper-redundant manipulator. It is comprised of 7 sections actuated by cables and 14 degrees of freedom. It has been optimized to be very robust, accurate and capable of moving payloads with high dexterity. Furthermore, it has been efficiently controlled from the actuators to high-level strategies based on the management of its shape. However, these highly articulated systems often exhibit complex shapes that frustrate their spatial understanding. Immersive technologies emerge as a good solution to remotely and safely teleoperate the presented robot for an inspection task in a hazardous environment. Experimental results validate the proposed robot design and control strategies. As a result, it is concluded that hyper-redundant robots and immersive technologies should play an important role in the near future of automated and remote applications.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Design of a Hyper-Redundant Robot and Teleoperation Using Mixed Reality for Inspection Tasks

Martín-Barrio

Roldán‐Gómez

Rodríguez

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Later Coverage Planning with combining viewpoint and path planning problem into one-step optimization problem has also been proposed to achieve better optimization results [17] [18]. Additional previously planning methods for VPP and CPP problems in different applications could be found in several survey papers: [4] [9] [19] [8] [20] [21].…”

Section: Relevant Workmentioning

confidence: 99%

Coverage Path Planning using Path Primitive Sampling and Primitive Coverage Graph for Visual Inspection

Wei¹,

Deng

Xiao

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

Planning the path to gather the surface information of the target objects is crucial to improve the efficiency of and reduce the overall cost, for visual inspection applications with Unmanned Aerial Vehicles (UAVs). Coverage Path Planning (CPP) problem is often formulated for these inspection applications because of the coverage requirement. Traditionally, researchers usually plan and optimize the viewpoints to capture the surface information first, and then optimize the path to visit the selected viewpoints. In this paper, we propose a novel planning method to directly sample and plan the inspection path for a camera-equipped UAV to acquire visual and geometric information of the target structures as a video stream setting in complex 3D environment. The proposed planning method first generates via-points and path primitives around the target object by using sampling methods based on voxel dilation and subtraction. A novel Primitive Coverage Graph (PCG) is then proposed to encode the topological information, flying distances, and visibility information, with the sampled via-points and path primitives. Finally graph search is performed to find the resultant path in the PCG to complete the inspection task with the coverage requirements. The effectiveness of the proposed method is demonstrated through simulation and field tests in this paper.

show abstract

“…(Hollinger et al, 2012;Alexis et al, 2015;Bircher et al, 2016;Galceran et al, 2015)). A review of current approaches on this topic can be found in (Almadhoun et al, 2016). This field is also closely related to coverage search (e.g.…”

Section: Related Workmentioning

confidence: 99%

Prioritized multi-view stereo depth map generation using confidence prediction

Mostegel

Fraundorfer

Bischof

2018

ISPRS Journal of Photogrammetry and Remote Sensing

View full text Add to dashboard Cite

In this work, we propose a novel approach to prioritize the depth map computation of multi-view stereo (MVS) to obtain compact 3D point clouds of high quality and completeness at low computational cost. Our prioritization approach operates before the MVS algorithm is executed and consists of two steps. In the first step, we aim to find a good set of matching partners for each view. In the second step, we rank the resulting view clusters (i.e. key views with matching partners) according to their impact on the fulfillment of desired quality parameters such as completeness, ground resolution and accuracy. Additional to geometric analysis, we use a novel machine learning technique for training a confidence predictor. The purpose of this confidence predictor is to estimate the chances of a successful depth reconstruction for each pixel in each image for one specific MVS algorithm based on the RGB images and the image constellation. The underlying machine learning technique does not require any ground truth or manually labeled data for training, but instead adapts ideas from depth map fusion for providing a supervision signal. The trained confidence predictor allows us to evaluate the quality of image constellations and their potential impact to the resulting 3D reconstruction and thus builds a solid foundation for our prioritization approach. In our experiments, we are thus able to reach more than 70% of the maximal reachable quality fulfillment using only 5% of the available images as key views. For evaluating our approach within and across different domains, we use two completely different scenarios, i.e. cultural heritage preservation and reconstruction of single family houses.

show abstract

A survey on inspecting structures using robotic systems

Cited by 74 publications

References 47 publications

Design of a Hyper-Redundant Robot and Teleoperation Using Mixed Reality for Inspection Tasks

Design of a Hyper-Redundant Robot and Teleoperation Using Mixed Reality for Inspection Tasks

Coverage Path Planning using Path Primitive Sampling and Primitive Coverage Graph for Visual Inspection

Prioritized multi-view stereo depth map generation using confidence prediction

Contact Info

Product

Resources

About