Analysis of Laser Sensors and Camera Vision in the Shoe Position Inspection System

Inspection systems are currently an evolving field in the industry. The main goal is to provide a picture of the quality of intermediates and products in the production process. The most widespread sensory system is camera equipment. This article describes the implementation of camera devices for checking the location of the upper on the shoe last. The next part of the article deals with the analysis of the application of laser sensors in this task. The results point to the clear advantages of laser sensors in the inspection task of placing the uppers on the shoe’s last. The proposed method defined the resolution of laser scanners according to the type of scanned surface, where the resolution of point cloud ranged from 0.16 to 0.5 mm per point based on equations representing specific points approximated to polynomial regression in specific places, which are defined in this article. Next, two inspection systems were described, where one included further development in the field of automation and industry 4.0 and with a high perspective of development into the future. The main aim of this work is to conduct analyses of sensory systems for inspection systems and their possibilities for further work mainly based on the resolution and quality of obtained data. For instance, dependency on scanning complex surfaces and the achieved resolution of scanned surfaces.

Comparison of Display Modality and Human-in-the-Loop Presence for On-Orbit Inspection of Spacecraft

Objective To investigate the impact of interface display modalities and human-in-the-loop presence on the awareness, workload, performance, and user strategies of humans interacting with teleoperated robotic systems while conducting inspection tasks onboard spacecraft. Background Due to recent advancements in robotic technology, free-flying teleoperated robot inspectors are a viable alternative to extravehicular activity inspection operations. Teleoperation depends on the user’s situation awareness; consequently, a key to successful operations is practical bi-directional communication between human and robot agents. Method Participants ( n = 19) performed telerobotic inspection of a virtual spacecraft during two degrees of temporal communication, a Synchronous Inspection task and an Asynchronous Inspection task. Participants executed the two tasks while using three distinct visual displays (2D, 3D, AR) and accompanying control systems. Results Anomaly detection performance was better during Synchronous Inspection than the Asynchronous Inspection of previously captured imagery. Users’ detection accuracy reduced when given interactive exocentric 3D viewpoints to accompany the egocentric robot view. The results provide evidence that 3D projections, either demonstrated on a 2D interface or augmented reality hologram, do not affect the mean clearance violation time (local guidance performance), even though the subjects perceived a benefit. Conclusion In the current implementation, the addition of augmented reality to a classical egocentric robot view for exterior inspection of spacecraft is unnecessary, as its margin of performance enhancement is limited in comparison. Application Results are presented to inform future human–robot interfaces to support crew autonomy for deep space missions.

Towards an Optimal Footprint Based Area Coverage Strategy for a False-Ceiling Inspection Robot

False-ceiling inspection is a critical factor in pest-control management within a built infrastructure. Conventionally, the false-ceiling inspection is done manually, which is time-consuming and unsafe. A lightweight robot is considered a good solution for automated false-ceiling inspection. However, due to the constraints imposed by less load carrying capacity and brittleness of false ceilings, the inspection robots cannot rely upon heavy batteries, sensors, and computation payloads for enhancing task performance. Hence, the strategy for inspection has to ensure efficiency and best performance. This work presents an optimal functional footprint approach for the robot to maximize the efficiency of an inspection task. With a conventional footprint approach in path planning, complete coverage inspection may become inefficient. In this work, the camera installation parameters are considered as the footprint defining parameters for the false ceiling inspection. An evolutionary algorithm-based multi-objective optimization framework is utilized to derive the optimal robot footprint by minimizing the area missed and path-length taken for the inspection task. The effectiveness of the proposed approach is analyzed using numerical simulations. The results are validated on an in-house developed false-ceiling inspection robot—Raptor—by experiment trials on a false-ceiling test-bed.

Towards the Determination of Safe Operating Envelopes for Autonomous UAS in Offshore Inspection Missions

A drive to reduce costs, carbon emissions, and the number of required personnel in the offshore energy industry has led to proposals for the increased use of autonomous/robotic systems for many maintenance tasks. There are questions over how such missions can be shown to be safe. A corollary exists in the manned aviation world for helicopter–ship operations where a test pilot attempts to operate from a ship under a range of wind conditions and provides subjective feedback on the level of difficulty encountered. This defines the ship–helicopter operating limit envelope (SHOL). Due to the cost of creating a SHOL there has been considerable research activity to demonstrate that much of this process can be performed virtually. Unmanned vehicles, however, have no test pilot to provide feedback. This paper therefore explores the possibility of adapting manned simulation techniques to the unmanned world to demonstrate that a mission is safe. Through flight modelling and simulation techniques it is shown that operating envelopes can be created for an oil rig inspection task and that, by using variable performance specifications, these can be tailored to suit the level of acceptable risk. The operating envelopes produced provide condensed and intelligible information regarding the environmental conditions under which the UAS can perform the task.

Autonomous Control Method of Rotor UAVs for Power Inspection With Renewable Energy Based on Swarm Intelligence

With the rapid development of renewable energy, the scale of China’s power grid with renewable energy has become much bigger than ever; as a result, we are facing severe challenges in the inspection and maintenance work of power grids that use renewable energy. Focusing on the shortcomings of the traditional manual inspection methods, this paper studies and proposes an optimization algorithm of automatic inspection of Unmanned Aerial Vehicles (UAVs) to improve the efficiency and cost of the inspection and maintenance work of renewable energy power grids. Firstly, the communication network of the swarm intelligence system has been established to transmit the local information sensed by each UAV in real time. Secondly, according to the sensing ability of UAVs, the segmentation model of UAVs overlapping sensing areas is established, which effectively reduces the probability of overlapping UAVs sensing areas. Thirdly, according to the difference between the coverage value and the effective coverage index of each point in the sensing area, the optimization function of the coverage index is given, which makes the UAV give priority to the inspection area with the lower coverage value. Finally, when a UAV completes a local coverage task, the traction speed is introduced to prevent the UAV from stopping, which ensures that the inspection task of the whole area can be completed in a limited time. The numerical simulation results show that the algorithm can effectively control the UAVs to complete the inspection task in the specified area, and compared with the single UAV inspection method, this algorithm can greatly improve the inspection efficiency and reduce the inspection cost.

Integrated Multiple-Defect Detection and Evaluation of Rail Wheel Tread Images using Convolutional Neural Networks

The wheel-rail interface is regarded as the most important factor for the dynamic behaviour of a railway vehicle, affecting the safety of the service, the passenger comfort, and the life of the wheelset asset. The degradation of the wheels in contact with the rail is visibly manifest on their treads in the form of defects such as indentations, flats, cavities, etc. To guarantee a reliable rail service and maximise the availability of the rolling-stock assets, these defects need to be constantly and periodically monitored as their severity evolves. This inspection task is usually conducted manually at the fleet level and therefore it takes a lot of human resources. In order to add value to this maintenance activity, this article presents an automatic Deep Learning method to jointly detect and classify wheel tread defects based on smartphone pictures taken by the maintenance team. The architecture of this approach is based on a framework of Convolutional Neural Networks, which is applied to the different tasks of the diagnosis process including the location of the defect area within the image, the prediction of the defect size, and the identification of defect type. With this information determined, the maintenancecriteria rules can ultimately be applied to obtain the actionable results. The presented neural approach has been evaluated with a set of wheel defect pictures collected over the course of nearly two years, concluding that it can reliably automate the condition diagnosis of half the current workload and thus reduce the lead time to take maintenance action, significantly reducing engineering hours for verification and validation. Overall, this creates a platform or significant progress in automated predictive maintenance of rolling stock wheelsets.

Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot

The pavement inspection task, which mainly includes crack and garbage detection, is essential and carried out frequently. The human-based or dedicated system approach for inspection can be easily carried out by integrating with the pavement sweeping machines. This work proposes a deep learning-based pavement inspection framework for self-reconfigurable robot named Panthera. Semantic segmentation framework SegNet was adopted to segment the pavement region from other objects. Deep Convolutional Neural Network (DCNN) based object detection is used to detect and localize pavement defects and garbage. Furthermore, Mobile Mapping System (MMS) was adopted for the geotagging of the defects. The proposed system was implemented and tested with the Panthera robot having NVIDIA GPU cards. The experimental results showed that the proposed technique identifies the pavement defects and litters or garbage detection with high accuracy. The experimental results on the crack and garbage detection are presented. It is found that the proposed technique is suitable for deployment in real-time for garbage detection and, eventually, sweeping or cleaning tasks.

Automatic Faults Detection of Photovoltaic Farms: solAIr, a Deep Learning-Based System for Thermal Images

Renewable energy sources will represent the only alternative to limit fossil fuel usage and pollution. For this reason, photovoltaic (PV) power plants represent one of the main systems adopted to produce clean energy. Monitoring the state of health of a system is fundamental. However, these techniques are time demanding, cause stops to the energy generation, and often require laboratory instrumentation, thus being not cost-effective for frequent inspections. Moreover, PV plants are often located in inaccessible places, making any intervention dangerous. In this paper, we propose solAIr, an artificial intelligence system based on deep learning for anomaly cells detection in photovoltaic images obtained from unmanned aerial vehicles equipped with a thermal infrared sensor. The proposed anomaly cells detection system is based on the mask region-based convolutional neural network (Mask R-CNN) architecture, adopted because it simultaneously performs object detection and instance segmentation, making it useful for the automated inspection task. The proposed system is trained and evaluated on the photovoltaic thermal images dataset, a publicly available dataset collected for this work. Furthermore, the performances of three state-of-art deep neural networks, (DNNs) including UNet, FPNet and LinkNet, are compared and evaluated. Results show the effectiveness and the suitability of the proposed approach in terms of intersection over union (IoU) and the Dice coefficient.