What are the most effective methods for recognizing objects with robots?

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Object recognition is a crucial skill for robots to interact with their environment and perform tasks that require manipulation, navigation, or inspection. However, identifying and locating objects in complex and dynamic scenes is not an easy feat, especially when dealing with variations in shape, size, color, texture, pose, and lighting. In this article, you will learn about some of the most effective methods for recognizing objects with robots, and how they leverage different types of sensors, algorithms, and frameworks.

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1 Vision sensors

One of the most common and powerful ways for robots to perceive objects is through vision sensors, such as cameras, depth sensors, or lidars. These sensors capture images or point clouds that represent the scene from different perspectives, and provide rich information about the geometry, appearance, and location of objects. However, vision sensors also have some limitations, such as noise, occlusion, distortion, or illumination changes, that can affect the quality and accuracy of the data.

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Gary Martins

Country Managing Director
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Integrated vision sensors with single or multiple library functions are critical. The integration of the vision system in one machine controller brings unrivaled synchronized performance. Microsecond motion responses and synchronized vision/lighting control brings a new level of innovation opportunity to OEM machine builders.

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Zakaria Bourma BRAHIM

Alternant ingénieur contr?le Recherche et développement en automatisme
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De mon point de vue, la vision industrielle a changé le donne de la robotique. Bien qu'ils aient besoin, de commande robuste et d'algorithmes solide pour corriger les perturbations extérieures sa reste envisageable actuellement avec le bibliothèque open source, ils font bien leur travail.

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Tariq J.

Principal MLE @TensorDot Solutions | AI Curriculum Developer | Talks of Generative AI, Computer Vision, AI in Aviation~Finance
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To overcome the potential issues as highlighted in the topic, generative AI can play good part for occlusion, noise and distortion. The illuminiation issue can be circumvented by used of better sensors/cameras or even infra-red sensors. Use of gigahertz frequencies for ranging and 3d mapping equipment may prove be the paradigm shift to overcome such obstacles.

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Kevin Ruiz

Data Scientist & Business Intelligence Strategist | Leveraging & Machine Learning & AI for Global Impact | Championing Data-Driven Strategies & Ethical AI Practices
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I would suggest object detection algorithms from multi-resolution models utilizing resolution sub-imaging. This is for efficiency in training in CNN applications involving images and image segmentation. Structured algorithms built from machine vision modules will be trained and there are several enhancing techniques methods such as Bayes, and other ML classifiers. There still remains the problem of multiple simultaneous object recognition and response. In my opinion, training robots to handle and recognizing increasing numbers of objects simultaneously is still an ongoing challenge.

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Tinotenda Mutami

B.Eng Mechatronics
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Forget single sensors! Robots rock with sensor fusion. Cameras see color, LiDAR maps 3D, radar peeks through walls. Combining these powers, robots recognize objects way better than solo sensors. It's like a superhero team-up, busting limitations and boosting accuracy. This fusion magic works in cars, factories, even rescues! The future is multi-sensor, and robots are leading the charge.

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2 Feature extraction

To recognize objects from vision data, robots need to extract meaningful and distinctive features that describe the objects and allow them to be matched or classified. Features can be low-level, such as edges, corners, or blobs, or high-level, such as contours, regions, or keypoints. Features can be extracted manually, using predefined operators or filters, or automatically, using machine learning techniques such as convolutional neural networks (CNNs) or autoencoders.

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Malek El Khazen

Data, AI & IoT Cloud Solution Architect at Microsoft
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Effective methods for robots to recognize objects, leveraging AI, include: Vision Sensors: Using cameras to capture images. Feature Extraction: Identifying key attributes in images. Object Detection: Locating objects using techniques like CNNs. Object Segmentation: Outlining objects precisely. Object Recognition: Classifying objects with AI models. Frameworks and Libraries: Employing tools like TensorFlow and OpenCV. These approaches when combined together and divided into steps will enable to recognize accurate object recognition in robotics and minimize hallucinations -

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Charlie Medinger

Focused on helping customers design better equipment.
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Knowing the application is key. My top two are LADAR and vision. LADAR works well in bad environments and low lighting conditions especially at longer distances. Once you home in on the object, vision does a nice job by increasing the robot's "intelligence."

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3 Object detection

Object detection is the process of finding and locating objects of interest in an image or point cloud. Object detection can be performed using different approaches, such as template matching, sliding window, region proposal, or neural network. Template matching compares the data with a predefined model or template of the object, and measures the similarity or distance. Sliding window scans the data with a fixed-size window, and evaluates the likelihood of containing an object. Region proposal segments the data into regions that are likely to contain objects, and then classifies them. Neural network uses a trained model to directly output the bounding boxes and labels of the objects.

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Rodrigo Arigheto

Application Engineering Supervisor | Industrial Automation | Channel Management | Sales & Technical Training Specialist | Product Marketing
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Um sensor laser de medi??o com uma ótima imunidade a cores e textura, com tecnologia CMOS embarcada e que possa trabalhar em angulo pode resolver uma grande parte de aplica??es em bra?os rob?s de pega e solda, sendo uma op??o com melhor custo beneficio que um sistema de vis?o pois seu setup e manuten??o s?o mínimos em ambientes mais agressivos e com fuligem ou poeira no ambiente devido ao alto ganho do laser difuso…Sensores de medi??o que possam realizar a leitura em vários materiais e angulos diferentes como o Q4X de alta performance s?o fáceis de instalar e e contribuem com espa?o e pouco peso nas especifica??es do rob?.

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Balamurugan Balakreshnan

Chief AI Officer/Chief Architect/Author/Board Member in UWM CSI
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Instance segmenatation is one of the best object detection technique i have seen. It can contour a object pretty well and also figure overlapping objects.

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4 Object segmentation

Object segmentation is the process of separating and labeling the pixels or points that belong to different objects in an image or point cloud. Object segmentation can be performed using different approaches, such as thresholding, clustering, region growing, or neural network. Thresholding divides the data into foreground and background based on a predefined criterion, such as intensity or depth. Clustering groups the data into clusters based on a similarity measure, such as distance or color. Region growing starts from a seed point and expands the region by adding neighboring points that satisfy a homogeneity condition. Neural network uses a trained model to directly output the pixel-wise or point-wise labels of the objects.

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5 Object recognition

Object recognition is the process of identifying and categorizing the objects in an image or point cloud. Object recognition can be performed using different approaches, such as feature matching, nearest neighbor, support vector machine (SVM), or neural network. Feature matching compares the features extracted from the data with a database of features from known objects, and finds the best matches. Nearest neighbor assigns the label of the closest data point in a training set to the query data point. SVM finds a hyperplane that separates the data points into different classes, and predicts the class of the query data point based on its position. Neural network uses a trained model to directly output the probabilities or scores of the objects.

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6 Frameworks and libraries

To implement and deploy the methods for object recognition with robots, there are several frameworks and libraries that provide tools and functions for data processing, feature extraction, object detection, object segmentation, object recognition, and machine learning. Some of the most popular and widely used frameworks and libraries are OpenCV, PCL, TensorFlow, PyTorch, and ROS. OpenCV is a library for computer vision that offers modules for image and video processing, feature extraction, object detection, and machine learning. PCL is a library for point cloud processing that offers modules for filtering, segmentation, registration, feature extraction, and object recognition. TensorFlow and PyTorch are frameworks for machine learning that offer modules for building, training, and testing neural networks. ROS is a framework for robot software development that offers modules for communication, visualization, simulation, and hardware interfacing.

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Balamurugan Balakreshnan

Chief AI Officer/Chief Architect/Author/Board Member in UWM CSI
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Pytorch, opencv, resnet, retinanet, i stance segmentation models or yolo models combined with pytorch makes it a winner as of now.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Balamurugan Balakreshnan

Chief AI Officer/Chief Architect/Author/Board Member in UWM CSI
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Real world implementation background, lighting, distance of object, how fast it moving, how to track over inside building or warehouse or plant are all something to figure.

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Vijay Patil

Data and AI at Microsoft | Solving challenges by innovating solutions in Data and AI.
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Two standard implementations in commercial applications. Automated Guided Vehicles (AGV) and Automated Mobility Robots (AMR). They both use LiDaR for connected and disconnected environments.

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Breno Silva Oliveira

TéCNICO em AUTOMA??O PLENO
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O radar de luz, também conhecido como LIDAR (Detec??o e Varredura a Laser), utiliza pulsos de luz para medir distancias e criar mapas tridimensionais do ambiente. é frequentemente usado em aplica??es como veículos aut?nomos e cartografia já esse case em paleteira aut?noma em uma indústria de pisos ceramicos

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What are the most effective methods for recognizing objects with robots?

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1 Vision sensors

2 Feature extraction

3 Object detection

4 Object segmentation

5 Object recognition

6 Frameworks and libraries

7 Here’s what else to consider

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