the need for automation in the textile industry is growing rapidly today. Color based object sorting is a highly challenging process to be considered and needs to be addressed. It involves an automated material handli...
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ISBN:
(数字)9798350386349
ISBN:
(纸本)9798350386356
the need for automation in the textile industry is growing rapidly today. Color based object sorting is a highly challenging process to be considered and needs to be addressed. It involves an automated material handling system. It synchronizes the movement of robotic arm to pick up objects moving on a mobile robot. It aims in classifying the colored objects then picking and placing the objects in its respective pre-programmed place. thereby eliminating the monotonous work done by human, achieving accuracy and speed in the work. the core objective of the project is to propose an intelligent color-based object sorting system using deep learning technique like Convolution Neural Network for extraction of feature embedded withthe machine learning algorithm. the two classifiers Random Forest and K-NN algorithm were implemented and studied for better classification. Based on the performance metrics, the Radom Forest algorithm out performs in classification. the project module involves cameras that captures the object’s color through the computervision Library and sends the signal to the controller. the dataset of the captured images has been uploaded and compared withthe trained data set. the ESP 32 Module transmit a signal to relay circuit, which then drives the robotic arm’s multiple motors to grip the object and position it in the given area. Based on the color observed, the robotic arm goes to the given point, releases the object, and returns to its original position.
this paper designs and implements a leg detection system based on laser scan data. People detection is an important function for autonomous navigation robots, especially in strongly interactive application scenarios. ...
ISBN:
(数字)9781728143903
ISBN:
(纸本)9781728143910
this paper designs and implements a leg detection system based on laser scan data. People detection is an important function for autonomous navigation robots, especially in strongly interactive application scenarios. though it's more intuitive to detect people by means of computervision, it often means more computation, higher latency, and higher hardware costs. In this work, we propose an approach to detect human leg using pure two dimensional laser scan data with higher accuracy. Our system is based on a random forest model for human leg detection. Different from the original solutions, we add a series of feature filtering mechanisms to filter the leg candidates based on their data characteristics. the features will be put into the random forest model after filtering, and the confidence of being human legs will be calculated. then, we verify the coordinates of the human legs using the map masking mechanism. It verifies the coordinates of the human legs with map data, which greatly reduces the probability that cylindrical objects such as pillars in the environment being misidentified as human legs, removing most of the false positives provided by the random forest model.
In modern robots, the usage of computationally expensive models involving deep neural networks, also referred to as DNNs, for tasks such as the localization of operations awareness, planning, and object recognition is...
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ISBN:
(数字)9798350360165
ISBN:
(纸本)9798350360172
In modern robots, the usage of computationally expensive models involving deep neural networks, also referred to as DNNs, for tasks such as the localization of operations awareness, planning, and object recognition is becoming prominent. Nevertheless, resource-constrained machinery, such as low-power aerial vehicles, often lack the requisite internal computing resources to easily run cutting-edge simulations of neural networks. Cloud robotics appears as an answer, allowing robots to offload processing to centralized computers for greater precision models. Nonetheless, the ignored downside of cloud robots lies in the possible delay and data loss experienced during contact over crowded wireless networks. this study discusses the robot Transferring responsibility Problem, exploring when and where robots should offload sense tasks that improve accuracy while reducing the costs involved with cloud communication. the method involves framing shifting as a sequence decision-making issue concerning robots and suggesting a remedy using sophisticated reinforcement learning. through models and hardware tests employing advanced thinking DNNs, what was suggested sharing strategy improves vision task efficacy by 1.3 2.6 times as a result of standard strategies, allowing robots to increase their sense accuracy while incurring minimal communication via cloud costs.
Collaborative robotics, in conjunction with artificial intelligence (AI), offers a contemporary and effective paradigm for secure machine-human interactions. this synergy branches out into a variety of industries, inc...
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ISBN:
(数字)9798350360165
ISBN:
(纸本)9798350360172
Collaborative robotics, in conjunction with artificial intelligence (AI), offers a contemporary and effective paradigm for secure machine-human interactions. this synergy branches out into a variety of industries, including education and entertainment, in addition to industrial uses. Two intriguing board games that offer a platform for examining the potential of cooperative robotic systems are chess and checkers. An intelligent and cooperative robotic system designed for use in Italian checkers games is described in this context by the study being presented. To record the game state, the gadget employs a camera. To physically move pieces across the board, a pick-and-place mechanism is used. An algorithm is used to automatically choose the optimal moves that comply withthe rules. the system respects the kinematic restrictions of the manipulator while optimizing minimum-time trajectories live for every manipulation, guaranteeing a smooth and dynamic gaming experience. An experimental validation employing a seven-degree-of-freedom Franka Emika arm verifies the effectiveness of the proposed approach in real-world settings.
Innovative Students analyzing the applications of robots want to have each experimental and theoretical understanding in a selection of regions. those regions embody perception sensors, three-d area, transforms among ...
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ISBN:
(数字)9781728163871
ISBN:
(纸本)9781728163888
Innovative Students analyzing the applications of robots want to have each experimental and theoretical understanding in a selection of regions. those regions embody perception sensors, three-d area, transforms among organize frames, and deciphering factor-cloud data. An outstanding device for them to acquire the critical factor is a collaborating show display screen. the human-system interface makes the challenge rely greater on the motion. At present, 3-D sensor is used typically via the robotics neighborhood (kinectv2). this sensor is positioned above the display and turned around with a purpose to experience human workout in the front of the display. A design proposal is detected in the nearest object interior to the front of the screen. To strive this, college students need to compute the extensively alternate of the coordinate body of the sensor and body of the display. Several strategies are applied to execute this process successfully. One in each and every usage of the aruco markers, they favor to devise and verify imperative mathematical technique and take a look at their answer. Subsequently, they look at to work with factor-cloud information. they choose to flip out to be conscious of the nearest man or female reputation subsequent in the front of the screen. they may additionally be capable to gain this through calculating the plane equation of the ground in mixture with inferring the records from the component of view of the coordinate body of the screen. the thresholding operations which are interior to the scanned factor-cloud are studied. Finally, an algorithm is developed for the interplay between the segmented character and the show screen. Completing this challenge represents university and college students can analyze data for higher modern robotics' programs, on the whole inside the concern of the Human-robotic Interaction (HRD.
People with visual impairment have difficulty in navigating a room based on text in room nameplate. Recognizing room nameplate in real environments withcomputervision approach is challenging, because the system has ...
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ISBN:
(数字)9798331520038
ISBN:
(纸本)9798331520045
People with visual impairment have difficulty in navigating a room based on text in room nameplate. Recognizing room nameplate in real environments withcomputervision approach is challenging, because the system has to detect the room nameplate objects. Detection result can be improved by adding attention module in deep learning architecture, however model complexity also need to be observed to prevent accident caused by slow response from the system. Based on that problem, we proposed to compare the effect of Coordinate Attention (CA), Convolutional Block Attention Module (CBAM), and Shuffle Attention (SA) on YOLOv8 model. Based on our findings, CA module shows positive result on accuracy of 99,50%, precision of 1, and f1-score of 0,997. As for CBAM has reduced accuracy to 96,15% from original YOLOv8n that has 98,52% accuracy, this may occur because the placement of the CBAM module before detection head is not suitable in the case of room name plate detection. Meanwhile, SA module has the least number of parameters and model size increase of additional 73.896 parameters and 154 KB, respectively. Our findings enrich insight in improving object detection method for autonomous smart wheelchair room navigation system.
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