Implementing CNNs in Robotics: A Deep Learning Approach
Explore the application of Convolutional Neural Networks (CNNs) in robotics, focusing on object detection, autonomous navigation, and real-time decision-making. Learn about the architecture of CNNs, challenges encountered, and steps for integrating CNN models with robot control systems for intelligent behavior.
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Dr. SNS RAJALAKSHMI COLLEGE OF ARTS & SCIENCE (Autonomous) Coimbatore -641049 Accredited by NAAC(Cycle III) with A+ Grade (Recognized by UGC, Approved by AICTE, New Delhi and Affiliated to Bharathiar University, Coimbatore) DEPARTMENT OF B.Sc CS (artificial intelligence &data science) Intelligent Systems and RoBOTICS Cource code : 20UAI801 MADHUMITHA.V Department of Computer Science(AI&DS) 1/X Dr.SNSRCAS BSc CS(AI&DS)
CONVULATION NEURAL NETWORK CONVULATION NEURAL NETWORK ROBOT CONTROL ROBOT CONTROL IMPLEMENTATION IMPLEMENTATION A deep learning approach for intelligent robot behavior 2 Dr.SNSRCAS BSc CS(AI&DS)
Introduction What are CNNs? CNNs are deep learning algorithms designed for tasks like image recognition,object detection,and video processing. Why use CNNs in robotics? Object detection &recognition Autonomous navigation Pattern recognition feature extraction Real time decision making Handling high dimensional data Improved accuracy 3 Dr.SNSRCAS BSc CS(AI&DS)
CNN OVERVIEW 1.INPUT LAYER: Take raw data in numerical form,typically as pixel values. 2.CONVOLUTIONAL LAYER: Applies filters to the input to extract features like edges,textures,and patterns. 3.POOLING LAYER: Reduces the spatial size of feature maps,preserving key information while reducing computation. 4.FULLY CONNECTED LAYER: Flattens the feature maps and connects all neurons to make predictions or classifications. 5.OUTPUT LAYER: Produces the final prediction,such as class label. 4 Dr.SNSRCAS BSc CS(AI&DS)
DIAGRAM OF SIMPLE CNN MODEL 5 Dr.SNSRCAS BSc CS(AI&DS)
IMPLEMENTATION OF WORKFLOW DEFINE THE OBJECTIVE Determine the task(e.g.,object detection,obstacle avoidance). Identify thetype of data(images,videos,or sensory inputs). DESIGN CNN ARCHITECTURE: Choose an appropriate architecture(eg.LeNet VGG or custom CNN). TRAIN CNN: Train the model on a GPU for faster computation. INTEGRATE WITH ROBOT CONTROL SYSTEM Deploy the trained CNN model on a hardware platform(eg.,raspberry pi). TESTING AND OPTIMIZATION: Test the system in a controlled environment. DEPLOYMENT AND MONITORING: Deploy the robot in real-world scenerios. 6 Dr.SNSRCAS BSc CS(AI&DS)
CHALLENGES OF CNN Data Requirements: Need large datasets for training High Computational Cost: GPUs/TPUs are essential Overfitting: Regularization techniques needed Interpretability: Hard to explain why a model makes specific predictions. 7 Dr.SNSRCAS BSc CS(AI&DS)
Advancements in CNNs Transfer Learning Pretrained models (ResNet, VGG, EfficientNet) for faster training and better performance with small datasets. Efficient Architectures MobileNet, EfficientNet, and SqueezeNet for lightweight models in mobile and edge devices. Hybrid Models CNNs combined with Vision Transformers, RNNs, and GANs for tasks like image captioning and video understanding. Self-Supervised Learning Training with unlabeled data using contrastive learning (e.g., SimCLR). Lightweight CNN Models ShuffleNet and GhostNet for low-power, real-time applications. Explainable AI Tools like Grad-CAM and saliency maps to improve model interpretability. Federated Learning Privacy-preserving CNN training across distributed devices, e.g., healthcare. 8 Dr.SNSRCAS BSc CS(AI&DS)
FUTURE OF CNN Integration with Reinforcement Learning Enhances decision-making in robotics, gaming, and autonomous systems. Applications in 3D Data Processing LiDAR and 3D point clouds for robotics, AR/VR, and self-driving cars. Cross-Modal Learning Combines image, text, and audio data for tasks like video analysis and multimodal AI. Interpretability and Explainability Research on tools like Grad-CAM and saliency maps to build trust and transparency in AI systems. 9 Dr.SNSRCAS BSc CS(AI&DS)
Conclusion 1. CNNs revolutionized visual data processing with applications in diverse fields. 2. Key advancements like transfer learning and efficient architectures enhance their versatility. 3. Future lies in 3D data, cross-modal AI, and improved interpretability. 4. "CNNs are shaping the future of AI-powered solutions." 10 Dr.SNSRCAS BSc CS(AI&DS)
THANK YOU! THANK YOU! 11 Dr.SNSRCAS BSc CS(AI&DS)
