chore: sync with upstream RIGNITC

Author: Mummanajagadeesh

.github/workflows/deploy.yml

@@ -61,6 +61,8 @@ jobs:
           find dist -type f -name "*.html" -print0 | while IFS= read -r -d '' file; do
             sed -i \
               -e "s|=\"/assets/|=\"$BASE_PREFIX/assets/|g" \
+              -e "s|=\"/content/|=\"$BASE_PREFIX/content/|g" \
+              -e "s|=\"/blog/|=\"$BASE_PREFIX/blog/|g" \
               -e "s|=\"/src/|=\"$BASE_PREFIX/src/|g" \
               -e "s|=\"/\"|=\"$BASE_PREFIX/\"|g" \
               -e "s|\"/src/components/|\"$BASE_PREFIX/src/components/|g" \
@@ -89,6 +91,10 @@ jobs:
               -e "s|'/src/|'$BASE_PREFIX/src/|g" \
               -e "s|\"/assets/|\"$BASE_PREFIX/assets/|g" \
               -e "s|'/assets/|'$BASE_PREFIX/assets/|g" \
+              -e "s|/content/|$BASE_PREFIX/content/|g" \
+              -e "s|'/blog/|'$BASE_PREFIX/blog/|g" \
+              -e "s|\"/blog/|\"$BASE_PREFIX/blog/|g" \
+              -e "s|\`/blog/|\`$BASE_PREFIX/blog/|g" \
               "$file"
           done
 

assets/images/achievements/competitions/ISRO.webp

@@ -0,0 +1,98 @@
+---
+slug: ai-in-robotics
+title: Artificial Intelligence in Modern Robotics
+date: 2024-02-20
+summary: How machine learning and AI are revolutionizing robotic perception, decision-making, and autonomous navigation.
+author: RIGNITC Team
+category: AI & Machine Learning
+tags: [ai, machine-learning, robotics, computer-vision]
+draft: false
+---
+
+# Artificial Intelligence in Modern Robotics
+
+Artificial Intelligence has transformed robotics from simple automated machines to intelligent systems capable of complex decision-making and adaptation.
+
+## Machine Learning in Robotics
+
+Machine learning enables robots to learn from data and improve their performance over time. Key approaches include:
+
+### Supervised Learning
+
+Supervised learning uses labeled datasets to train models. In robotics, this is used for:
+- Object recognition
+- Gesture classification
+- Path planning
+
+### Reinforcement Learning
+
+Reinforcement learning allows robots to learn optimal behaviors through trial and error:
+
+$$
+Q(s, a) \leftarrow Q(s, a) + \alpha [r + \gamma \max_{a'} Q(s', a') - Q(s, a)]
+$$
+
+This Q-learning update rule helps robots learn the best actions in different states.
+
+## Computer Vision
+
+Modern robots use advanced computer vision techniques:
+
+```python
+import cv2
+import numpy as np
+
+class ObjectDetector:
+    def __init__(self):
+        # Load pre-trained model
+        self.net = cv2.dnn.readNet('yolo.weights', 'yolo.cfg')
+    
+    def detect_objects(self, image):
+        """Detect objects in an image using YOLO."""
+        blob = cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True)
+        self.net.setInput(blob)
+        detections = self.net.forward()
+        return self.process_detections(detections)
+    
+    def process_detections(self, detections):
+        """Process and filter detections."""
+        # Implementation details
+        pass
+```
+
+## Neural Networks for Control
+
+Deep neural networks can learn complex control policies:
+
+> [!NOTE]
+> Neural network controllers can handle non-linear dynamics that are difficult to model analytically.
+
+## Autonomous Navigation
+
+SLAM (Simultaneous Localization and Mapping) combines localization and mapping:
+
+1. **Localization**: Determining the robot's position
+2. **Mapping**: Building a map of the environment
+3. **Path Planning**: Finding optimal routes
+
+## Challenges and Solutions
+
+> [!WARNING]
+> AI models require significant computational resources. Real-time performance can be challenging on embedded systems.
+
+> [!ERROR]
+> Always validate AI models thoroughly before deployment. Incorrect predictions can lead to dangerous situations.
+
+## Future Directions
+
+The integration of AI and robotics continues to evolve:
+
+- **Edge AI**: Running models on-device for lower latency
+- **Transfer Learning**: Adapting models to new environments
+- **Multi-Agent Systems**: Coordinated robot teams
+- **Human-Robot Interaction**: Natural communication interfaces
+
+## Conclusion
+
+AI has become an essential component of modern robotics, enabling capabilities that were once science fiction. As algorithms improve and hardware becomes more powerful, we'll see even more impressive applications.
+