Machine-Learning-on-board-Robots-Implementing-AI-Directly-on-Hardware.pptx

Machine-Learning-on-board-Robots-Implementing-AI-Directly-on-Hardware.pptx

1. Machine Learning on board Robots: Implementing AI Directly on Hardware Embedding machine learning algorithms directly on board robot hardware unlocks a new level of real-time decision-making and autonomy. This approach enables robots to process sensor data, adapt to their environment, and respond instantly without relying on external computing power. by jayesh soni

2. Benefits of On-Board Machine Learning 1 Faster Reaction Time Localized ML processing allows robots to make split-second decisions without network latency. 2 Improved Reliability Embedded AI can continue functioning even when disconnected from the cloud or a central computer.

3. Challenges of Deploying ML on Robots Resource Constraints Embedded systems have limited processing power, memory, and energy compared to server-grade hardware. Model Optimization ML models must be carefully tuned to run efficiently on low-power microcontrollers and SoCs. Integration Complexity Seamlessly combining sensors, actuators, and AI algorithms requires specialized engineering expertise.

4. Selecting Appropriate ML Algorithms Lightweight Models Algorithms like decision trees, random forests, and shallow neural networks are well-suited for embedded use. Online Learning Techniques that can continuously update models, like incremental decision trees, enable ongoing adaptation.

5. Hardware Considerations for Embedded ML 1 Microcontrollers Low-power MCUs like Arm Cortex-M can run basic ML models for sensor processing and control. 2 System-on-Chip (SoC) Integrated CPU+GPU SoCs offer more compute power for complex vision and decision-making tasks.

6. Optimizing ML Models for Embedded Systems Model Compression Techniques like quantization and pruning can reduce model size and memory footprint. Inference Acceleration Hardware acceleration and model partitioning can speed up real-time ML inference. Energy Efficiency Power-aware model design and hardware-software co-optimization minimize energy consumption. Rigorous Testing Extensive testing and validation are crucial to ensure robust, reliable on-board AI.

7. Integrating Sensors and Perception for Real-Time Decisions Sensor Fusion Combining data from multiple sensors (e.g., cameras, LiDAR, IMU) provides a comprehensive view of the environment. Real-Time Perception Embedded ML models can rapidly process sensor inputs and identify objects, obstacles, and threats. Adaptive Behavior Robots can autonomously navigate, manipulate, and interact based on their real-time understanding of the surroundings.

8. Case Studies and Best Practices Autonomous Vehicles On-board ML for perception, prediction, and control enables self-driving cars to operate safely in dynamic environments. Robotic Prosthetics Embedded AI allows advanced prosthetic limbs to interpret neural signals and provide intuitive, responsive control. Drone Navigation Lightweight ML models running on drones' microcontrollers enable robust, real-time obstacle avoidance and path planning.