Challenges Solved
- Perception-to-Actuation: Integrated Deep Learning perception models with ROS nodes for physical actuation.
- Kinematics Synchronization: Solved real-world kinematics synchronization for a wheeled robotic arm platform.
Signal
Physical Robotics / Hardware Integration
Project Architecture
The system is built on a modular ROS architecture that decouples high-level perception from low-level motor control:
- Perception Node: Runs real-time Deep Learning models (likely CNN-based) to identify target objects and calculate 3D coordinates.
- Kinematics Node: Receives target coordinates and computes the necessary joint angles using a custom Inverse Kinematics solver.
- Actuation Node: Communicates with hardware controllers (servos/motors) to execute the movements.
Technical Depth
- Real-time Inference: Optimized models for low-latency inference to meet the control requirements of physical hardware.
- Coupled Motion: Engineered a synchronization layer that accounts for the movement of the wheeled base, allowing the arm to track targets dynamically.
- Hardware Integration: Directly interfaced with motor drivers and sensors via ROS serial and custom drivers.
Media
Links