Dynamic Motion Trajectory Optimizer
Overview
Model ini dirancang untuk mengoptimalkan jalur pergerakan humanoid secara dinamis berdasarkan kondisi lingkungan dan status internal sistem.
Tujuan utama:
- Mengurangi deviasi jalur
- Meminimalkan konsumsi energi
- Meningkatkan kelancaran gerakan
Problem Type
Regression (Multi-output)
Input Features
- current_velocity_mps
- target_velocity_mps
- terrain_friction_coeff
- obstacle_distance_cm
- center_of_mass_offset_cm
- joint_stress_level
- battery_level_percent
Outputs
- optimized_step_length_cm
- optimized_stride_frequency_hz
- predicted_energy_cost_joule
Model Architecture
- Input Normalization
- Dense(256) + ReLU
- Dense(256) + ReLU
- BatchNormalization
- Dense(128) + ReLU
- Dropout(0.3)
- Dense(64) + ReLU
- Dense(3) + Linear (Multi-output)
Loss Function
Mean Squared Error (MSE)
Optimizer
Adam (learning_rate=0.0008)
Metrics
- MAE
- RMSE
- R² Score (per output)
Training Strategy
- 85/15 Train-Validation Split
- Early Stopping (patience=15)
- Learning Rate Reduce on Plateau
Expected Performance (Simulated)
- R²: 0.92+
- MAE rendah pada energy prediction
- Stabil convergence dalam < 60 epochs
Deployment Scenario
- Real-time motion planning module
- Adaptive navigation system
- Energy-aware walking optimization
Version
v1 – Initial production-ready release