Create robot_navigation_model_v2.json
Browse files
robot_navigation_model_v2.json
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{
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"model_name": "robot_navigation_model_v2",
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"version": "2.0",
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"description": "Model navigasi outdoor untuk robot otonom berbasis GPS, LIDAR, dan vision. Termasuk perencanaan rute jarak jauh dan penghindaran rintangan real-time.",
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"architecture": {
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"type": "Multimodal Transformer",
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"modules": {
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"gps_encoder": {
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"input_dim": 3,
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"hidden_dim": 128
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},
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"lidar_encoder": {
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"type": "VoxelNet",
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"layers": [64, 128, 256, 256]
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},
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"vision_encoder": {
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"type": "MobileNetV3",
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"output_dim": 256
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},
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"decision_head": {
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"type": "TransformerDecoder",
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"num_layers": 4,
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"hidden_size": 512,
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"heads": 8
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}
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}
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},
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"inputs": {
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"gps_coordinate": "latitude, longitude, altitude",
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"lidar_data": "voxelized lidar point cloud",
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"camera_frame": "RGB 720p",
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"destination": "latitude, longitude"
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},
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"outputs": {
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"drive_action": [
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"accelerate",
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"decelerate",
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"turn_left",
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"turn_right",
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"stop"
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],
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"speed": "0.0 – 1.0",
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"steering_angle": "-1.0 – 1.0",
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"recommended_path_segment": "list of GPS waypoints",
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"confidence": "0.0 – 1.0"
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},
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"capabilities": [
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"GPS-based global navigation",
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"Local path planning menggunakan LIDAR",
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"Multi-sensor fusion (GPS + LIDAR + Vision)",
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"Dynamic obstacle avoidance",
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"Lane and road boundary detection",
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"Outdoor terrain classification"
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],
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"training_data": {
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"datasets_used": [
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"outdoor_lidar_nav_set_v3",
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"gps_driving_dataset_2024",
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"robot_delivery_path_corpus"
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],
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"total_samples": 125000,
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"terrain_types_covered": [
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"asphalt road",
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"gravel",
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"grass field",
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"urban sidewalk"
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]
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},
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"example_inference": {
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"input": {
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"gps_coordinate": [-6.2001, 106.8168, 35.2],
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"lidar_data": "2048 voxel points",
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"camera_frame": "frame_0882.png",
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"destination": [-6.2010, 106.8180]
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},
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"output": {
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"drive_action": "accelerate",
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"speed": 0.62,
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"steering_angle": 0.12,
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"recommended_path_segment": [
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[-6.2002, 106.8170],
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[-6.2005, 106.8173],
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[-6.2008, 106.8177]
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],
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"confidence": 0.91
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}
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}
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}
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