Autonomous Robot

Self-navigating robot using SLAM and path planning algorithms with real-time obstacle detection

Robotics

# features

Key Features

Interactive 3D structural breakdown of the LEO Rover.

LOADING ASSETS

SLAM

Simultaneous Localization and Mapping utilizing 2D/3D LiDAR fusion to construct high-fidelity spatial maps of unknown environments in real-time.

Autonomous Navigation

Dynamic path planning and robust obstacle avoidance algorithms ensuring seamless point-to-point traversal across complex terrain.

Computer Vision

Edge-deployed YOLOv8 inference enabling rapid object detection, semantic segmentation, and advanced environmental perception.

ROS2 Architecture

A decentralized, highly modular communication framework managing sensor data streams and autonomous state machines continuously.

Path Logging

Comprehensive telemetry recording system tracking odometry, executed paths, and obstacle metadata for post-mission kinematic analysis.

# products

Product Showcase

Deep dive into the specialized hardware components used in our mobile autonomous systems.

Active

Hardware Module

RPLidar Sensor

A high-performance 360-degree laser range scanner. Provides the robot with precise spatial awareness and real-time mapping capabilities.

Precision

92%

Range

85%

# files

Design Files & CAD

Models, schematics, and assets.

Autonomous Mobile Robot Assembly

# source

Source Code

Explore the primary logical modules.

EXPLORER

yolov8_node.py

srcyolov8_node.py

1import rclpy
2                        from rclpy.node import Node
3                        from sensor_msgs.msg import Image
4                        from cv_bridge importCvBridge
5                        import cv2
6                        from ultralytics import YOLO
7                        from vision_msgs.msg import Detection2DArray
8                        
9                        class YoloV8Node(Node):
10                            def __init__(self):
11                                super().__init__('yolov8_node')
12                                self.bridge = CvBridge()
13                                self.model = YOLO('yolov8n.pt') # Load YOLOv8 Nano
14                                
15                                # Subscribe to camera
16                                self.subscription = self.create_subscription(
17                                    Image, '/camera/image_raw', self.image_callback, 10)
18                                
19                                # Publisher for detections
20                                self.detection_pub = self.create_publisher(
21                                    Detection2DArray, '/detections', 10)
22                                
23                                self.get_logger().info("YOLOv8 Node Initialized.")
24                        
25                            def image_callback(self, msg):
26                                try:
27                                    cv_image = self.bridge.imgmsg_to_cv2(msg, "bgr8")
28                                    results = self.model(cv_image)
29                                    
30                                    for r in results:
31                                        for box in r.boxes:
32                                            conf = box.conf[0]
33                                            if conf > 0.5:
34                                                self.get_logger().info(f"Detected class {box.cls} - Conf {conf:.2f}")
35                                                
36                                except Exception as e:
37                                    self.get_logger().error(f"Failed to process image: {e}")
38                        
39                        def main(args=None):
40                            rclpy.init(args=args)
41                            node = YoloV8Node()
42                            rclpy.spin(node)
43                            node.destroy_node()
44                            rclpy.shutdown()
45                        
46                        if __name__ == '__main__':
47                            main()