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high-level architecture for a mobile robot - Coggle Diagram
high-level architecture for a mobile robot
Perception Layer
The perception layer is the layer of the robotic software architecture that processes sensor data to construct a meaningful representation of the world.
Sensor Data Acquisition: This module receives raw data from various sensors (cameras, LiDAR, ultrasonic sensors, etc.). It's the first step in the perception pipeline and is responsible for interfacing directly with the hardware.
Data Preprocessing: This module pre-processes sensor data to remove noise and make it suitable for further processing. It might include operations like filtering, normalization, or resampling.
Feature Extraction: This module extracts useful features from the pre-processed sensor data. For image data, this might include edges, corners, or other image features. For LiDAR data, this might include points, lines, or other geometric features.
Object Detection and Classification: These modules use the extracted features to detect and classify objects in the sensor data. This might be done using traditional computer vision techniques or modern machine learning methods.
Localization and Mapping (SLAM): This module uses sensor data to estimate the robot's position and orientation in the world and build a map of the environment.
Sensor Fusion: This module combines data from multiple sensors to create a comprehensive, unified representation of the environment. This can help to overcome the limitations of individual sensors and improve the overall accuracy and reliability of the perception layer.
Path Planning Layer:
The path planning layer is responsible for deciding how the robot should move to achieve its goals.
Goal Setting: This module decides what the robot's current goal should be. This could be a fixed point, a moving target, or a higher-level goal like "find the exit" or "follow the human."
Costmap Generation: This module generates a costmap of the environment, which represents the cost of the robot moving to each point in the environment. This could be based on the distance to obstacles, the steepness of the terrain, or other factors.
Path Generation: This module generates a path from the robot's current position to its goal. This could be done using algorithms like A*, Dijkstra, or RRT.
Path Smoothing: This module smooths the generated path to make it more feasible for the robot to follow. This might involve reducing sharp turns, maintaining a safe distance from obstacles, or optimizing for other criteria.
Execution Layer
The execution layer is responsible for carrying out the commands generated by the higher-level layers.
Motion Planning: This module plans the sequence of movements the robot's actuators need to make to follow the desired path. This might involve inverse kinematics, trajectory optimization, or other techniques.
Actuator Control: This module sends commands to the robot's actuators (e.g., motors) to execute the planned movements. This might involve PID control, feed-forward control, or other control methods.
Monitoring and Feedback: This module monitors the execution of the movements and provides feedback to the higher-level layers. This might involve comparing the robot's actual position to its desired position, detecting any errors or unexpected events, and adjusting the plan as needed.