Automated Vehicle Obstacle Avoidance via 3D Point Cloud Grid Mapping
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Solution Overview
Problem
Automated moving vehicles in logistics and smart factories face challenges in effectively detecting and avoiding obstacles, leading to potential collisions with operators or other vehicles in shared workspaces.
Innovation Solution
An automated moving vehicle system equipped with sensors and a processor that generates and updates point clouds of the workspace, calculates distances to obstacles, and controls movement to stop or adjust paths based on threshold distances and obstacle types, utilizing inertial measurement for adaptive threshold settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the automated moving vehicle uses a simple obstacle detection method, then the device complexity is reduced, but the measurement precision of obstacle detection deteriorates
Solution Approach 1:
The patent segments the workspace into a grid map with multiple cells, where each cell can independently store obstacle information. This segmentation allows the system to process spatial information in discrete units, improving detection precision without requiring a monolithic complex system. The grid map divides the continuous workspace into manageable segments that can be processed independently.
Solution Approach 2:
The patent transitions from traditional 2D distance-based obstacle detection to a 3D point cloud-based detection system. By incorporating depth information and spatial coordinates (x, y, z) of obstacles, the system achieves higher measurement precision. The point cloud data adds a dimensional aspect to obstacle representation, enabling more accurate distance and position calculations.
2Reliability
If the automated moving vehicle uses a complex obstacle detection and avoidance system, then the safety and obstacle avoidance capability are improved, but the device complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-establishing a grid map of the workspace before the vehicle begins operation. The grid map pre-stores obstacle information and potential hazard zones, allowing the vehicle to perform predictive path planning rather than reactive avoidance. This preliminary preparation reduces the complexity of real-time decision-making during operation.
Solution Approach 2:
The system continuously updates the grid map based on real-time sensor feedback from the point cloud data. As the vehicle moves and detects new obstacles or changes in the environment, the grid map is dynamically updated to reflect current conditions. This feedback mechanism ensures the vehicle maintains accurate spatial awareness without requiring an overly complex control system.
3Reliability
If the automated moving vehicle frequently updates its moving path to avoid obstacles, then the safety is improved, but the productivity deteriorates due to frequent stops and path recalculations
Solution Approach 1:
The patent applies local quality by implementing differential path adjustment rather than global path recalculation. When an obstacle is detected, the system modifies only the local portion of the path near the obstacle while maintaining the overall mission trajectory. This approach, reflected in the grid map's cell-by-cell obstacle marking, allows the vehicle to avoid collisions without completely re-planning the entire path, thus preserving productivity.
Solution Approach 2:
The system dynamically adjusts the moving path based on real-time obstacle detection while maintaining flexibility in path selection. The grid map enables dynamic path planning where alternative routes can be quickly identified and executed. This dynamic approach allows the vehicle to adapt to changing conditions without rigid adherence to a pre-planned path, balancing safety with movement efficiency.
Data Source
AI summary
An automated moving vehicle and a control method thereof. The control method includes: obtaining a static point cloud and a current point cloud on a work space through a sensor, wherein the current point cloud includes a current scan point; calculating a shortest distance between the current scan point and the static point cloud; in response to the shortest distance being greater than or equal to a first threshold, calculating a first distance between the current scan point and the automated moving vehicle; and in response to the first distance being less than a second threshold, controlling a driving device of the automated moving vehicle to stop a movement of the automated moving vehicle.