Robot Route Conflict Detection Using Predicted Trajectories
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Solution Overview
Problem
Existing autonomy systems for robots are limited in extensibility and adaptability, failing to support the addition of new modules and rapid adaptation to new platforms, focusing on narrow mission sets and lacking comprehensive conflict detection capabilities.
Innovation Solution
A system and method for conflict detection along a robot's route using sensor data from various sources, generating predicted trajectories of nearby objects and the robot, and performing comparisons to detect conflicts within a clear region, with dynamic size adjustments for sensor uncertainty and time thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing autonomy systems focus on narrow mission sets with specialized algorithms, then the system can be optimized for specific tasks, but the system lacks extensibility and cannot support addition of new modules or adaptation to new platforms
Solution Approach 1:
The autonomy system is divided into separate functional modules including sensor data processing module, track generation module, conflict detection module, and route planning module. Each module operates independently with well-defined interfaces, allowing new modules to be added without redesigning the entire system. The sensor data processing module receives data from multiple sensors, the track generation module creates trajectories, the conflict detection module identifies potential collisions, and the route planning module generates safe paths, with each module being independently developable and testable.
Solution Approach 2:
The system employs a universal architecture that can handle multiple sensor types (radar, lidar, infrared, camera, ADS-B) and multiple robot platforms through standardized data interfaces. The conflict detection module universally applies the same clear region assessment and time to closest point of approach calculations regardless of the specific sensor source or robot type, enabling rapid adaptation to new platforms through parameterization rather than structural changes.
2Measurement precision
If the clear region size is increased to account for sensor uncertainty and future time distance, then conflict detection accuracy is improved, but the system may generate false positives and reduce operational efficiency
Solution Approach 1:
The clear region size is dynamically adjusted based on sensor uncertainty characteristics and the time horizon being evaluated. Rather than using a fixed buffer zone, the system calculates the clear region radius as a function of sensor error margins and the future time distance being projected, allowing the assessment boundary to expand or contract appropriately for each detection scenario, thereby maintaining detection accuracy while minimizing false positives.
3Reliability
If multiple sensor sources are integrated to generate accurate predicted trajectories, then conflict detection reliability is improved, but data processing complexity and computational load increase
Solution Approach 1:
Data from multiple independent sensor sources (radar, lidar, infrared, camera, ADS-B) are merged into a unified track generation process. The system correlates observations from all sensors to generate a single predicted trajectory for each nearby moving object, combining the strengths of different sensor types to improve accuracy while presenting a unified data structure to the conflict detection module, thereby managing complexity through integrated processing.
Data Source
AI summary
A method is provided for detecting conflict along a route for a robot to travel. The method includes generating a predicted trajectory of a nearby moving object, generating a trajectory of the robot, and performing a comparison of the nearby moving object and the robot on their projected trajectory and trajectory to detect a conflict that may be avoided. The comparison includes determining updated positions and velocities of the nearby moving object and the robot. The comparison also includes determining if the nearby moving object is within a clear region that includes the robot from the updated positions, and if a time to closest point of approach between nearby moving object and the robot is less than a time threshold value from the updated velocities. The conflict is then detected when the nearby moving object is within the clear region, and the time to closest point of approach is less than the time threshold value.