Robot Conflict Avoidance Using Perception-Uncertainty Routing
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Solution Overview
Problem
Existing autonomy systems for robots are limited in extensibility as they are typically configured to address only one aspect of robot operation, such as automatic control, task allocation, or data processing, which restricts their efficiency and adaptability in various mission scenarios.
Innovation Solution
A system and method that enables conflict detection and avoidance during a robot's mission by monitoring its state and environment, generating a local route based on perceived objects, and transitioning between global and local routes based on uncertainty thresholds, using a mission management system with situational awareness and mission execution subsystems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing autonomy systems are configured to address only one aspect of robot operation, then the system design can be focused and simplified, but the extensibility and adaptability of the system is limited
Solution Approach 1:
The autonomy system is designed to perform multiple functions including automatic control, task allocation, real-time data processing, route planning, and conflict detection/avoidance within a single integrated architecture. This multi-functional design allows the system to address various aspects of robot operation simultaneously while maintaining a unified codebase and shared data structures, thereby improving extensibility without proportionally increasing complexity.
2Reliability
If the robot transitions to a local route when perception uncertainty exceeds a threshold, then conflict avoidance capability is improved, but the deviation from the global route reduces mission efficiency
Solution Approach 1:
The system dynamically adjusts the robot's route based on real-time perception uncertainty measurements. When uncertainty exceeds a threshold indicating potential conflicts, the robot transitions to a locally generated safe route. Once uncertainty decreases below the threshold, the robot returns to the global route. This dynamic switching mechanism ensures conflict avoidance while minimizing deviation from the optimal mission path, thereby balancing safety with mission efficiency.
Solution Approach 2:
The system changes the operational parameter of route selection based on the uncertainty threshold parameter. By monitoring perception uncertainty and comparing it against a predefined threshold, the system switches between two operational modes: following the global route when uncertainty is low, and following a local safe route when uncertainty is high. This parameter-based control enables adaptive behavior that maintains mission efficiency while ensuring safety.
3Measurement precision
If the region size and shape are dynamically updated based on robot state, then the accuracy of conflict detection is improved, but the computational load increases
Solution Approach 1:
The system pre-defines the relationship between robot states and corresponding region parameters (size and shape) in the autonomy algorithms. When the robot enters a particular state, the system immediately applies the pre-configured region parameters without requiring complex real-time calculations. This preliminary configuration approach maintains high conflict detection accuracy while minimizing computational energy consumption during operation.
Data Source
AI summary
A method is provided for detecting and avoiding conflict during a mission of a robot that includes a global route of travel. The method includes monitoring a state of the robot and a state of an environment of the robot as the robot travels the global route. The method includes generating a local route of travel through a region of the environment that includes the robot, the region having a size and shape that are set based on a type of the robot and the state of the robot when the local route is generated. A measure of uncertainty in the perception of objects in the region is monitored based on the state of the environment. And the robot is caused to maintain the global route or transition to the local route based on a comparison of the measure of uncertainty and an uncertainty threshold.