Hybrid Vehicle Path Planning for Disturbance-Adaptive Control
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Solution Overview
Problem
Current vehicle path planning systems face challenges in balancing robustness to disturbances and verification complexity, with open-loop plans lacking feasibility in dynamic conditions and closed-loop plans requiring full stack verification for disturbance rejection.
Innovation Solution
A hybrid open-loop/closed-loop path planning approach that estimates the vehicle's current state, generates control errors, and switches between open-loop and closed-loop plans based on threshold comparisons to adapt to disturbances, simplifying verification and improving robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If open-loop path planning is used, then verification complexity is reduced, but robustness to disturbances deteriorates
Solution Approach 1:
The system dynamically switches between open-loop and closed-loop path planning modes based on detected disturbances. When disturbances are detected (via control error threshold exceeded), the system transitions to closed-loop mode for robustness; when nominal conditions prevail, it operates in open-loop mode for simplified verification. This dynamic adaptation resolves the contradiction by allowing both modes to be utilized contextually.
Solution Approach 2:
The system changes the operational parameter (planning mode) based on the control error magnitude. By monitoring whether control error exceeds a threshold, the system adjusts its planning approach between open-loop and closed-loop modes, thereby adapting robustness levels to match actual operating conditions while maintaining verification simplicity during nominal operation.
2Reliability
If closed-loop path planning is used, then robustness to disturbances is improved, but verification complexity increases
Solution Approach 1:
The path planning system is segmented into two distinct modes: open-loop planning for nominal conditions and closed-loop planning for disturbed conditions. This segmentation allows verification to focus on open-loop behavior during normal operation, reducing overall verification complexity while maintaining closed-loop robustness when needed.
Solution Approach 2:
The closed-loop control logic is extracted as a conditional fallback mechanism rather than a continuous operational mode. By taking out the closed-loop complexity and activating it only when control error exceeds thresholds, the system achieves disturbance robustness without permanently incurring the verification complexity of full closed-loop operation.
3Device complexity
If open-loop planning is used, then device complexity is reduced, but adaptability to dynamic conditions deteriorates
Solution Approach 1:
The system implements dynamic adaptability by switching between open-loop and closed-loop modes based on real-time disturbance detection. This dynamic behavior allows the simple open-loop planner to remain the primary device while gaining adaptive capabilities through conditional activation of closed-loop planning when dynamic disturbances occur.
Solution Approach 2:
The path planning system achieves multi-functionality by combining both open-loop and closed-loop planning capabilities within a single unified architecture. The open-loop planner handles nominal conditions with simplicity, while the closed-loop planner provides adaptive response to disturbances, making the overall system universally applicable to both static and dynamic scenarios.
4Adaptability or versatility
If closed-loop planning is used, then adaptability to dynamic conditions is improved, but device complexity increases
Solution Approach 1:
The planning system is segmented into conditional modes where closed-loop adaptability is separated as a distinct functional segment activated only when needed. This segmentation allows the majority of operation to use simple open-loop planning, reducing overall device complexity while preserving adaptive capabilities for dynamic conditions.
Solution Approach 2:
Closed-loop adaptability is applied locally only in the regions of operation where disturbances are detected (when control error exceeds thresholds). Rather than implementing closed-loop complexity globally, the system applies adaptive quality locally where needed, maintaining simplicity in nominal operating regions.
Data Source
AI summary
Disclosed herein are systems, methods, and computer program products for vehicle path planning. The methods comprise: estimating a current state of a vehicle based on sensor data; generating a control error representing a difference between the estimated current state of the vehicle and a desired state of the vehicle as described by a previously published trajectory; comparing the control error to a threshold value; generating a first plan for the vehicle using an open-loop path planning approach when the control error is below the threshold value or a second plan for the vehicle using a closed-loop path planning approach when the control error is above the threshold value; and causing the vehicle to execute the first or second plan.