Velocity Planning via Time-Distance Domain Transformation
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Solution Overview
Problem
Existing methods for planning vehicle velocity along a predetermined route fail to effectively manage dynamic threats, craft performance limitations, and boundary constraints in a simple manner, making it difficult to calculate allowed velocities that satisfy complex mission requirements.
Innovation Solution
The method transforms mission demand, dynamic limitation, route, and situation data into the time-distance domain, creating a distance envelope and determining a velocity profile that avoids threats and adheres to craft limitations by using conventional optimization techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional optimization techniques are used to solve velocity planning with multiple constraints, then the computational complexity increases, but the ability to handle dynamic threats and craft performance limitations is improved
Solution Approach 1:
The velocity planning problem is segmented into distinct constraint categories (dynamic threats, craft performance limitations, boundary constraints, mission requirements). Each constraint type is processed independently to generate specific velocity restrictions, which are then integrated to form the complete velocity profile. This segmentation allows the system to handle complex multi-constraint problems through modular processing rather than attempting to solve all constraints simultaneously.
Solution Approach 2:
The patent transforms the velocity planning problem from traditional spatial coordinates into the time-distance domain. By representing velocity constraints as functions of time and distance rather than solely spatial position, the system can effectively manage dynamic threats and performance limitations. This dimensional transformation enables the use of conventional optimization techniques in a transformed space where the constraints become more manageable.
2Reliability
If velocity planning considers multiple constraints including dynamic threats and craft performance, then the safety and mission compliance improve, but the calculation complexity increases
Solution Approach 1:
The calculation process is divided into sequential steps: first determining velocity restrictions from dynamic threats, then applying craft performance limitations, followed by boundary constraints, and finally mission requirements. Each constraint type is calculated independently and then integrated. This step-by-step segmentation reduces calculation complexity by avoiding the need to solve all constraints simultaneously in a single complex optimization problem.
Solution Approach 2:
The system performs preliminary calculations to establish velocity restrictions before final velocity profile determination. By pre-calculating the maximum and minimum allowed velocities based on threat assessments and performance limitations, the system reduces the complexity of the final optimization step. This preliminary action creates a constrained velocity corridor within which the final velocity profile is determined, simplifying the overall calculation process.
3Speed
If real-time velocity planning is implemented, then the responsiveness to dynamic situations improves, but the computational power requirements increase
Solution Approach 1:
The velocity planning algorithm is segmented into independent constraint processing modules that can be executed sequentially with minimal computational overhead. Each module handles a specific constraint type (threats, performance, boundaries, mission) and produces intermediate results that are quickly integrated. This segmentation enables real-time execution on embedded systems with limited computational power while maintaining responsiveness to dynamic situations.
Solution Approach 2:
The system transforms the problem parameters from traditional spatial-based velocity planning to time-distance domain parameters. This parameter transformation allows the use of simpler optimization techniques that require less computational power while achieving the same responsiveness. By working with time and distance as primary parameters rather than spatial coordinates, the algorithm reduces computational complexity and enables real-time execution.
Data Source
AI summary
A method for planning the velocity of a craft along a predetermined route, where said method comprises the step of transforming demands and limitations of said route and said craft into a time-distance domain. Said planning subsequently takes place in said domain. The method saves computational time.